diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index c6e5072b781ffdc610924a5a3cbe176e0285324e..94b3e38662b93415c95454345559a5fd0dca160b 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -107,8 +107,9 @@ ubuntu:
- $ENABLE_NIGHTLY_BUILDS
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/ubuntu
before_script:
- # - apt-get -y remove python3-sympy
+ - apt-get -y remove python3-sympy
- ln -s /usr/include/locale.h /usr/include/xlocale.h
+ - pip3 install `grep -Eo 'sympy[>=]+[0-9\.]+' setup.py | sed 's/>/=/g'`
# - pip3 install `grep -Eo 'sympy[>=]+[0-9\.]+' setup.py | sed 's/>/=/g'`
script:
- export NUM_CORES=$(nproc --all)
diff --git a/conftest.py b/conftest.py
index 131167994d7cc31ae919ee7fb51bb5897fcdc995..3c140f19efdea93fcd5f6b94c7f706b7a1c77ef2 100644
--- a/conftest.py
+++ b/conftest.py
@@ -82,10 +82,6 @@ try:
except ImportError:
collect_ignore += [os.path.join(SCRIPT_FOLDER, "pystencils/datahandling/vtk.py")]
-# TODO: Remove if Ubuntu 18.04 is no longer supported
-if pytest_version < 50403:
- collect_ignore += [os.path.join(SCRIPT_FOLDER, "pystencils_tests/test_jupyter_extensions.ipynb")]
-
collect_ignore += [os.path.join(SCRIPT_FOLDER, 'setup.py')]
for root, sub_dirs, files in os.walk('.'):
diff --git a/doc/notebooks/01_tutorial_getting_started.ipynb b/doc/notebooks/01_tutorial_getting_started.ipynb
index 564f7e0174744ecdd7be2574a313aae2534c4d74..5cb9acd727844ce4d4213780ef455c8dd14d0b05 100644
--- a/doc/notebooks/01_tutorial_getting_started.ipynb
+++ b/doc/notebooks/01_tutorial_getting_started.ipynb
@@ -6,7 +6,11 @@
"metadata": {},
"outputs": [],
"source": [
- "from pystencils.session import *"
+ "import pystencils as ps\n",
+ "from pystencils import plot as plt\n",
+ "\n",
+ "import numpy as np\n",
+ "import sympy as sp"
]
},
{
@@ -66,7 +70,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "7.96 ms ± 797 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+ "4.65 ms ± 22.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
@@ -89,9 +93,12 @@
"outputs": [
{
"data": {
- "text/plain": " src_E src_N src_S src_W\ndst_C := ───── + ───── + ───── + ─────\n 4 4 4 4 ",
- "image/png": "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\n",
- "text/latex": "$\\displaystyle {{dst}_{(0,0)}} \\leftarrow \\frac{{{src}_{(1,0)}}}{4} + \\frac{{{src}_{(0,1)}}}{4} + \\frac{{{src}_{(0,-1)}}}{4} + \\frac{{{src}_{(-1,0)}}}{4}$"
+ "text/latex": [
+ "$\\displaystyle {dst}_{(0,0)} \\leftarrow \\frac{{src}_{(1,0)}}{4} + \\frac{{src}_{(0,1)}}{4} + \\frac{{src}_{(0,-1)}}{4} + \\frac{{src}_{(-1,0)}}{4}$"
+ ],
+ "text/plain": [
+ "Assignment(dst_C, src_E/4 + src_N/4 + src_S/4 + src_W/4)"
+ ]
},
"execution_count": 5,
"metadata": {},
@@ -113,8 +120,10 @@
"outputs": [
{
"data": {
- "text/plain": "<Figure size 216x216 with 1 Axes>",
- "image/png": "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\n"
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 216x216 with 1 Axes>"
+ ]
},
"metadata": {
"needs_background": "light"
@@ -172,7 +181,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "1.76 ms ± 74 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+ "951 µs ± 15 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
]
}
],
@@ -224,7 +233,9 @@
"outputs": [
{
"data": {
- "text/plain": "sympy.core.symbol.Symbol"
+ "text/plain": [
+ "sympy.core.symbol.Symbol"
+ ]
},
"execution_count": 11,
"metadata": {},
@@ -251,9 +262,14 @@
"outputs": [
{
"data": {
- "text/plain": " 2 2\nx â‹…(x + y + 5) + x ",
"image/png": "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\n",
- "text/latex": "$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2}$"
+ "text/latex": [
+ "$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2}$"
+ ],
+ "text/plain": [
+ " 2 2\n",
+ "x â‹…(x + y + 5) + x "
+ ]
},
"execution_count": 12,
"metadata": {},
@@ -279,9 +295,14 @@
"outputs": [
{
"data": {
- "text/plain": " 3 2 2\nx + x â‹…y + 6â‹…x ",
"image/png": "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\n",
- "text/latex": "$\\displaystyle x^{3} + x^{2} y + 6 x^{2}$"
+ "text/latex": [
+ "$\\displaystyle x^{3} + x^{2} y + 6 x^{2}$"
+ ],
+ "text/plain": [
+ " 3 2 2\n",
+ "x + x â‹…y + 6â‹…x "
+ ]
},
"execution_count": 13,
"metadata": {},
@@ -299,9 +320,14 @@
"outputs": [
{
"data": {
- "text/plain": " 2 \nx â‹…(x + y + 6)",
"image/png": "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\n",
- "text/latex": "$\\displaystyle x^{2} \\left(x + y + 6\\right)$"
+ "text/latex": [
+ "$\\displaystyle x^{2} \\left(x + y + 6\\right)$"
+ ],
+ "text/plain": [
+ " 2 \n",
+ "x â‹…(x + y + 6)"
+ ]
},
"execution_count": 14,
"metadata": {},
@@ -319,9 +345,14 @@
"outputs": [
{
"data": {
- "text/plain": " 2 2\nx â‹…(x + cos(x) + 5) + x ",
"image/png": "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\n",
- "text/latex": "$\\displaystyle x^{2} \\left(x + \\cos{\\left(x \\right)} + 5\\right) + x^{2}$"
+ "text/latex": [
+ "$\\displaystyle x^{2} \\left(x + \\cos{\\left(x \\right)} + 5\\right) + x^{2}$"
+ ],
+ "text/plain": [
+ " 2 2\n",
+ "x â‹…(x + cos(x) + 5) + x "
+ ]
},
"execution_count": 15,
"metadata": {},
@@ -346,9 +377,14 @@
"outputs": [
{
"data": {
- "text/plain": " 2 2 \nx â‹…(x + y + 5) + x = 1",
"image/png": "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\n",
- "text/latex": "$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2} = 1$"
+ "text/latex": [
+ "$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2} = 1$"
+ ],
+ "text/plain": [
+ " 2 2 \n",
+ "x â‹…(x + y + 5) + x = 1"
+ ]
},
"execution_count": 16,
"metadata": {},
@@ -367,9 +403,16 @@
"outputs": [
{
"data": {
- "text/plain": "⎡ 1 ⎤\n⎢-x - 6 + ──⎥\n⎢ 2⎥\n⎣ x ⎦",
"image/png": "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\n",
- "text/latex": "$\\displaystyle \\left[ - x - 6 + \\frac{1}{x^{2}}\\right]$"
+ "text/latex": [
+ "$\\displaystyle \\left[ - x - 6 + \\frac{1}{x^{2}}\\right]$"
+ ],
+ "text/plain": [
+ "⎡ 1 ⎤\n",
+ "⎢-x - 6 + ──⎥\n",
+ "⎢ 2⎥\n",
+ "⎣ x ⎦"
+ ]
},
"execution_count": 17,
"metadata": {},
@@ -394,9 +437,14 @@
"outputs": [
{
"data": {
- "text/plain": " 2 2\nx â‹…(x + y + 5) + x ",
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- "text/latex": "$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2}$"
+ "text/latex": [
+ "$\\displaystyle x^{2} \\left(x + y + 5\\right) + x^{2}$"
+ ],
+ "text/plain": [
+ " 2 2\n",
+ "x â‹…(x + y + 5) + x "
+ ]
},
"execution_count": 18,
"metadata": {},
@@ -414,8 +462,161 @@
"outputs": [
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+ "<g id=\"node9\" class=\"node\">\n",
+ "<title>Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"284\" cy=\"-90\" rx=\"28.7\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"284\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">Add</text>\n",
+ "</g>\n",
+ "<!-- Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,)->Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1) -->\n",
+ "<g id=\"edge6\" class=\"edge\">\n",
+ "<title>Mul(Pow(Symbol('x'), Integer(2)), Add(Integer(5), Symbol('x'), Symbol('y')))_(1,)->Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M193.41,-148.65C210.74,-137.62 236.33,-121.33 255.9,-108.88\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"257.85,-111.79 264.41,-103.47 254.1,-105.88 257.85,-111.79\"/>\n",
+ "</g>\n",
+ "<!-- Symbol('x')_(1, 0, 0) -->\n",
+ "<g id=\"node7\" class=\"node\">\n",
+ "<title>Symbol('x')_(1, 0, 0)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"102\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"102\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
+ "</g>\n",
+ "<!-- Pow(Symbol('x'), Integer(2))_(1, 0)->Symbol('x')_(1, 0, 0) -->\n",
+ "<g id=\"edge7\" class=\"edge\">\n",
+ "<title>Pow(Symbol('x'), Integer(2))_(1, 0)->Symbol('x')_(1, 0, 0)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M158.73,-74.15C148.67,-64.37 135.33,-51.4 124.11,-40.5\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"126.36,-37.81 116.75,-33.35 121.49,-42.83 126.36,-37.81\"/>\n",
+ "</g>\n",
+ "<!-- Integer(2)_(1, 0, 1) -->\n",
+ "<g id=\"node8\" class=\"node\">\n",
+ "<title>Integer(2)_(1, 0, 1)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"174\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"174\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">2</text>\n",
+ "</g>\n",
+ "<!-- Pow(Symbol('x'), Integer(2))_(1, 0)->Integer(2)_(1, 0, 1) -->\n",
+ "<g id=\"edge8\" class=\"edge\">\n",
+ "<title>Pow(Symbol('x'), Integer(2))_(1, 0)->Integer(2)_(1, 0, 1)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M174,-71.7C174,-63.98 174,-54.71 174,-46.11\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"177.5,-46.1 174,-36.1 170.5,-46.1 177.5,-46.1\"/>\n",
+ "</g>\n",
+ "<!-- Integer(5)_(1, 1, 0) -->\n",
+ "<g id=\"node10\" class=\"node\">\n",
+ "<title>Integer(5)_(1, 1, 0)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"246\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"246\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">5</text>\n",
+ "</g>\n",
+ "<!-- Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Integer(5)_(1, 1, 0) -->\n",
+ "<g id=\"edge9\" class=\"edge\">\n",
+ "<title>Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Integer(5)_(1, 1, 0)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M275.19,-72.76C270.58,-64.28 264.84,-53.71 259.68,-44.2\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"262.61,-42.27 254.77,-35.15 256.46,-45.61 262.61,-42.27\"/>\n",
+ "</g>\n",
+ "<!-- Symbol('x')_(1, 1, 1) -->\n",
+ "<g id=\"node11\" class=\"node\">\n",
+ "<title>Symbol('x')_(1, 1, 1)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"318\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"318\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">x</text>\n",
+ "</g>\n",
+ "<!-- Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Symbol('x')_(1, 1, 1) -->\n",
+ "<g id=\"edge10\" class=\"edge\">\n",
+ "<title>Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Symbol('x')_(1, 1, 1)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M292.06,-72.41C296.08,-64.13 301.04,-53.92 305.54,-44.66\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"308.78,-45.99 310,-35.47 302.48,-42.94 308.78,-45.99\"/>\n",
+ "</g>\n",
+ "<!-- Symbol('y')_(1, 1, 2) -->\n",
+ "<g id=\"node12\" class=\"node\">\n",
+ "<title>Symbol('y')_(1, 1, 2)</title>\n",
+ "<ellipse fill=\"none\" stroke=\"black\" cx=\"390\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
+ "<text text-anchor=\"middle\" x=\"390\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">y</text>\n",
+ "</g>\n",
+ "<!-- Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Symbol('y')_(1, 1, 2) -->\n",
+ "<g id=\"edge11\" class=\"edge\">\n",
+ "<title>Add(Integer(5), Symbol('x'), Symbol('y'))_(1, 1)->Symbol('y')_(1, 1, 2)</title>\n",
+ "<path fill=\"none\" stroke=\"black\" d=\"M302.95,-76.49C319.71,-65.42 344.35,-49.15 363.14,-36.74\"/>\n",
+ "<polygon fill=\"black\" stroke=\"black\" points=\"365.15,-39.6 371.57,-31.17 361.29,-33.76 365.15,-39.6\"/>\n",
+ "</g>\n",
+ "</g>\n",
+ "</svg>\n"
+ ],
+ "text/plain": [
+ "<graphviz.sources.Source at 0x7fc1288673a0>"
+ ]
},
"execution_count": 19,
"metadata": {},
@@ -441,7 +642,9 @@
"outputs": [
{
"data": {
- "text/plain": "sympy.core.add.Add"
+ "text/plain": [
+ "sympy.core.add.Add"
+ ]
},
"execution_count": 20,
"metadata": {},
@@ -459,9 +662,14 @@
"outputs": [
{
"data": {
- "text/plain": "⎛ 2 2 ⎞\nâŽx , x â‹…(x + y + 5)⎠",
"image/png": "iVBORw0KGgoAAAANSUhEUgAAALAAAAAaCAYAAAAXMNbWAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAGkElEQVR4Ae2b7XHVOhCGDxkKOIQKbm4HAToIHQRuBYQOwvAr+ZcJHRAq4KMDoAJIOggd3HA6CO+jSELWkWU7tmVncnZGyJJWu6/Xq9VKJzy4vr5eDEnHx8dLyXtrZe7Y+pX6V0Pq6SJrjpi64N/w1lvgYTykj32gvl+qv8VjLdunmvva8er5vZ7PVf51fRPUc8Q0gRnurkr8KPQr9yZb7oFaDPuqnqi+rfMi5kDz93iwdKp6R327rmOCeo6YJjDDnVZJEPoav4F3YA2arV+1j54xc8s283+25C3F1huT7OLSoVKY742eNrYVzy8Z5LPqw9Aw3oEZVGG770VScKYS5rs4DynJRS/BPSb3xaT5GG3KHaTH2087VbZbs5v6liphQGCHrjhmCrV4ztT/mvlu3OTAVthT1c/dwBC15AHepCVDyBtCRldM4gf/Y9XvhtB/D2V8l+1wOBfAnPM9cbbQ+DeVXRVSPZw0RwTZDyovYHIR+I2emybC35oEhBVG/ktOHUbk1jKGZuyKSfwY+61q7LOh21ngStP4/gQz7PlFZc0nZGMCRCW6qp0i/HTffpuFu4Xg5mGw6CvhOO8b1UambS9Uk8dMQhZDV0wswN5pVZcXFk4OwGypgwaULhhC3gHwXEiGiZah3JpnbI3Na89hkrVSIZq/VDnbUsPlKIMcvCQP5wUI1x5sC8gngrESJ6EemF5qbmlHIkpR5kLF8FhbY/Om98dXzaIgArPiOWStBrIYd74AoPYk+bWryjON99AZk/CS+062Y4xnitlLxuYmumaQXloek0I8UyPrvHZFkGZA8L9SIdL+pwL9EA+5zUL1I+qxqQAm0p/sfXgXDGPbA/kBHn40Olfb7x527IPqttv5YJClE98hqD1WwW9O1OcOdWpWCJtje4+9MnrTYC43GcstHlSatncukd9RxPtDhVPgntqkBswnbylNY2N6qhdipedobAw53akxDpx8Iy78429CVGNXKU34xyfrP/gLhcXFzp8ibI6T58j56w4OvK2yquOWIlZPaAx4MYRbIcwPx9UclwphwvDOUGsvVAjDmt66DuHhrEFwgYhgMfbGHcXMHPgf4Xqu4v1Lz6QIRNm6wzG4mxzYydsmB85+KI3/tEr1aIj7O06WRojq4luS9JfAlF3YfTHIbnzAVBRC70LjqTND7kQf/lhEtD1BTkDoivv88Ah4vOzEA07MDs5tS3zOoI1P5sgvThwYRzRGS82QgouoP2WciGXc5hww9cWg+SkHXaif3Y0P2+mHE/G7gIKj4gBuh0Qm0Zm+2pxePIPikS70kspsq/Y/WtAfEJhiagoc8Dt/vSKFaE0C4oxjDmxMVJ9JplsLGZhxREys8pSB195gRAxrulp0sCP6HdLy893c/WkLEYOxcI5I2dA4oOwWB0cUw+8jLB0JcjJXOHBtyLbO+dV+IOSwSjFEGPY5OKwY7ELI7sLveJmnMgomp8PWvGMyFyuIIYLUqgnm8PswaZL8V3r5u5jUn9GyoOp2A5w7xq+uClUiMIk/KyVFKKJc8dGoQyb1MeYODuFQ9tnK+q26clecnfR3cBRMf8X7J6IDV4YpKoUhpbupr/Lx7TcCL9t5aeLHrMphTe1DC6Lu7GTOWA1AWaQE0tVDPfChiGqUOJKySsilMMBC4/xWTfQDFM6HY/t0Ap42pDkox9B1enNiRsGUUPhRfZ8T/XSVwlCjPtvNNRX3vdwM/a/iImBdxMsK6zPINwaHinNiIidB8B/1xb7mVOFryXzcMajGyW/eh/9SdHR09Ftlj+eSRTr3VZYldXbRJWyXKrtd5vTltTY57CvHzZe8U5Vz1+5aD40np1+6dlQuczyM8T4qBzyTA0OfVMiTStOzzEosjSWljyjWFA1S8/r0EZnqolNWro12Pi1Te6kJ3OOfZCfmB2+NJy82Ocrukf1Nwb7Trvjw2QUpBESI/66CgCJkgbDFzZaEkUMIKVPqvnIU3NLVZ6vnCs5t1+DjF1PeoXOax2SoJ54bIS3+lR7yWuzcFDC4xv0iPrPITQRWgzyYPyrGAKXoQPo63XWWAhbp4bAROkU0PKsmAYjzxaEKmDlEFQtKPS0B3ibnRQXv49/pAXkEpBdlu+Gv5+sunQ3fffxHNiE68EfUd2HB3blPJLtyM0FUrdygxC+icfNHQeF38A4MswaIwOSl3sNjIZv2xgJTWEA+SRBhR6mc1UwK4QBpkFzpUrW5NnP9m3pjgRlYgKC6dnf8B+NFxn0ejbbTAAAAAElFTkSuQmCC\n",
- "text/latex": "$\\displaystyle \\left( x^{2}, \\ x^{2} \\left(x + y + 5\\right)\\right)$"
+ "text/latex": [
+ "$\\displaystyle \\left( x^{2}, \\ x^{2} \\left(x + y + 5\\right)\\right)$"
+ ],
+ "text/plain": [
+ "⎛ 2 2 ⎞\n",
+ "âŽx , x â‹…(x + y + 5)⎠"
+ ]
},
"execution_count": 21,
"metadata": {},
@@ -511,9 +719,13 @@
"outputs": [
{
"data": {
- "text/plain": "f_E__1",
"image/png": "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\n",
- "text/latex": "$\\displaystyle {{f}_{(1,0)}^{1}}$"
+ "text/latex": [
+ "$\\displaystyle {f}_{(1,0)}^{1}$"
+ ],
+ "text/plain": [
+ "f_E__1"
+ ]
},
"execution_count": 23,
"metadata": {},
@@ -539,7 +751,9 @@
"outputs": [
{
"data": {
- "text/plain": "True"
+ "text/plain": [
+ "True"
+ ]
},
"execution_count": 24,
"metadata": {},
@@ -575,9 +789,14 @@
"outputs": [
{
"data": {
- "text/plain": " \n(img_E__2â‹…wâ‚‚ - img_NE__2â‹…wâ‚ - img_NW__2â‹…wâ‚ + img_SE__2â‹…wâ‚ - img_SW__2â‹…wâ‚ - img\n\n 2\n_W__2â‹…wâ‚‚) ",
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAArCAYAAADykI6AAAAACXBIWXMAAA7EAAAOxAGVKw4bAAASZ0lEQVR4Ae2d4bXctBaFb7JSQIAKIB0QqIDQATwquKGDZOUX/GNBB0kqeIQOAhVA0gGvA8LtIG9/HsuxPfKMJFsz9tyttTSS5aOjo32OZZ2RZd95//79lYMRMAJGwAgYASNgBIyAETACRqAUgR9//PG+6j5r63/Wptcqv7lXytT1jIARMAJGwAgYASNgBIyAETACLQI/y7n4PqCh/HPl3yg+uBsKnRoBI2AEjIARMAJGwAgYASNgBAoReCwn41Gv7s/Kf6ayz+1w9FBx1ggYASNgBIyAETACRsAIGIEiBFjd+CtW8473cMRgcZkRMAJGwAgYASNgBIyAETACpQhoZYMVjm+UPji4h0MEPHv1WulvpY25nhEwAkbACBgBI2AEjIARMALbQEDzfjZ//6H4lfI3JVKr3ueq943iQ+pPPlIlQpyNj5Xa2QApByNgBIyAETACRsAIGAEjcOEItE7Gtbr5Rnmcj6ygOryhitWNhy2vq+gjVTr5WETfK228kqxWTGwEjIARMAJGwAgYASNgBIzAphFo/YGvlX6b2hHR4mw8Vdq8rao93nc4dIIlEJZRPlW+aBklVSjTGQEjYASMgBEwAkbACBgBI7BOBOQLvJZkz5UefeJJNDgbPCH1tNcbHI+nsUeqXurECzsbPaicNQJGwAgYASNgBIyAETACtw8BVjdeyi9IebSKb27wWlzSEHlV7s3gkSoVsLnjleJHdjiEgoMRMAJGwAgYASNgBIyAEbjFCMgnCPu6kx+tGsM1fksVGzx+2YKzIRnxtJ61HWIJh9B8Pn2X9e/WEbCOt67B4/Jbx8cxuq0Uto3bqvnD/bZdHMbHZ9MRsC2lYyVK/IO/hRkf8ftfVs2WuHM4xIC9G0zcfyphdIY6k59PP4MsbrIOAtZxHVzXxNU6XpM21iWLbWNd+liLNLaLtWhi+3LYlhJ1iJOh+LvIm/0YidUGZP09HKwW/C6GW9koPvn59EEPfbBlBKzjLWsvTXbrOA2n20hl27iNWj/eZ9vFcYxMkYaAbSkNp0DFlgveYlsU+g5H2L9RxOgMlfCyop9PP4MsbrIOAtZxHVzXxNU6XpM21iWLbWNd+liLNLaLtWhi+3LYlvJ0yArHfS1M4C9kh+aRKlXmcSoCzI4G0T8R0QOlKOssQW2/GDWMLCz5vB2V+7AAAeu4ALSNVbGON6awE4m7Brugq5LDY/yJdJ7azBpsw3aRqq310q3BjkDHtpRnI8KLOTb7N75WPPqK3DH3sMLxBSdaRmOa2DGT+9U8eiW5B59PjwnssmwErONsyDZXwTrenMpOIvCq7IIee4w/id5TGlmVbdguUlS2SppV2REI2ZaS7YSFicZnSK7REjavxRXQPJfFzvPNfVlcMrPRndd1fav8apygFl8nCyBgHS8A4spZWMfLKEg48v5zxvLx6sAyDZyBi23jDKBvoEnbxQaUtBERbUvpihJWPOHEZvs76bV2lOEtVdykkh6nym2gJn1rJHw+neWdq/aYtOiVXTVlNe8yBFqdWsdl8G2ilnW8qJp4XTjxIoJt4yLUuHgnbBeLQ3prGdqWslXfzK+F2+eKWVsYgsPBDWpTk/TWSFjZYDIa9qCwTNf/nHo2kq6wHgSs4/XoopYk1nEtZLfP17axfR3W6IHtogaqt5OnbalI78FX4OmiIoeDVv8+1LQUg1PC67AeKL7RcbdkrzwrJEz6v1S8VvxYkcn/P4qsPrwWDR8UhOY7RQJ5lmWiKysqZxc8/OBBYIPKI5WHdvlkOjKRdkHnaffkQe0GfD5R43wcJch5pTx9/V5pI5tS+sZHVB4qf6N0FUGyhD5YxxGN9PCxji/0Oo6o/WiR7aLK+A7uqxrjjxrCiOAS7IIu9frh+8JIx6c67OnA957l7j2oz2NMvhEHh4N5fla4J0PGSyG82yWTv89Ey2oCzgV7ProJtfJft+deK/9S8U+OlV4phe5fpUys2eEeypl0w+cjxUEQDZNxnItmT4lSZMQweA1u067K9urp3DlDwId+gUEfH75xwmQ+BLCgT18oRh2uQHjiNPTBOo4DH/Cxji/3Oo5r/nCp7WLh8R24VzjGH7aC/bOXYBf0KvTD94V9HZ+qJOjA956F7j0ozmNMvvkKsxtFKvbntEmM7qZUEnP+of+z5fit0uDhXOkcgxCOBoFJNO/o/aU52v0EL4jy/uSa8j2BRcMFxaaUr3bVG6OgPWhDO+HUKlLJ3MeHFZ2x89bH6KrFgaWoBkcdgw2rPcRXbdzDpmZn1Wa/D9bxCOwRPtk6hp14oOfHioNVuVFT1Q7VrnW8MLojTG0XH/C9mPH9Q5fSc5dgF/R21A/fF9JNYDHKkQ48xnxA1mPMh3l5tl3IrubOO1ltywr3RI3SCDe7JPrb/77Ff0TxU4/qLwl+g/Aqw+EYP9LEJIfQ/8efY1YvYs9/sTrwGzwhIigfeGS/93fHofrvJD6t7GDTd7YQCNyC44aj0eGmPHtTmJSyhD0ZRAffPxRJUwNv84rhPtkHMbaOd6tzAbfBNSA8sc+DOm5pcDwJyfqyjneArfh38rqpbBeMEcGe+vA047na7saT3sm3KmfSOA6TfRChr/0xWmnHk5hWtosl7wn0dLIfOmfbSLOFuVSTOtiQLU32wXZUbB6TmKbYhVotmnf2pE2ex4Q6OBzv2oPJyhL+BhqlrD5A1zkP4ZzKmpufjscTazwvbnQND+VDYNLW8aFQNGHi9t9A1KbwxqkJE/TBaZUjF21Ezw+ICw7E94lif9VmwEXnJvERYZA9TFZD3f6eGf71ZmUjYMcjZZQdfAtA227z2FlgWpq2vK6UWscREA/hI/KjOlZ99I+Ngm9yaNu1jpMRW5ZQ+Fe99mfYRcyhuBI/7IvX4k6OV2OERDs5foVzqnO28R15JUfVMX6MScqxZJq0jYBbK/fgninemxgvwOBQP8K5tj/QhvsXVQmbv/fvulH/V9hdtC0FW1G6yvkFGm5lqzaPLLEiyVTNLiQPc8zseWdJP0IdHqnKCexF+E1ChhWNfl02g48n1ZxncB0MRKpPGYMw/9Jd6Zg84YtdsseHgWvAo6WjLhvZB84I/ChXTH50RbThkSYUzIS/Czpmw3sja1cYz8QG2D3ZxQvHqo8Vkwf2p6whXLKOJ+3iBDpeg26DDJvQcRA2J5UeJ3U8xedQHdtFh9pZxndalw4WGeO7nmRkFrCN0ntChpQnIfWYMRNm21ID4OrsCKliY0yJug/peIqf6syde5aOMXPnnf0/zae6NyjH4Wj+3VLaLMUPzvYOAFKHTJSft8UYTj/EHAvq8JjVeO8FHW08SfHlPKsdXVBZt1LRtgvvMY+r9hwb1jtnRHlk5AZF28SjQXWg/0cpm+L5Z5C3ao3bwxMcOCIRxvSlk709Hyv7Trz6Mr/Q8U2PH/j0l8t6p+plJQN4XaqOU+yimo7raS2P81Z0nNerHbX6lqLjAevEOraL+B9HjBfVxncU1drr7DF+oPTEg4VsIzb+x8oG94REEU9C1upg9feFEjASdVzCelAnsZ1j40zMbmJlq7SlNdoRSmrlGowxA+UlHiTqeMBNdZaYe8ZsIFY2sAu1PXfe2Z+zDvo1dXBXJ961J7l5HAp04EpC/q5IvvNu2mPqjx+FahwJ6lC3F6ANZbwuNjxa9Ss0Lb9Azp4OQnNul+1+cQCCA9QUqi6ODE7DeOLfVYpk4BPkoX3yj5Q2fYa+V3YIJ1YtuvOqg6P0sWKfT9R5Ek0TVIeBnWXHRR6j2XFN/m3kpK+K5C9Gx+rPUbug3+ozeu90GEFuto4jPE9ZtAkdlwCSouMx35Q6totmLOCaOPX4jrqWGuPHqj96vJBtbH28ACePGUet5TCBbanBZ412hGB7Y8xhbcbPpug4UnOJuefsMUayJ887RdvoUX15F+nPwaJ7qszjSDeiOrZBmQkbj1PhkV0pDU4ChwgQ+0eem1SfDloCILN5+YnSzmHQMbKwPMQSE2/F+kSRTsEbGceBySGrAcVB9ZEdOccOCu2hhH55MyFVWXTzung9VXxOFA31/1H8VPFlr2zwjQ6d64JokAVszvJ9DrV/kTruAE7LVNVxmgj1qKzjYmxtF7t9SH0AGTdrju+0NXuM7wtcKT9pG7reZt0TKsmbxdZjRhZcc4kv1pZWakfo6yxjjPBYZO45d4xp5ciZdzLuE5jjZoV7LTWTajp/MEiw2BtOrlTORbLnsKiclYa90NJTZy+Mz+mYfRh7tCpH3uwO7zXYW5EYncPRYXWiH3CCcIiiDgeEkivmAEVxgz6Etj/cnOAPn0YfSvsOTyCvlqq9qKwq37KOc/CqpuMcIWrSWsdF6NouRrDJjqqN7zQl/kuN8SPJFz88aBvqR9E9YXEpZzBUH277fWEGellVL9qW1mRHaEXynHOMCRP3sYFkzz3Vj6Ixpu1/7ryzmZtK6Ox9x8HhoOJgH8UYgdrH6jirCewLuVa+cSTaMsqvI+3T6ewlnQifqSKcjbFBIFcAe6pednmrdFZFUDz9JWBAzUcSm6ML+Gn7tiYdx1CtouNYQ5dYthEdl0C/FbtATuKqQoFdIH/tMX4pjLZiG0v1d1E+BbaxFbsowcm2VIKa6hTYES2t0ZZOMvcUXvS9ZN75pepNPXUEppMhOBysIvBWJ97wcq6bFRNR9jc0AVmUYf8Gk/C3u9LBL+eXkHWKB/zHqwu1HBzwpz3SLqjfMa+1O7/BzLl0nANVFR1Ll1zc6BMb57WlLGHyooLov8Q6t9WwiI6FC443GB0L7AEbX6fH6pSc34RdCIu91eCSzlaok2sXiMCYODU+Z4lY2Z4Wtw3Je1vGC/SYaxtRu6is487eKrdjW+qQzs7k2hENnNOWpsY2ZBrf0xa3C7VROu/k3vxWMTsEh+NX1XyuyGRo8nGhbO55FWifgOPD3g1Ax9mYuoGiEGhmBfEPnhoD/BjE8TE0Y0OY1T6VJcNHs5lsg8FZdJwJTS0dYzcXtWI1gesiOtY1wbXXPF440c6pi20X8xDPtQtaW2SMh1Fle1rcNiTvbRkvUE+ubUTtorKOkbMJlduxLQWg89NcO6KFs9kS17giTgc6H881x8c17KJ03omfUPRneONw0GlFJvYH9yfofLXQtj/lXOy1K3o2OKOErKA6OCnPlPYnfz+pDBAbJetc43gpxRj7gfa6Nzf1Tzh/HAHhiX7PpePjAu4orONUpCJ0p9JxpOmjRZItdu0frdcS2C5SkYrQ5doFLFRnyTE+ItViRbaNGVDm2kapXZSIqLbmjBklTdqWSlBTnVw7ohnVKRpjSkRUWzFb2tTcU31gbkxInsftyHe/d3sHr5Q/6z6OniypWTxELtAucKzIoxjPFJu8jnkbVgjQs4rCq2eboDyPtXxCvZaWDXKxfSNnc8h2kt7K36V0fMwuArjWcUDidOmejkua1rV7TMexa/9YnSCK7SIgcdp0zzZK9FwickI7ga1tIyBxunTPLkqaTtDx3phRqZ3A1rYUkDhdejZbkv1tbe6JfeKkjf+MT9PW+/fvr4g//PDDfcX3io9C2dpTZFX8uURO1fsmp57owed1Th3T7mxrDg7C3Dpur9E5OK657hwdl/RL7fna34hNzbGNXD0X2pLvC2ewpTl2UajnrDGjsA3bkm2pmY/37Ue2vhq7kCz/zhlX7wa3RB7LjfJ4WykbNUO1s6aSmWUdNrrzL0RyED3LQrlLQuBS9NxasmAm3EPAOt6D5OIKSnVcAoSv/RLUzlen1DYK9VzSUd8XSlCbWafULkqatS2VoLadOhuwpVWMMcKpeVJIafE+787hwDzEiH0NPGIQXs26equRrDgBvKmG5+NSA33EwUoKouVjh6+Uli0jJbVioikEhLt1PAXOhZQX6rik9772S1A7Y51C28jSc0n3JJfvCyXALVSn0C5KWrctlaC2oTprtaWVjTFsU7iepdb+0g15LZc8VnwzLl/7sWS+X0vGmrxryXyJfGvqoSbvS9RFrT6tTQ9rk6cW7lvguzZdrE2eLeiwhoyXoIdL6EMN3Z6a59r0sBZ5JMcTxdl+wR0UOg7yqng/73OlL8bnfGwEjIARMAJGwAgYASNgBIzAZSMgP4AtC/gED5Wf9ZTP4JGqHmxfKc83MLL2RvTqO2sEjIARMAJGwAgYASNgBIzABhGQD8BWhdeK13OdDbofdTjEmP0NvP6KfQs5eyPg6WAEjIARMAJGwAgYASNgBIzAdhHgcxl8LqJ4o3i/61GHAwI1wNIJ36PwKgeAOBgBI2AEjIARMAJGwAgYgQtHoF1swNlYbGvF/wGor/xOC8qk9AAAAABJRU5ErkJggg==\n",
- "text/latex": "$\\displaystyle \\left({{img}_{(1,0)}^{2}} w_{2} - {{img}_{(1,1)}^{2}} w_{1} - {{img}_{(-1,1)}^{2}} w_{1} + {{img}_{(1,-1)}^{2}} w_{1} - {{img}_{(-1,-1)}^{2}} w_{1} - {{img}_{(-1,0)}^{2}} w_{2}\\right)^{2}$"
+ "text/latex": [
+ "$\\displaystyle \\left({img}_{(1,0)}^{2} w_{2} - {img}_{(1,1)}^{2} w_{1} - {img}_{(-1,1)}^{2} w_{1} + {img}_{(1,-1)}^{2} w_{1} - {img}_{(-1,-1)}^{2} w_{1} - {img}_{(-1,0)}^{2} w_{2}\\right)^{2}$"
+ ],
+ "text/plain": [
+ " 2\n",
+ "(img_E__2â‹…wâ‚‚ - img_NE__2â‹…wâ‚ - img_NW__2â‹…wâ‚ + img_SE__2â‹…wâ‚ - img_SW__2â‹…wâ‚ - img_W__2â‹…wâ‚‚) "
+ ]
},
"execution_count": 26,
"metadata": {},
@@ -606,9 +825,14 @@
"outputs": [
{
"data": {
- "text/plain": " \n(img_E__2â‹…wâ‚‚ - 0.5â‹…img_NE__2 - 0.5â‹…img_NW__2 + 0.5â‹…img_SE__2 - 0.5â‹…img_SW__2 -\n\n 2\n img_W__2â‹…wâ‚‚) ",
"image/png": "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\n",
- "text/latex": "$\\displaystyle \\left({{img}_{(1,0)}^{2}} w_{2} - 0.5 {{img}_{(1,1)}^{2}} - 0.5 {{img}_{(-1,1)}^{2}} + 0.5 {{img}_{(1,-1)}^{2}} - 0.5 {{img}_{(-1,-1)}^{2}} - {{img}_{(-1,0)}^{2}} w_{2}\\right)^{2}$"
+ "text/latex": [
+ "$\\displaystyle \\left({img}_{(1,0)}^{2} w_{2} - 0.5 {img}_{(1,1)}^{2} - 0.5 {img}_{(-1,1)}^{2} + 0.5 {img}_{(1,-1)}^{2} - 0.5 {img}_{(-1,-1)}^{2} - {img}_{(-1,0)}^{2} w_{2}\\right)^{2}$"
+ ],
+ "text/plain": [
+ " 2\n",
+ "(img_E__2â‹…wâ‚‚ - 0.5â‹…img_NE__2 - 0.5â‹…img_NW__2 + 0.5â‹…img_SE__2 - 0.5â‹…img_SW__2 - img_W__2â‹…wâ‚‚) "
+ ]
},
"execution_count": 27,
"metadata": {},
@@ -634,9 +858,14 @@
"outputs": [
{
"data": {
- "text/plain": " \ndst_C := (img_E__2â‹…wâ‚‚ - 0.5â‹…img_NE__2 - 0.5â‹…img_NW__2 + 0.5â‹…img_SE__2 - 0.5â‹…im\n\n 2\ng_SW__2 - img_W__2â‹…wâ‚‚) ",
"image/png": "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\n",
- "text/latex": "$\\displaystyle {{dst}_{(0,0)}} \\leftarrow \\left({{img}_{(1,0)}^{2}} w_{2} - 0.5 {{img}_{(1,1)}^{2}} - 0.5 {{img}_{(-1,1)}^{2}} + 0.5 {{img}_{(1,-1)}^{2}} - 0.5 {{img}_{(-1,-1)}^{2}} - {{img}_{(-1,0)}^{2}} w_{2}\\right)^{2}$"
+ "text/latex": [
+ "$\\displaystyle {dst}_{(0,0)} \\leftarrow \\left({img}_{(1,0)}^{2} w_{2} - 0.5 {img}_{(1,1)}^{2} - 0.5 {img}_{(-1,1)}^{2} + 0.5 {img}_{(1,-1)}^{2} - 0.5 {img}_{(-1,-1)}^{2} - {img}_{(-1,0)}^{2} w_{2}\\right)^{2}$"
+ ],
+ "text/plain": [
+ " 2\n",
+ "dst_C := (img_E__2â‹…wâ‚‚ - 0.5â‹…img_NE__2 - 0.5â‹…img_NW__2 + 0.5â‹…img_SE__2 - 0.5â‹…img_SW__2 - img_W__2â‹…wâ‚‚) "
+ ]
},
"execution_count": 28,
"metadata": {},
@@ -681,14 +910,11 @@
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": "<Figure size 1152x432 with 1 Axes>",
- "image/png": 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HbR5BEEOMTGjm83l897vfxbVr1zA/Py89LpVK4Vvf+hYAYH5+HsvLywiHw3C5rBNT/uQnP8Hjx4/x7NkzzM/Pw+PxIBwOH30ghAYJV+LUwgEkc2UUSpVaKEldAeoT99a8jrNRHy7MhBH2u+F2KC3fujkUBRG/GzfOjeMH93ewk8jpPJhNHk6Zt9UkbBgyTyuTrNEqqlFj+2p4cL1cNxqhnaYEUQCqHEjnS3i4FccLXhcJV4IgbKF6VtPptOaVSCaTiMfj2N3dRTKZRDKZRKlUQrVaPW5zCYIYYhwOBy5fvtwUKpxIJPCtb30LMzMzeO6555qOC4fDODw81CI/OOfY3NyE2+02Fa7lchmFQgGHh4dIJBIol8t0D+sRJFyJUwvnHJlCGcVKFWLcLROUoapPpyM+nJsKwu1UbGfT9XscuDIXQchby8Bb1W6eejGoZfGtf1bDd8V6ok3qD1FUMiEUWRSz4njU/6tTV8XjtZmzLUSr2n6uWMaTnTguzY/aOhcEQZw+1DBgNQS4Uqkgk8lgb28Pjx49wq1btxCLxRCPx7X6Doej7Tlp/YCSMRHEcOF0OrG8vNxUvr+/D8YYJiYmcO7cOemxh4eHAIBAIACPx4ONjQ3Mzs4iFApJ65fLZcTjceRyOZRKJWkdojuQcCVOLRxAplBGqdzirRgHlieDeGEhaisDsIrCGAIeJ+ZHA9hN5PHsMKPvXGjMmMm3VT92MhTLQn/FbdEEZpa22MTOTL6AW2t7+NS1hRaWEgRxWqlWqygUCnjw4AG2t7exurqKlZUV5HK1CJRqtTqQIlUGiVaCOH1cvXoVgUAAP/jBD3Dx4kWMj49L68ViMbz99ttYWFiAx+PBrVu3+mzp6cF6oh5BnGCqnGM/VUC2WNaFzYrUHqpqiYmUNp9bWD1xkdvpgNuhQOik9lPNCGxMCmUWNqwLHVYTNzXaARcqCe1wk/b1CZyEdsT2hXKu1QMqFY5coSx4kQmCIPSk02ncv38fP/7xj/Hee+/h6dOnyGazWiIm1SMLDLZHU7RR/EkQxMkmFAohEonA4XAgHo/j8PBQ+vefyWTw8OFDzM3Nmc6bJboDeVyJUwvnQCJbRL5YQXP2XUAUd5UqR7na2cMK5xxcbN9402NcK2YywWqwqfFDv0xOI1+T/iDG9MfI2zL0WY8nFsWtKLCrnNfmBtMUDoIgTMjn81hfX8fKygoSiYRpPXFt1EFCtUtcAscotAfNZoIguofP50MoFEIoFEIymUQsFkM0GtX2q/P2s9ks9vb2MDExcYzWng7I40qcbiRCTyZi95N5bBxmpfK2FU/2Uni6l255rCGRcaNQLOcm5Yb6ZjS10wKzatVqFdl8CVVSrgRBHAGjMBwkZPao9g6qzQRBdJdgMIg33ngD+/v7WFlZadr/5MkT7O7uYmZmBqOjowgGg8dg5enhyMKVMfaEMfYxY+wDxtjNetkoY+zrjLEH9Z/RVu0QxLGiRcmq4bSN8FjOOdYPMri/mbAt+AAgky/ho6cHSOdLqHksxbBe9YdF+K5YroYBq5mVjGG9TeXG8Rja12UVVvtTjxfCmIXwYPGAQqmCnVgaxVbzgwmCIGwyqN7LQbWLIIje4/F4cObMGRwcHGBtbQ3FYlGXMXhjYwOJRALnz5+nNVv7QLc8rj/LOb/OOb9R//yPAXyTc34BwDfrnwligDHO5RREJYDdZB5P9tKIZ4solitmjWiUK1UkskV8vHaITL6kF5ScgzO5IIRMKGqhuzAI03r4LlfDkcVdenHLZW1q/YqfjecEkn0clUoViUwBFUr3ThCETcwEoDrXddC8l6q9RrtoritBnB6cTicmJiZQKpWQTCaRyWS0OfrVahU7OzvIZDJYXFyE2+0+bnNPPL0KFf5VAP+qvv2vAPwXPeqHILoOk8TbHqTyuLsZx+9/9yFWd9Mt29hO5PDB0wP8q++sYCeRM3YgrOdqQPYcZFpZX8XqkY+1OL4tVzJBEEQHiAJQFH1i6O2gYrR3EIU2QRDdR1EUeDweXLx4EbOzs/jggw+QTqe1JXDW19eRy+Xw0ksvwefzHbe5J55uJGfiAP6a1TLA/H84518FMMU536rv3wYw1YV+CKIvaL5Lg6cxVyrjRw92UalW8XA7gjcuTiLgdcLtdAAAiuUKMoUynu6l8b27W1h5lkC2IC5CLXpdG32J4cEcaq4mbWXV+i6zpEqNbc7qbRmSLWntyNpoKjdp31De7lxZgiAIkWESf6KdxoRNBEGcDtRswR9//DEuXboERVHw4MEDjI2NYWJiAopCaYP6QTeE65uc803G2CSArzPG7ok7OeecMdb0eMsY+zKALwPA4uJiF8wgiE6ohdRyDjCmDw82irZymePpXhoOxhDLFDEZ9iIadMPrcsKhMKQLJcQzRdzdiOEnD3axfpBBuVIXrWrWX61ZdQ6p0A/XZxVmsjA0E/HJdVl/9fuY2TG6rMHi+TDUNQhYTmqVIIguYPTADpoYlCVgGjQbCYLoD+Pj40ilUtja2kImkwHnHGtraxgdHaVswn3kyMKVc75Z/7nLGPsTAK8B2GGMzXDOtxhjMwB2Jcd9FcBXAeDGjRv0JEwcLzpRac3j3SRWd5P41q1NeN0O+FwOjIe8eHaYRaZQqid0gl5kqnNUj/rMI009XBPd+kRLrfuybUob54YgCEKGlTAdRNFqZBhsJAiid0xNTSGXq0392traQrVaxXvvvYff+I3fwJkzZ47ZutPDkfzajLEAYyykbgP4PIBbAL4G4Hfq1X4HwJ8epR+C6D3iWqsmCZKEJEoctbVM88UKkrkSNg+zyBbLqFZ5bW1TqZdTTZjE9f5LIQFSQ3sa/ZomyZx4o1ynW5mkHS5UarJPbEdo31BO3laCIDpBNr/VmNxoEJMdmSVoMu4nCOJkwxhDIBDAtWvX8PDhQ9y6dQtutxuRSAThcPi4zTs1HNXjOgXgT+o3dCeAf8M5/0vG2E8B/DvG2N8H8BTAbx6xH4LoISbhwcykvC42GYAqr6JaBUrlStOcU+34+lI1ohOUCZ+ZMA9VDOs1hhHXe27s0y2tIy51Y2yn/pColsvGJJQ1Lckj9NvYRQ9rBEF0hjEEd5A9mTLBPcj2EgTRO7xeL5aXl/H9738fyWRSW7fV6/Uet2mnhiMJV875YwAvSsoPAHz2KG0TxCDT8rFFkgnYeAwz2W6uZRSz0l0t6eajVjeingmCOJ3Ikh0NIjLbWn0mCOLkEggEcP36dXzve9+Dw+HA66+/jmAweNxmnSq6kZyJIIYWLtmSJiUSQnmZsb6JV7axKYbdMtMES+aZg23YJ2KzfW0sTe002ucmx9OjGkEQ7SCKQLN1UQdNBLYKDx40ewmCaE0oFMKXv/xly8Sw4+Pj+MpXvoK5uTldGLC6NM6v/dqvoVwuY3p6usnbevbsWXzlK1/B0tIS/H5/z8ZxWiHhSpxyuF4bMi7oM0PYLFQnZ6O8FjJsR1RyrW5NMAqSuZ7dVyvXwov1FmqC2SBmG9HIgl2i7VwuuJuyFovhzZCLVqlwJwiCaIFx+ZthFH9G4T3I3mKCIOR4PB68/PLLlnX8fj9eeeWVpnLGGBwOBy5dumR6bDQalR5LdAdadIg43XDoPZFcnXdq7lUU10pt55FFEj3cXC6Jwa3NfW2vXdOwZCu9aTEYNV1Tm9HJBEEQAPRJjIZR8FESJoIgiOOHPK4EAbQXvqtuNz3HcDQcps115WHGZv0aPqvKlQveVHU3a8N+JivXf5Z5WvWiuJGcimQsQRB2kK2FKkt8NEhYhTerZYNoN0EQxEmFhCtB6NCLsoaXUQjn5RJhqLki9eHFavRtrYodQQydOOUcYIbsxo3swbzhClXblArT5rBkfX/65XmMNuqEuFSYEwRBtMbM0zosHlgxvHkYQ50JgiCGHQoVJggDTBInbAzH5UJlbvHcIu6SST1jGTf8n7WIzdXtYvJys8PtSE8xcpnXP1C4MEEQnWAm8ozzXgcFWQZh0QNLopUgCKK/kMeVOLW4HAo+9/wsbpwbQypXsnEE1/2wRwcPYnXPbZVz/H+/fRcb+ynsJrK6ZEuqemSa/1OfjEn0+jbmx+rlLNMNRxDLhjVmZbbR4xpBEO1g5VUdVI8reVUJgiAGCxKuxKnFoTDMjvoxOzqY6crLlSr+4uYq9pNZnRBVf+jCmIU5sWIory7rsRi3rJui29hiujmt+nm8jYzI+mOIkw3nHPl8vskbxhiDoihwOBxwOk/uVwnnHOVyGQ6HA4pCQUqdYpaJV72uBlG8ypbtoYzCg0mxWES1WkW1WjWt43Q64XA44HA4+mjZcFOpVLR/ZhERiqJAURS4XC76myB6zsl92iCIk4JJ3K8qSJuWuQGk22qYb1sYYqPpK+n0wTnH5uYmKpVK0z6fz4eRkRGMjIwcg2X9gXOOZDIJv98Pn8933OYMPUbROiwPukZbh8Xu0wDnHLFYDLlcDoVCwbReJBKB3+9HKBTqo3XDTS6XQzabRSwWM63jdrvh8/kwNTXVR8uI0woJV4IYCvRZf3VZhWUzZU3Cg/XRzvrwYGlf0uzBvLlLYujI5/NIJpM4PDxEOp1GIpHA4eEh8vk8crkcUqkUOOemHleg9qbd6XRqHlePxwOfz4exsTFEo1FEo1GcOXMGfr8fbre730PsiHK5jEKhgKdPn2Jraws7OztYW1vDZz/7Wbz66qvHbd6JYpjEn7gObb8FdywWQyqVku5TFAVutxvhcBher7dvNvWbSqWCdDqNvb09JJNJ7OzsIB6PI5PJIB6Pt+1xnZ6eRigUQjgcxszMDEZGRhCNRuHxeIbqujwK6r1+a2sL2WwWuVwOz549Qy6XQz6fRzqd1ryt5XLZtB3V4+p2u7UXfNFoFBMTE4hGo5ibm0MgEDjRkTlE/6CriBh4OOeoVDmyxQpK5SoK5drPSpWjVDH/kuqwtyMe2i1Fx1GtcqRyRZTKFXMhKm7qJq0asgPX58RahQc3t2mcy2pjGZxqHqjGgGrBvI6hTelH5q7/cwKKF2AeAEo9WxXRLpxzZDIZFItFFAoFZLNZ7S364eEhMpmMJlxzuRxyuRzS6bQujFPFKnxSFa6jo6MYHR1FNBpFPp9HJBJBIBDAyMgIvF4vXC5X/wZvAecc1WpVOy+ZTAaZTAbpdFonXLe2tnDjxo3jNtcS9XcWi8UsH95FFEVBNBpFKBRqW/QUCgXs7OygUChIvfFATXAdHByYPvTK5pByznF4eIiNjY22H3QZY9pDcy+8alZL43SKKrby+TyKxSJKpRIKhQJKpRJKpVruhVgshnQ6bWqT2+3GyMgIfD4f3G639i8YDMLlcg1taKwqVtV70t7eHvb29pBIJLC7u6sJ11gsZut3It6rEokEgsEgwuEwEokEIpEIRkdHMTY2Bp/PB6/XC5/PdyJEbLVaBedc80gXCgXk83ntfqcK12w2i62tLZ1wFbHzwsbv98Pr9SIajeLw8BCRSATpdBqjo6Pw+/0IBAIkYokjQVcOMZCID8qVKkeuWMb9Z0nsJvN4FstiP1lAKldCLNMcFsRNPxk9hVxeze6yNbKezcSrsR1pU82e06e7KWTzpcZ+rmYaFueb6r2wYqKm2hdNo1zsicHYDjcpb7Sj1eMmGrKyA2S+BZSfAdViY0zieWkau+R8OycB5zTgHAPci4BrDmD+xv4T8DDRS2Te0UePHmF7exubm5u4f/8+0uk0isWiVl82d0/dNnqZzB5g8vk8CoUC4vE4Hj16pNWZmprC9PQ0PvnJT2JpaQmjo6PaMcfxYCieH1WAbWxs4Pbt29jY2MDOzo5Wb1geXLe2tvDhhx/iG9/4BgqFguXvU932+Xz47Gc/i+vXr2Npaamt/g4ODvDv/t2/w/r6OrLZrFbe6loSt43roKrb7777Lm7evGm7HXXb6XRieXkZn/vc5/DSSy+1NR4z7PTdbnsilUoF+Xwejx8/xs7ODvb29rC1tYXd3V0tPNPu+N1uN6ampjA3N4fp6Wm88MILmJiY0L2UGIbrWT1H+Xwet2/fxuPHj7G1tYWVlZWmMQPm86fF9oz3se3t7aa2HA4HXn75ZSwtLeHs2bNYXl7WRP8wnDcR3TNUpYJCoYDHjx/j2bNn2NjYwOPHj7WXAmr9dq5zszqZTAbZbBaHh4d4+PChVr6wsICZmRk899xzeP755xEMBgEM33kljh8SrsRAksiWcGczgZWtJLbiOTzaSSFXLKNYrqJYqaJcrqBS4ShX1ZszN2hGM6FoV8haeDab2rHqw6Qt03b0dQqlMirVaqNMXdwVzfNXjfNQOcyX01HFaaO+fOyqeDW2Y/pdU80D5W2g+BTgef04jWOWfq5vFzcA5qp5XJkHUAKA93nAewXwLAHKCIlXCwqFAnZ3d/Ho0SPs7+9jc3MTsVgMhUIBxWIRuVxO85JZPaSID3pWdazKAWjhyDs7O5idncXs7Cw+8YlPIBKJ9D28sVqtYmVlBbu7u9jZ2cHq6qrmichms5qYN45hWLHz++lFf+1uy5J/tdpW6eUyOt08Z5xzFItFHB4eYnd3F6urq9jd3cX+/r7mcS2XyygWi6bXoexvUi0vl8uaJ/LBgwd47733MDs7q4nYycnJgQ8nLhQKWFlZwb1797SIB/XcANa/j3YEluzvoVqt4t69e1hdXcU777yD5eVlLC0tYWlpCbOzs0PluU6n0zg4ONDd65LJpOZxFSMlrO4Ndq892bbIzs4OYrEYnjx5gp/+9KeYn5/Hq6++iunp6aGZRkIMBiRciYGhXKkiW6zgWSyLrVgOH6/H8WgnhZ1EHk/30jrRBtS2tTVUjR49Q9gsF44xirBGVt5GGzXB1jhODadVhZx+nqneUylS80w2e2K5wQ61P93YBAshaV/0rBqzCsPggRVHCzCtfQbDcbqlcASbmzzJZg+K1VqYcDXbWrgaxmO5zZx1u8u1tj0XAEeoFkZMAGh4buLxOBKJBNbX17G6uoqDgwNsbW2hWCxqc1btCE/jfhHZXD+ruX9q2GMmk0GhUEA6nUY4HMbS0hLGxsYQCoX6JhKr1SoePHiA7e1t7O3t4enTp6hUKjrv37ALVlkCpF6OSTxv7XpoZJ9b9dWv34/V9dCOveVyGdlsFqlUCoeHhzg4OMDu7i6ePn2K/f19HB4eWp47u31zzrWXUwC00P9kMgmXy4VCoYCxsTFdxMMgoIbsHxwc4ODgAPfu3cPKygr29vaQyWS0OlbjbnXNAa3vdUBN8KXTaTDGNE9lPp8HYwzhcBiBQACKogzkPUINM1evse3tbTx8+BD7+/vY39/XvgNkkQ+tsHqB2eq6Va9L9d6fTqeRzWYRDAZRLpe1fAgEYQcSrsSxIr4pzxUreLCdxO9/fxUPt1M4TNfnSWpqsYVo1RSXQSRpYa1cKJKLPrVCY5/BC1DvW3jk0sq5UK4KUVWc6oQuaxbPRtGq9c30ZcY+G0MSbBZEIhfPi1afN+oJfaom6cub29fEren3lMSjajzHdgWrtl0Cch8B+Xs1b2v0twDfVcA1Xx/04D1E9ANjyOv6+jp+9KMfYX19Hevr69Jj2n3gEkWqerzZA72dh8dYLIZYLIaVlRW89dZbuHbtGl588cW+CZJqtYrvfve7SCQSTQ9jxv6HKVRYxPhCwVje7XHZ8bjY9VzKHojNxtNrD3K7QkncL5JKpfDo0SN8+OGHuHv3LnK5nDZ/VdaPnRcBrWxWj1NFy927d/HSSy/h8uXL+Nmf/dmWY+wXYgK4d955Bx9//DGePHnSVM/KTrO/26Pc6zjn2NrawrNnz/Duu+/ijTfewLVr13Dp0iX4fL6BuTeI11omk8GzZ8/w7W9/G8+ePcPOzo6lSG11b7AaYzt/88b9mUwGjx8/xuPHj/Haa6/hypUr+Jmf+ZmW7REEQMKVOGaqHCiUKvj2nR3cfZbATx8f4CBZQL5UaRJL2ia4QYjVYY2iVre9XtwWmcm29lkwTNSkpoJONkYbfbez03iuzKqajsey5ZaV2qdaAngMiP0hUHi5Fj4c+pTEwtNBuVzG4eEh3nvvPWxsbGBtbU03d1XE7nww2TFmx7V6yLEK4eSc47333sPq6irW1tbw6quvYmZmptWQu4Yd8TGsD1DtPkh2E6vroB2vmEirFwq9Gk87L2iMFItFrK+v4/bt27h9+7bmZcrn81qyHDNRIQvPFMvtXJsyUXL//n1sbW3h8ePH+MIXvoCpqaljD9M8PDzE9vY2/vN//s/Y3983zZ4s0mr87Qh+44s59Xhxu1wua/eqH//4x/jSl76EaDSKQCDQsv1ek81msbe3h5s3b2qJ5FKpFIrFYkfXi9kxYrn4WaUdD6y47/bt29jc3MS9e/fwpS99CaOjo8d+TRKDDQlX4tioVDlSudpc1vefxvBwO4mnexktRJU14m+FoyTePPVH3dOqHq8WqR4/1ZtoPFQtY/qGDV7OxmfNK2nwBKu1tXaaOmrYyiSCTvTYinaIob5ayDJThyt4fM3Cg4XwX3GcmrTkkjY0Expzh3XnSa+8dX2BN47tWqiwRhWoVoHqLpB/CEABfJcAxwignK41NmOxmJYEaWVlRUvsotKOt8p4DNBY6kaWAbhcLqNUKjU9gBuxeqPPGNMesB48eICJiQkwxjA1NdUXwSja0mn43KBj5bnr5bxQsQ8rsWw876Kt4jHDFMKtLjO1tbWFtbU1PHjwAOvr6yiVSm2/8LESE3bPiXiNZ7NZlEollMtlPHjwAKVSCYuLi3A4HH0/t5VKBYlEAqurq1hdXcXTp0+1JW1ajcNMNJnVkZ03s5cFZgJWfSGYzWZx9+5dnD17FjMzM/D7/X0/d9VqFaVSCXt7e9jf38fGxgYePHiAw8NDxONxyxc7ZhE06rnw+/2WfavXkeyciu0bt81Ip9NaiPODBw+wuLiI+fn5gQ3HJo4fEq5E31FvcsVyBWsHGfzzb6xgM5ZFrlDRiz5BFKoqtKaHJA9cjAvFNdHLDKJXE4uCuNXtE8JiAb3DUz/HVS6q1HJRMBuFYnNYcqMNo02qkGXqduODpoEbNjbbr7Yt6kVtvi2rjakhto32MU3EMhjPieRFgn5Awg/JtjBm29uydvIPgNI24FkGfM8Drtn6YE7ul534gLCysoL79+/j+9//fkuRKvtsxLjf4/EgFApJ5x4lk0kkEgktI2U77Rvr5fN53Lt3D5xz7O7u4ld+5Vf65u2UPeS2460ZdKweIHs1PtkDs0wwy14O2L127bwo6QbteuQ5ry3lc+vWLXzrW99CMpnUQoLNhEQrb7LMHjvHmImXUqmE/f19fPOb38SlS5fwG7/xG/B6vX1NPMQ5R6lUwv379/GDH/wAKysrUtuN9zMzgWT2U6Ud77yZ4AVqXvSDgwP82Z/9Gd544w3cuHEDy8vLttrtFuq5SyaT+M53voMnT57g6dOn0jFYCXjZOXE4HJidnYWiKKb9F4vFplDuVtd2Ky9tsVjE/v4+/uqv/govvPACxsfHT8xSRET3IeFK9B3OgXypgn/7o6d4/8kh1g4yKJWrjZ1MqKgiCC59Y9BciXZucRx1XSNrTnVJGsrNtsX6xmLpdpsODt2xLQZntZsZN5hkX13o184Ja+4fwvmyNY7ee3MAAJUMcPBvgOivA8HXAedUf/o9JrLZrPYFv7m5iUQiAcD8gc2sTOTs2bMYHR3F+Pg4xsfHtfUMFUWBw+GQPtCqi9Krayvu7u7i7t272N/fRzKZ1NU1exA3Psw8efIEqVQKkUgEzz//fM8TyFh5Vk/aA1MvxuNyuTTvuPgCQ0RNyJVKpbQMplaiTP2dhEIh+Hw++HztRVE4HA5MT0+39Bp1A9HeYrGIWCyGd999F7u7u9jY2EAqlbK9ni5g7h33eDwol8uma+Wqx5iVmZ3vw8ND3L17F3/0R3+EL33pSxgfH7dt61HZ3NzE2toa/uzP/kwLDbYSomb7otEoIpEIFhcXMT09jXA4jEgkAo/Hoxt3qVRCNpvF5uYm4vE49vb2tGziZmsMq33IRFc+n8ePf/xjrKys4Jd+6ZewuLiIiYmJbp0eUzjnuHXrFlZXV3Hr1i0tG7WV/epx4r1OURQsLy9jamoKk5OTmJub09ZfdbvdLV/YFItF7O3tIRaLYXV1FU+ePEEsFjOdomL3RZOamMvtduPnf/7ntSVzCEKEhCvRd7LFCj5ej+POZhyre2kUS/XlXlSnmiGUlotqssnb2vC0MqMnUBKaasweLKuriTNdkqeaHVr4sswbqNs0eIq5xD4IX4pN7Ujsk4xfDC/W5SHWhR0L9gvtNzS/6M3U2yELX+Z1b21rbWr4HcjOfSeeV12bFaC0DxRWa2u/Bidh7xXGcFGtVpHP57W3648fP9bCbM2QPTA4HA74fD5tGZpAIIDFxUVEIhHtITAUCiESibS0SX2ACYVCGBkZgcPh0MKVNzc3tVBiO2/8Oefa+q/37t3D9PQ0vF5vT9+6W3lWzUJVh5VeeJC9Xi+Wl5cxPj5ueh1mMhlsbm5q8zpb2af+m5iY0P61g6IoiEajCIfDbR3XDrJrOJ1O4+HDh3jy5Ani8ThisVjL862243A44HQ6EQgE4PP54PV64fV64XQ64XQ64Xa7dcJVXSonHo8jnU63PLcyu4FauH8qlcLq6iq2t7fhdDpt/d0fhWq1qi2JsrKygoODg5Z/W8bzrZ6naDSK6elpRKNRzMzMYHx8HIFAAOFwGC6XS+c1LJVKyOVy8Pv9SCaTmJiYQDAYRDwe1zLwlstlqUdX9nusVqta6PD9+/fh9XoRDAbh9Xq7/nfGeS0rdSaTwc7ODu7evYuNjQ3tHmvn3qS+hBwbG0MwGEQ4HMaZM2e0DNNTU1Pw+XxtzS0NBoMYHR3Vxn5wcICdnR0cHh6iUCho9VrdZ8V7bLlc1qa/vPTSS1AUpS8voYjhgoQr0Vc45zhIF/D/+8EqHu4kkcwKDzzMKKBEUWUIeUVDiOlEnyEkVRRrqghrksSCqOT1vhhT9VJD3jFAJx7VgFqx26YwYiYvV49lMD4U86b/Q+3H4CXmXFJXFpLLGsXq+RBt1y2lI+hCXeizcJwoeuXitd6IaIvMrna2W4Uf5+4AvAQEP1Ef88kRr5xzVCoV7O/v4zvf+Q7u3bsnfcveKuRVnb80NzeHF154AXNzc1haWoLX67UMDTODMQaPx4Pp6WlMT0/jueee00TrH//xHyORSDTZKXuIEa//bDaLmzdvYnFxEV6vF4uLiz0N27V6mDKb6zYMWCVW6dZYQqGQLhOojJ2dHfz4xz/Gzs6O7mHWbB4c5xyKouDcuXO4evUqrl271hVbO8Uq1FJkd3cXu7u7lnWMiB7VYDCI5eVlzM/PY2ZmBjMzMwgGg1KPsyq03n//fW2NTjOPt6w/8frO5/N49uwZ7ty5g0qlgpGREV3dbqKGuK6srOAnP/kJ7t6922SfbK6qkZmZGczPz+PGjRuYn5+3lSDJ5XLB5XJpLzTUsW9ubuL27dt4++23m5La2fmbKRaLeOedd+D3+xGJRLTkct06f+r41Sy83/zmN/H06dOmvyWjzUacTid8Ph9eeuklXLx4EefPnz+yyFaXsFlaWkKhUEAikcCPf/xj7e/daF+recfqdjKZRDqdxqNHjwAAZ86c6dhG4mRCwpXoK+8/jeGjtRjubMRRLBtCnzTPqfpR/6VlDHmVht4awndlYbstw2qZWM+8tijcmu7/osKUHMiMNjYGbdmrbDyAoPE0oax+FkS3ybG6cmGHTkPWj7H/NddnD1VxG6iWgfSPAO8lwNX7sK1+kUgksLW1hT/+4z/GwcGBqQAwC6sLhUI4d+4cXnnlFS0UWPXotAoLa5doNIpgMIjf/d3fxdtvv42PP/4Ye3t7pp5WFWPZj370I+zu7uI3f/M34Xa7ezb/Tvbw1CrEclgwm9c6CPN3j7t/u7TyEqmfxbqtzjHnHGNjY5iZmcHVq1cxPT2tZfdVk6E5nU7Tl0kjIyMIBAKYnJzEm2++if39fXzjG9/AxsYGksmk6csh2Yss1fa3334b8XgcZ86cQSgU6snfWyqVwu7uLr72ta/pphO0mn/NGIPL5cLExAQ+//nPY2FhAeFwGD6fT5o4zi4ejwcLCwuYnJzElStX8ODBA7z//vvY2NjQwodlHlfjuczn83j77bexurqKv/t3/y6CwSA8Hk/Hdomoyyj98Ic/xPb2NmKxWJOX1Sjw1W2n0wmPx4OXXnoJZ86cwdmzZxGNRuHxeLqetdflcmF0dBSf+cxnsLS0hNXVVXz961+3TLYlnkPZC9fvf//7yGazJFyJJki4En2hyjkKpQoebKdwZyOBfKkCVZqq4bc6b6IhJFTnBdQ5ZvXhr2q52RqojT7shBhDFx6sC5tV+6qHzYrd1RoVy+rbgke54bGUeWKFY4TxM/FgYfz1QdTsk5wzyMZp6Es1hQke5aa+ufh7sEDs2GxbsKOtba0d3UkAUAIqKSB3H3DNnCjhur+/j7W1Nezs7OgeWmQPK2K4pcvl0h7MlpeXcfbsWe2Br1c4nU5tnuGFCxdQLpeRzWZRKBR0c8laCdlYLIatrS1sbGxo8696QS88kYOCHdF13AyaPTLaeelifBAXRY7P50MoFMLU1BQmJiYwNTWFpaUljI6OShOgmaHOO/d4PAgEAvD7/Xj++efBGMOzZ88Qj8eb7DfaYhSx6XRau89cuHChq/cINWJke3sbKysriMVipvN1jSJMURT4fD4sLi5iYWFBm4/v9XqPZJP6O1JFnMPh0OwslUpIJBLIZDKa/WYvBdWyZDIJzjnu3r2Lc+fOYWJioiuZmvP5PNbW1vDs2TNNtBqvL9UWcTsQCGgvRy5fvqx58V0uV0/+3hRFgaIoCIVCmJubg6Io2NzcxKNHjyznt1vZcnh4iMPDQySTSQSDwY6igoiTCQlXoi9UqhyHmSLee3yIm48PtHI1W29TeHCTABPmnqrH6sQZ15VDUi7uU8OAhUcOTUSL4cWiUNWF2GpeX4NnUWaHRLTqla5E1HH9OMR+msbMGvbpxqJV5ro9jbEZzo0gWsWeRIEtCl1L+coNfZtti+Ow2jY5R7rtah5I3wR812qZhrVBDTdPnjzB7du3dZ5WoNkrIeJ0OhEOh/HZz34WS0tLfUkcItri8XjwwgsvYGFhAevr69jb20M6nTa110g+n8fe3h7effddTWz3UuAMunhqF7PxDNo4zbzdg0Q7NpmNR1EUjI6O4vz58/i5n/s5RCKRrqwBqs5L/dznPgeXywW3240PPvjA8t4gE+GMMcTjcdy8eRPz8/Ndn6tZKBRw584d/PCHP2wSrVZixul0Ynx8HG+99RYuXLiAcDjc9WtEFXqXLl3CuXPnkMvl8PDhQ024Wp1LlUqlglQqhW984xtQFAUjIyNdEf/ZbBYrKytIpVIol8stPdPqz+npaVy7dg2f/OQnEY1G+yr6RkdHtbnGf/RHf4TV1VXLBFJmZLNZHB4eYmtrC8vLyyRcCQ0SrkRfSGZL+A8/WcPqXgr5Ys3baqSmmwyCDjJ51PAVGsWsXWRfP01iVWeY+XHGcm5WT2xDa9MorFt0wCVlLeyx1b7QvDBcfTbhpopmJ15TreZGdPA7kyfnErstA8X12r/SIuCa7qCTwUNd4w4w91AZy8fHx/GlL30JFy5cOLbMjC6XC9FoFL/1W7+Fb3zjG/j444+bxLeKbFzpdBo/+tGPcPHiRfj9foRCoX6YfSKwe530EzPPFTA4IcxW2LVP9GiGw2FMTk7i9ddfx+zsLKamphAIBHryEP7aa6/hzJkzKBaLWFtb014UyRKNycaRSCTwwQcf4DOf+QyCwWDXwkkLhQL+03/6T1hZWWmZcVz87HK5MD09jb/39/5eV7ysdnA4HPjiF7+IO3fu4Kc//Snu3LljmXVYpFKpYGdnB7dv3wYAvP766z25nlv9bTPG8MlPfhJLS0uIRCLH8jfldrsxPT2Nn/u5n8PKygq+/vWvm9prRSKRwP379zE/P3+ksHDiZEGvMIieUyhXEM8WcWcjgUS21PCoGkQIN/Ok1UNxNbGj7tfaEHfwRltcX672y7nQF8S2jOX19tUQWbEvLrRv+Cl6KDmARnZiDqEj44Ca7ND5WMXDxHEa7NOdwyZhzC3KueC15VoyJnAueHu5LozLVHyKnlGxP/X31dLDKqkjXi9aG8b2OVAtAsUtoLBmYtzwwTnX5gnZ9aQ5nU6MjIzA4/HA6Tye95OMMTidTkxNTWF+fh6zs7NNdcyy96pjzmazePbsGba3t3tmpywBzLBnEVaRhZUPAlZzRAcJWeIbznnT+TSGtzqdTiwtLeHKlSu4fv06lpeXMT09jZGREcv5q0fB7/djfHwczz33HCKRiNaH3fDMarWKXC6H/f19bZmto5LP5xGPx7Vsy9VqVXpOZZ/n5uZw8eJFTE5OwufzdSX0thWMMYTDYczOzuLy5csIhUI6wSSbniFSLpextbWleWzVNXy7baOILFxYXdqmH+fMzEaXy4WZmRksLCxgdHS06XtIds8XfwK1lx6Hh4dtLStFnHzI40r0nEy+jN1kHh8+jRkEZQ1NjHHdp7pnsjEXtVbGa/NKhSNrm0K5MWyWQ/e54bwUw48F0an2JX7B6kKaeVOdxvzXej1J+G7DVFHgom43l9jR6JNL2hHbV8OXRdEM4zEywWosko0Non1iOx2GChtttBsq3LTfpP3CKqD4geCr1jYSPUdRFC1jarlcxtOnT7WHEKukLGL5gwcPoCgKzp8/37SvG5jNWxx0D6AVxvNoNUez3zapDHJ4sEqrOa2yMjWD6+uvv47l5WUsLy/31EaRQCCAT33qU1hdXcXBwYGWbbidc7y2tgav19uV6QXJZBKbm5t4+vSpJuLs2MIYw/PPP48XXnih6wnk7DAzM4NwOIyPPvoI1Wq1ab1sq3vD+vo6UqkU3nzzTUxOTvbcU2gnlPm4mJqaQqVSwdLSElZWVnTTRVSs7lEkXAkZ5HEles7j3TRuryeavZl1jGG26oZanwn/dBXEj8ykHfU4Zh2hapRI0nBhWREz3W2Kzk4Lu+3CmzY6R9V/ok1m2/IOufCvVV1p722Um9TJrwLZOzaOGX4GzZNmxrlz53Djxg1Eo1EtBNHuQ9aDBw9w9+5dxONx06QunSC+4Ze97R+0h8CThszrOizXsxnnz5/H5z73Ofyjf/SP8MYbb2BhYaGv/avzy1977TW88cYbWnmr8yruf/z4MTY3N7tiz507d/Dnf/7nTeG2Rs+l+FkV/VevXsXc3FxX7GgXp9OJYDCIn//5n8elS5c0O1XMQnVVstks/vIv/xIbGxtdtcvK0z+ojI6O4hd/8RfbSkCmksvlsL293dX7PjH8kHAles5WLIcn+/U3bVwfAst5I4QXqGXF5cK2Ws5VMcTqnkDNsSmIJCEMlQmeXS78T+d5NXoF620wXThrvZLOjnq5YEeTWDOOs8k+gydTHLN2btSuDWLQYB9T68q82VzffuNQbjCZC/1zzX6tb66OvvafU1EwEvDC4TDcQoxtcjRs0gYk86o2zrG+jtCGaaiwobyar2UYriSBavdDtY4bq6UQBhW3241gMIiFhQVdUhrjQ6tsLMViEclkEmtra6ZzZI+CzOs3DOe0HWThrceJ0ZZBfklgJ5Q8GAzi/PnzePHFF3Hp0iVMTEwcebmWTmCMQVEUTE9PY2FhAU6ns6VX2+g9TCQSUs9YO1SrVW1t24ODg6YXE1ZJrHw+Hy5fvoxIJNKzLLitUM+jmo13YmKiZXi3eC+uVCrY3NzEwcGBlnH4qLRaNmhQcblcGBsbQzgchtfrbWtqRqVSQT6fH6h7F3H8dBwqzBi7BOAPhaJlAP93ABEAvwtgr17+33HO/6LTfojhRb3ZPN1PY2VLTMpQF0JiXC6ETLiMS7L7cqGusKFpR0lYriGZDwe0ZXJqPwQBIL0xcq19tS4TynX1xPYlbTYtuaO2ZRTOTBwn13aLYdBiO+o5Y+L5M/Zh6JtprmdRMDTXZYbxa/YywO1yYCoahMsoXMXudX0Yfn+W4lWyrf2QHWfYrpaAahYo7QDuWQAnK6nDMDysyHC73bh69SoSiQRisZjpg5gscU82m8WdO3cwOzvblWysarsqxgfoYT3HZpglwDkuxIf8QQ51BMxDnEVGR0fx6quv4vXXX+/Z0k3tMDMzg1KpBI/Hg3w+35bHShWuR5l7XK1W8ejRI2xvbyObzZpec8Yyl8uFcDiMF154oS/JmKxQFAWTk5NYXFzEzs4OYrFYW4madnd3sbW1hampqa4klhu0v2G7OBwOBINBRKNRhMNhaYZhMw92tVpFsVgk4Uro6Njjyjm/zzm/zjm/DuAVAFkAf1Lf/U/VfSRaTy+cA9liGdvxHJ7Fspq4EENsRTTRKn4GDCIRxr2NMGITdO3ZDe0VtaTFPrlFBsOO+v3CzJuQlYuOy6b6TKijO8CkHTS8w7z+gQNQFAa/1w1H01voSk00omresLQXG7RzHit5IP0eUI63cdBwMEzeKhG3243r169jdHQUgN7romL2QJZOp/Hee+8d2RNkxmnwuKoMyvVi9AQOyzlXRbcxvHVmZgYOh+MYLdPj9/vx8ssvIxwOW9YzXg+FQgGZTEZLptQJpVIJ3/ve9/DkyRNdH2bXnnouz5w5g+eee05LxjQILC0t4a233rKV5M6YwOvOnTt45513enZtD8vfDFA7j4uLi7brD8p9ihg8uhUq/FkAjzjnT7vUHnECKFc5dhN5ZAplVCr1L0DOGxG2TQqLQ+ep05STodwQvqr36ql1ucE7qZaJ7Qjl+opC/4329X2pNgreQGkyIbFcsFuwQ3hsN4QHG23n2ra2uq0hbJiDG7zHhnHW25d7sPV2amHDdW+r+ALA6VAQ8rmhKIYvF14CKnGAl3Vt6c6laXiwxbY0VNhquwQUNoFqDicNK5E1yA8yiqIgEAggGo3q5jvJlkVREUPvMpkMDg8Pu5bx1IqT9NAkLs+ifj5OW8wYtnNu9BofVwZXM1wuF6ampjpa1qZcLiObzXYkXHO5HOLxOGKxmO31O9XzNjU1hcXFRSiKMjDn0uv1IhqNYnx83PbarOpLmUQigd3d3ba93nbaV38O8j1fJBKJaPd9WX4BlWEZD3F8dEu4/i0AfyB8/oeMsY8YY7/HGJPOyGaMfZkxdpMxdnNvb09WhRhyypUq1g4yyBbKUAULV+NNBfGoyUJNVwn7uTB/UycGaxvM0AqatmEQsfU2jeWaiGxkMRbFM+eN+beNmZ4QwpEN4rTevn5ObOOHbm6url9xSRuuu7Hzuh3G7Mlc2GZiexJxqxPf2vAEMW3cNpxR9WinQ0Ek6INTDRXmHOBVoFoAygcAr+jG3LzNTcqFbRi21X7sbPMyUFgHKlmcZIbJ+8oYg9vtxvj4OKan5WvsWs2FK5VK2N7exu7ubtdtMxN1gzY39CgYvV79HpfoTR/WczpMf2/qOqgej8e0Djd8x6iUy2VkMpmOfk/pdBrb29tIp9PS5WDM/qYYY5idne1rFmY7iPPzR0ZGWtYXx5ZMJrG3t4dkMtn1pXGOEsp9HIyOjiISiejuA7L518MyHuL4OLJwZYy5AfwKgD+qF/1zAOcAXAewBeCfyI7jnH+Vc36Dc36jG2nXicGjVKniyV4amULd+9ZQmQ24IZS3LkaNYaEWsrRlBGnTsZLQW17vVF5eC7Hl0Ef/2rm9NoU7mxxkuz2J7c3nizV5SLU9hpMn1rH6vtDVAxDwenDt7BT8HmH+aHkXqOy3HENbdPodVs0DuXu1BE0nEPGhZdjCXCcmJrCwsGA65039JxvHkydPsLbWvTV6jQ9/Rs/koC/XYgcz+/s9LivP+jBgfOAedHw+H65evYpgMKiVyYR3t6/x7e1tfPjhh6YeRtnft8PhwPT0tJbEZ9BwOBx48cUXMT09bfkyyxjdANQ80G+//Tb297v73WgMWR/0vye32y31/pPHlWiXbnhcvwjgPc75DgBwznc45xXOeRXAvwDwWhf6IIaQcoVjK5ZDrlgW/HyaH7BWqeHGBNfCU6HzoGmJgeqHGb2VDU+d4E1Va3BDJmFtUy9n1SzERu8uE9rX91uvZ8cDqKlzXrfH0LfWheDhVB+oIQh68QuqVfix5DyCC28JDONUjVDHL9oo+xrxuh2YnxiB2yXMQyofAOWEzqaOt1V72woPNpwXXgIqaaCcNPw+hhurrIzDkLQjHA5r81wB8wcXmbiJxWKIxWJds8VOwhiZjSeB4xqTlado0M7zoNnTDowxuFwueDyelktQiZ7Xo445lUrh2bNnqFarpsLK2IfD4dBCcQcpTFhFzTAsZjqWnS/Zi41yuYzt7W3bYdOdMmjnzIjf74ff77f0sA7z3xvRP7ohXH8bQpgwY2xG2PdrAG51oQ9iCClXqljbr4cKC6JC005auCigCRct9Ba6cNeGttHPCTWGuNbarwtEg7hThSyXlIuCrck+TVAJ3Qq218J862HQutBeQ79CA9oXHjeOUxW3Ytf19nTnRhizOG5Rtot21EVrQ/TqrEGjiqFcFPv1MoUx+D1uLEyMwO0SElaUd4HKgf6ctbutdXTEbbXNcgIoH+IkIXpI1OtIzMY76F/+4XAYY2Nj2mezzMLiPvXz4eEh4vF4Vx6wWx1vlvl2WBmEudB2zvmgIvvbGvS/NaA2R1Odm2m8Boyi6yjeV85rWWCTySS2trZQrVZNE3AZ72EOhwNTU1PHnknYDEVRMDs7q1uHGrAX8aJmGM7lcke6b8n+fgf578VIKBRCMBiUvswYhr8jYnA4knBljAUAfA7AfxCK/1+MsY8ZYx8B+FkA/6ej9EEML4VyBbc2Yohni9L9TSGvgjADak5A0VuqHiMNlZU1aFJPdjwT93B5PfEAY/itGMLb6ic4k45DZq8YBmwM1211rGifWG78imAm420OnK6VXTkziReWpzEeCeiXw8l9COTvtbCsD4hmFzeB/OqxmdJrjEu4DIPHNRKJQJ0eYvbAYhTmKul0Wkv8ctRkJ7I+ZKGUJ+WhyhiqexzXiVW/g3ieW82/G/S/NaAmGNTlWMRrXgzZ7cY4OOdYW1vTkhEZMRN5qmf4ypUriEQiR7ajl0SjUZw5c8bSK2ycblAul7G1taWt6dopgxLyf1SM31nGn8M2HqL/dLyOKwBwzjMAxgxl/+WRLCJOBPlSBZlCBeVKVf9AUvcwMn2BztNaK+WNOZc6Dyk072GTl070zmqtiIKx8VmzgRuONdvmgH6NVcFOoX1pG00PZEavZot6knE2on6N67ca2tGFBxvaF7zA+vBlCOVMaL+2dX5uDOfmxhpL4fAywAtAeV8IFTbYbWdbZns727I2ywmgHMNJweohc1jeXiuKApfLhdHRUaRSKd06fWahu+p+dV2/RCKBQCBga4mKdjB6rof9IcrO9dJvZN70QT3Prc7foNotYvRuGsVBt651zjn29/eRyWRM96v9iX0DtVDh0dHRjjIg95NAIIDx8XGsrKzYOmfqftUTHYvFujKHdxjC7DvBeE5PwpiI7tOtrMIEoSNbLCOVF7LoaZrCIFrVLWMIbK1QJ664Wtb4IPTYCA/lwufa1M1GO+KcVa6FzBrCj7VuhFKmb79xLBqhuILNavtcDBEW7DBm/TWGLzfaMJwVrhet4rloakcUMsL5bRa8whJFRs82b8x3ZWBwORRcXJjA+TnhfRUv18Rh+RCopLQhiL8TW9tHFa2yNiupEyVcrcLShilxjNPpxMTEhGXGUxFxbKVSCbFYDOVyucVR9rF6kB+G82mGMdRatu84kImXQTzPJ00gWF0PVuV2qFar2NvbM11r2cqbpigKRkZGBl64BoNBjI+Pt/QMyu7TiUQCh4e9m7YyLN7KdsKlh2E8RP8h4Ur0hP1kAZsHQjp9Q7irRl09NcJgeVM9XaitjfuYLnSYGcpVc4zhvmZtqJ+5fp96P9WHM+v7ZGgywdJmbiwwHsubyzhXbWG6fdxgi1mfWjVmbKHeDms0Mhr240tvXMGNi3NYmo42KpV2gMTXgErCvLPjopwASidrjqvIMIYuArUHVXG+mNlDl6ysWCxia2ura8tL2O13mBmk8RhDKYeNQRbadjGGwHdjTJxzPHnypO3kaS6XCz6fDz6fDw6Ho/UBx0gwGMT09DQUpb1HZ8YY4vE4er304zD8PdkV/MMwFuJ4IOFK9IRUroiDdKH2QX3DZvQqqts6LyG0Yxo0PKMNT6vRQ2moh+Z2OLi2vE3TTVHwlErb17ZVDyt0ZTr7uVar0bPmaTUcw7kgIMVyfd+10OmGHWrrTBiP6MnWRLqxnHPBfsO5EkxTS1m9r7Dfg/mJEbxx9QwiQV8tTJjzmpe1tAHk7gKVfKMR47m0s10/H03ea6vtVm2WYkDp9KwTPSxf9oqi2Ar1lSUkKRaL2N3d7fq6iGIfVklshpFBs98scmDQ7DzJdFt4c84Rj8eRy+Us6xjx+XwIhUJD4TEUPa4qdq/ZTCZzpDmusn5kc/KHAZnnn/72CbuQcCV6QipXQixTaNZFpjcqrnkJuShQ1HJNC4nSTyIUReFrEDfMUN7Qnnpx29jJhepG0SrJTqzZVxeVOiHVqNdkH28cr4pNGOxgvLFdP03CSeHaZ112YkFwi1mQme5EyWl4mBkcioLJaBDnZkfx8sU5hHzuhu2lHaD4FCisArwgCFbVPsHuVtvdCg8Wt8sxoLjfOFdDTquspsPy4KIoCvx+f5OHRZbp1LhdLBaxv7/ftVBhUTTJPE/D8EAtw+w8GvcdJ8MaMTAo568bdOvFDOccqVRKmpjJag67KlyHAb/fj2g0Kr0vtXr5kslkkEqlOu5bFqkwLH8vIq3u72afCUKlu5ktCKJOMl+qe1xFodXwEIplus+ih9AQLmsV2quVtbjXydqS7W8O1WU124R441bhxa3g0NvMtHjh+gaH7UbVqtLqXDjvrZo1nCCPy4n5iTD+97/yOq6dnULA66pXqAI8DyT/GsjftWGdnRH0iipQyQAOH4b9lmd80z+sX+6MMWloYKvxqEtuqEtLdMuWYT6XZrQKyRsE8TVMD+AnZd6zijGzcDfI5/PSF0pWf2PqWrPDdk6N47Gyn3OOWCyGYDB4pD6H7RzJsDuGQbg/EYPJcD/FEQNLrlBGOlfSezVV0ae7H1l403SbggeSca26MRuutB2dx1NWbsMOVfA13Uxbt8/Fz6zRPpOU60Ql48Jw5TZpVW3Yb7BGm1cr/xrhGA0HMDcexhdfvYizM6MIeN2NL51yorb8TfFpzatp2mcb20bbZSHkpuMzu144UC0D5QOATQKOk3PLM0scMwwPN4qiIBKJNCVjMb6NFzP8qmWlUgnxeLwnyZmG5fy1op2Mp8TpxHjNH+Xaz+fzSCQSqFarLftTUfvz+/1dybTbLxRFwdjYGA4PD6Vh0WbjLJfLKBblSwO2y7AnDTsp91nieDg5T3HEQFFbDqekiYiaQJIIEM3LyPVlQpFebDXaYBCEnkxQaj9aCzmdTcbtuuBm4iGsdfuSXMXCYZIlgSAOnQvhug2hLutLO6burZb11bBbf3rFMfH6PkVh8LhcWJgYwXNLU/jCa5fgczvhUNdsreaB8h6Q+QlQ2gIqQhbJppcGRkGpH29TnaZ2WojWJhEsKy/X5ro6o839nDCG5WFAURSEQiHdHFfZA7TMw1WpVJBKpY68jqux35NEK+9Pqzq9wmxJlGH4HZiFWw+63XY5yjiKxSKSyaTpPEyrUHWv13tkT2Q/YYwhGo0im802CVdxrMalh8rlMsrlMiqVChwOx5GvG9l5HYZrUfYy0rhPZRjGQ/QfEq5ETyiUKsgVymiIK4k4EcNSbbws1MJquaFMhrkrUdKqSect44nbo1Wos2iyNDy6SdAzndDXHSO4VLXDmKHfeh1eb5sB8LqcmIwG8StvXMGbzy9hbnwELqdhKnzyPwPZj4HMuwDMvF523v62qnOUNobn7TOhx8xjYdw+KmZrWpr1PawM0sOg7EF7GM/xMNpsRjfGUS6XUSgUpHPwT0PWbhXxepaNsVwuY2trC+Pj4/D5fEfuaxix+puXvdAiCCMkXImekMqVcKhmFYYY8gqDV1XirWwqF/ZZhZla1YXciylvx8wOE++hWGj0ctZhhnbknuJmmxpituF1ZcL4GufV3OtrFMFqm+rnsN+DEb8Hy7NjODsdxcxYGFfPTGAiEoDbJcxBrCSA7Ae1f4X1midT69JwzpixTG9XY9vM4230yprUMetf3K6WgMIm4JkFMIJhplVY3zB/0ctCF43l4v50Oo10Ot2xt8aOaBrWh0Oz+ZjGc3ucdqkMS5i27JwO29+b2UugXrwQAuzNWT/JGM9rtVpFsVi0DKlup72TgNWYTtpYie5AwpXoCZlCGclcUdMWosdVFFtqKTMTHtB/uTGZQFPbNIodYa8onMU9TFK3YaNcDNbcmAwuRYGiAApjQtIp3qSz9MdK+mv67jYT5ibldusBAANcQkIcp0PBVDSIqWgQn3xuES8sz2B6NASv21n70uC81kY1B5S2gcw7QP5BbY5r07jQqC++pGgaNjcXmtp+4ziMdSXC13S7BBR3gWoBw04rsTWMX/RGgWr20Gv8nMlkkMvljhRmOIznyw5m4XfH7Xkd5pcEJ8FzaPb777YQNwujHiTvf69odY7L5XLH59nqfA7ruRxWu4njg4Qr0TtU116zdmrelnpgm+uboWtH8p0g7ZPDtHHTPhkAzuB1OnBpYRSzo0GE/R74PE5pv42OZMVWX14ywWfRlrjfoorb6cCVpUkwMLicCs5MReHzOOFyOKAoDIrCDGOvZw+O/SGQu10TrTCbW2jny9iG/Uduo1vHDCYn7Yu+0/EUi8WuJTs5yZwEwTUInERvl5FujM8sEsQsfJboHKv5oYPMsNpNDAYkXIke0aZ3EDAIOaMAs/AiSo7VPKzMzAtr7EPw3nJD4iQAo0EvoiEvzs9GMRMNIhr0YioSQMDrgtvlgNPBOtRGNg+y3bZ1RYeiYDTsA8DgUBhCfg+cigJFkXxxFFZryZdyHwO5O7WETKg0fgc6D6qkf1veULPfuc3ttto/ecjCaYfhIYAxBrfbDUXRz5822m/1+ajeIatw62E7n0ZkIeXdPHfdsM24PchY2XkSwl17cW20ClfvZl+DQKtruddjHYa/IyOt7u/DOCai95BwJXqDVLAaykxDe40fLYSuzsPa2Mcg7mstdMVy9VbpUBicDgf8HicWJ0ewMBHCJy7N4uJcFBMjfnhdzpNzY9VCfEsAL9e8rIUHQH4FSH23FmbLVU+rLJT3CILVKiTbaruj9ocbq1C7YfqiVxQFHo9Ht46rWVbhXmVvbSVaTwKy0EK1/LhEg1kClkFMxiLzFBqXbBo2jH9nvQ7fNf5+h/GcdUo3lhqyatdYNmy0mu8+jGMieg8JV6LvWEToNm5Yhjpi1LEYFsxRz7Yr0b1Msi1+sLIDAGZGg1iaGsHf+JlLODs1grGwD4wxyJyTww8HeK7mXS08BFLfByqx2tI3vIoO3cmDw5CbrzLMcwRFHA4HZmZm4PV6AViLgV7PibOaJzZM51TETnjwoIxtkOc8DtN5tEurueS96kfW17CdOyusrpVui7CTlONAxrDP2yV6CwlXovdwQJ/dljeX18N6a9tqXeFhVn1r21Su6wTamqtCueG9pHY4k3junA4Ffo8TNy5M4/mlCZybiWB5KoKgzw2nw7AsjHZsGagmAJRsePdM9pse1gPFxVETqdUCUE3XQoArcaC0C1QOgUoSKB8CXPCyyrye3QgPplDhjhh2z0W1WkUikUCpVNLKjF4gtUylF14Ls/JhPrdA6zEc5xhl4YHAYD+kDoONR6FX18Nxh8/2k35mKB/WNVxbYXZvIAgREq5Ej+AAbyznopszqukfQVTq5qKqe6EJ2YZ3VCw3fLYTWgpV3zaLG8aAEb8H09EAXr80ixeXJ3FmcsRQj6MmUEs1YYcKwItAZa/+udo0Dum5sVHUtKNlUybhz7JjqmmgmqllBy49Bcq7QHEL7YnHboXvtvi9HbkvnDjMws+GRXRVq1WkUimdcLVabqQXiV1O6vxWoPVD7HGMa5jX9DxpYeVm4cHdHtMgXoe9wu5LNqfz6FOMZPfHYcNMpJ6ETMlEbyHhSvQUq9uObJ9ZWK+sTPdR3ZCJFCFm2Cw8mAEIeFz40mvn8LmXl3B2agRK002TA8gDxftA5SlQfA8o7wM822jclni2IQ77Iuy0zgz9tYnZeR8YxLEON0YhN6xCq1qtIp/Po1Kp6Lysdubb9WrNyX49zPcLO8mnjgOZR/24bbJClkxoUG21gygQgP6G4p9GxPG7XC5MT09rUyQ6acfohTwJc4eH3X6iv5BwJXrCiN+N8ZAX+6l8vaQhrHSeVp3g0ntCpeuyMhjWV1Ur6+tqApVxLYeTrk0GTaw5HQpCPjd+81OX8dK5SUxFAvW1WVm9ThEoPwEqW0DpEVDZB6pJoHoI8DyakxaZbQ+aJ7KDvoy/j076Omp4cKc2nAD6Pf+zH8jertsRlEfFmDil1/31GzNv/HGPR/a7NQrDQcLONTJM2Ik0aBe3241AICA9P63OU7lcPnHLWpndPxirZVPv5No5SXOFxWvMbHrIMP+NEb2FhCvRE/xuJ0I+V1246sWGLGy4ySMmE1XGcq4Xuk3zaBtat3EsA7ggfhkDIgEP5sdDePPqHKaiAYR87nr9CoASUNkFSg+B8iOgeBvgRQBlwbQ+iMpeCrqO++qDULbatt0vThwn/UvdjnB0OBxNy+l00oeZiBp2Bk2Ay4TMsDyA93Ku9SBw1OvE6XTC5/N1FOJZKpVQKBTa7vM4KZVKqFarbR3DGIOiKEcOFT4p16LRezws9wLi+CHhSvQEt0uBz+1oXdGI6g1tVQY0hag2VTGGsBrDjlELD/7CK2fxxRvLODsV0WcMriaAyjMg8/u17WoeJ1IFDXyobxfgODFjHOa5rTI6DWkNBoMIBAK9NO1EMczXyCBw0l5smNGJx9Xj8WBkZKStc6Oez2Qyib29vYH0tsuoVqvY3d1FJpOx/Jsy7nO5XPB4PEfu/yTMAR12+4njhYQr0RMCHifCfjek3jkz75laqBapMb5CWK++qtzbqvcO1vcavLMA4HE58MufOI9XL0xjOhKAwuo3Ul4GKhtA8UOg9ACoxoFqSbC1Qw/oIHsl+x2W3O75O4o3mbkA/1nA4cNJ4CSEUnHOUSgUUK1WpSGKdgSC3++Hz3f036mVIBnmczyo4bjDek6tBMow0s11XNXrKxgMolgsah5Uq7bUvsvlsi5J2zBQLBZRqVRajk8kFAphZGSkazb0ao3YXmM1Z/wkzCEneg8JV6IneN1OBDzOujPPIDC5+MkQygvVwWohbnlzlmGhsk6kNua36oWTy+FA2O/GjQvTODM5gqAYHsxzQOkxULpfCxHmBWguuyYxamNbGFtH270WdydRiIvbzAE4o4DixknC+LA5TA8wnHMtOZNKu7a7XC64XK4j2SATrMP6QGiXQRGwJ4Vhv0a6ZT9jDD6fDy6XSzdntVVExTCFClerVZTLZS2pXDv3CJ/Ph1AodGQbjN7KYbv+ZN9bZvsJQgYJV6InhLwuRIOeJs1pvCVZRQVzyX61PbHcqk1W99YaZk9gdiyIKwujePn8NLwuIaS5mqyFB2f/BOBZgBveBJ+GsNqBwM5JbuMXwdurPiwMa9hYuVzGzs4O8vm8dL9Voo5uCUqjOBWF7LBmaxZpNZ5+j6kfv9Nu0yphzKDabYU4JrPtTmCMYWRkBMlkEul0Wnpfkt2vkskk3O7heKmYz+eRTCal56rV9RyJRDA2NtZx37Kswt34vR0XZlmth/FviugvnWe2IAgLIgEPJkd8ojsVDa+dREVoN+PmelwsN3hOdcdruwVPHW94ZButcVyaH8UvvLIMl0Ophwfzmme1+D6Q+/O6aC3r+xLbt7stjo3z9rZl58zOdrt96drhNvoSbYTJtqFOW+dP/P2pG7L2ISmX2KCFCvtx0hjWL3gxVFi2zyisxAc0r9eL2dnZrjzsmoVIDut5VbEK/xwUj+swnOOTmDBG/Nsy2+603enpaYTD4aZrrJV3slqtYn9/3/RF1qBQKBSQSqW08Yi0un+EQiFEo9GO+zb+nobxhaWImd3DOh6if5DHlegJAY8TI776HFcbYayqOBXDiNXbFzOKFAihwpJQVA7olsxh9XhhxgEwhpDPhfmxEC7OjULRsjFVgep+bdmb0sO6aBWFINrbVguM4tV0u3mMunPW1E+LOpZ9GbY7ad/40Nv1UGE77dsQr8wBKB7AGam734lBgHOOXC6nCxVWy828Fuq20+lEJBKBw9FB8rcOGMYHKTs293tc7cwHHCSswlwH2e5+wxhDNBrVzTsXxZXVi5JqtYpkMgm/39/RGqf9olAoIJ1OW47FTKAHAgGEw+GO+7YS/sN6HQ6r3cTxQh5XoicEfbVQYbu3JQYIotVe/bb3McDpYHj98iwuz48iGvQKDuECkPs6UH4AcCF7cF+cEnY66VadLrVv+oVzFDt7cB4coZpoPQUctwetHcxChc1EgrjdTeFq55wN03m1w3GNR/RSGX8OA7KlO4bJfhX199Dt3wFjDBMTEwgGg6b7zVDvB7lcriu29Ip4PI719XWpiDQL2VXLJyYmMDs723Hfqvg33g+H8RoE9NehcUziT4IwQsKV6AlRNVQYEDxpgjet6aYkeNGM20ZPpLbJhRILL58wadapMLxxZQ6LkyONN+bVfC1zcOkOUDlo7osLdqhjadoW7RZs4OI2l2xz/ZDNQnN1JrWoY9mXpLzJU9nKBtkxsD7GKgzY7By1bF9s26SOZwrwLuCkYPwyH9Yv90qlgv39fcukLLJQOMYY3G43JiYmjpSYCUCTd9fsAWpYvQKtHmr7fe3IwhuNcwQH7XqWiVWRVp7EQaRXoaaKouDMmTMYHR01rSMTJZxzVCoVbG5uIpvNdsWWXiGGCovIMuWqOBwOhEIhhEKhIy/fJZtCMWz3J1n2eNlc3WEbF9E/SLgSPcHjciDgcSHgccIhLo5qFGGieJOFpupEr0zc8EZYsKG8Ub/2T1EYPE4HlqZGEA0K4Ug8A1T2gMohwPOCoIO+DamgM9QxCkNZ6KuZyDIVxGK7FnXMRKasL90xRrtb2QDJMYbfITfU0Z0nw3jNzlHL9sW2IekHNW+rq/OEGIOG8eFoGOdllstlFAoFZDIZlMvl1gcYcLlciEajR/a4yh4Arc5npVJBsVgcGqFiHM8gPBianbtBfUlgJwJg0Gw+LhhjCIfDCAaDpstUyZLvMMZsvcgaBPL5vFS4Wt03FEVBOBzWMi5363oZxBc9diBhShwVW8KVMfZ7jLFdxtgtoWyUMfZ1xtiD+s9ovZwxxv4XxthDxthHjLGXe2U8Mdi4nQouzkYQ9rlgens13Lu4qHGFn2bHc6ENbiw34HM7MT7iw/x4uL7GbJ3KGlB6F+AVwxF2vhSEOqb3YVk7bbZtp45pdcuz16Ibk53cRp12bOjo2BZ1GADXOOCZtnH8cDHMX/qJREJ7SJUlZ2qF1+vF4uJi3zORplIp7O/v97XPbjII14zVHL1BsM8Ow/iyqJ+MjY3h/Pnzpi+WZOesXC5jbW0NmUym1+YdiUQigY2NjbYEo9PpxOLiIvz+7iYHHKa/GTucpLEQvcWux/VfAvgFQ9k/BvBNzvkFAN+sfwaALwK4UP/3ZQD//OhmEsOI06FgfiwAn8dZ84oaPYPM4D0DdF5FVt/JOBprsRrCTxtzVMUlb/TlaieTET+eOzMBh2K44Zd3gOI9AFULT6QNb2grT6dZWxJb5W2JxzSfC/GHtd2S7bZs0LlUDTbJ7BPOl8x7a7YtbdPGttiOdxHwXwAxOBweHmJ7e7ujY10uFwKBQFdChVXsPoSWy+Wh8riqDJu9xHATjUZx9uxZKErj8bKVp71arSKVSuHg4ACHh4cDd81yzpFKpZBMJqUeVyu8Xi9eeumlI2UUPukM2u+bGGxsCVfO+fcAHBqKfxXAv6pv/ysA/4VQ/vu8xjsAIoyxmS7YSgwZDoVhcsQHr8shSCOu1xn6uFJhndTGMja1Mi5oJ4lAUbdl5XUCHhcmR/xQVNHKOcAL9bVbDw3iCfrjbYW3SuwRRWIrkWVr22BbUyiuRDC2I/TsbsPQr9aUcdtwvprOmcW2aZsttjlHLY20C3BFT1So8EkgnU4jFot1FOoWCAQQCoXg9Xp1D8ad0k6opxriPGzIksUcZ4Kmk8RJG083CAaDmJqaavn3afzbK5VKiMfjODg4sDjqeOCcIx6Ptz29QVEUeDwezMzMmIZPt2tHO+WDzqBOESAGm6N8809xzrfq29sApurbcwDWhXob9TLilOF2OnBhNoqQ16WLouXMJKpW1FFiYV2TMEM9cXqj2q7hYF2Bz+PEaNAr3CSrQGUXqCZqAlYnoo2I8cgmdXSD6uUXidA2M/bbj/6HAKbUBKtrDHCOnKilcIb1IUXl4OAAW1u1rw5jcptWY5ufnz9SZk4jsjmgZp/V+W3DgtW5PO75rbJzPWzX9bDZq2KWhKxbjI6O4ty5czrhajcL88bGBlZWVrpqTzeoVCp49OiR5g22e858Ph8ikQgmJye7uszPsF57IlbZkU/C+Ije0ZXkTJxzqyd+KYyxLzPGbjLGbu7t7XXDDGLAcDoYFsaC8HucEEUhE71kGvV99XoM4n59GLAq1hjMQ4VFeN0LF/a5MTcWgiIq4GpSL1pNPZEGz6HME9nK02nqHRQNbWO7XU+kcVszsUNvaCtPrnG7n6HCihcIvXji1m8d5qzC1WpVmyO2trYmrdNKUC0vL2NpaelIdjDGMD4+rlu2Q+Z5NX6OxWJ49uzZ0JzzQVvz0SqT7SDP15P9vofdU9TLeboOhwM+nw9XrlzB+Pg4APn5kvW7sbGBBw8eDNzfWLVaxd27d7G3t2f7WuWc4+zZs7h27VrX7LD6+xm0c2YHcTw0d5ywy1GE644aAlz/uVsv3wQgrj8xXy/TwTn/Kuf8Buf8xsTExBHMIAYVh8Iw4ncj4HXB63LWCmUht00htQAHFwSueBzk4qapXN3FtU2nQ4HX4xRuihyopgFetCkqhX505UKHVuGzLcWxze1OBZ2s36PYIDtnrYSy1fnrZqgwcwP+c4DzaMsPDDLD9qBSqVRwcHCARCJhueyFbFyMMTidToyPj1sut2EXNcOn2p/swc/4WU3ONCzn3cqLQaHC9jFbBmfYxYKKcSmoo45JURS4XC6cPXsW0WjUdngr5xzZbBbxeByJRALFYvFIdnSLUqmETCaDvb09LXmUnXPkcDgwMTGBubm5rgqxbv++jgPjPXfYx0P0l6MI168B+J369u8A+FOh/O/Wswu/DiAhhBQTpwiHomA05MVM1I+piGx+h37eKge0+ayM6+uBqTrLIBS1Ii4UaQqmIZc4FyVbnXqoME83jjmKqGwpGNG83YmXVdaOcbtb4tFsW/i92d42G69VeaeiXPECI588cfNbrbxWgxxuyTlHsVjE7du3EY/HdfvMHurE8TgcDoyMjGBmZgbdeNHpdrvhdDp1fRm3jeGN+/v7ePLkyUCfZxUzD5dYPghjGPSH1UEQ+71Adg13c5kSh8OB1157DfPz89IwYWOEg7qvXC4jnU7j9u3bSCaTA3HOk8kk1tfXsbu7i0wmI33RJTuXHo8Hi4uLuHChe8kB1b6N3wPtzNUfRGTjGYTfPTGY2F0O5w8A/AjAJcbYBmPs7wP4fwL4HGPsAYCfr38GgL8A8BjAQwD/AsB/03WriaFiZjSApcmwVICJt1otPJjp6+j3G+CGctk8V65Wkt0IubzYrPpRkOjPrrbTr/u89PfT5rZMjBvLWYcDcgQB9wTgmwcc3V2CYNAZ1IeXcrmMZDKJmzdv6pKvtJqHqY4nFArh1Vdf1YX3doq63qQ650z2ICjapz5IlctlZDIZ3Lp1C4eHxlyFg4UsnNH4cHsc14pRxJid82QyiXw+33f7jLQ6X4PyAqBden0dMMYQCoUwNzeH5eVlKIpi2af4OZvN4oc//CGePXs2EFm819fX8c4776BcLkvvEzLR6PF4cOPGDUxPT3d12S7Z70rs/7jPlV2sXpKI4pwgZDhbVwE4579tsuuzkrocwD84ilHEyWIy7MPcmBCy2RRei4awbLrvGjyW6vFC9mFZORePt3P/M/MUymy26xltalPiIe20bWM7drY76cu4LZ53K++qbLvTftvtyzUGeOYAxVdL0nSCOW4hYpfDw0NsbGzg8PBQCwE0PnCZPcwoigK/34+lpaWuJTgJhUJalk8rb4VRUBWLRTx+/BgjIyOIRCJND+SDjFUiql7hcDgQCAS0RD3Gh1Kzc5/L5QYmVNSI2XU7bMjuHd0aD2MMLpcL4+PjOHv2LJ4+fYpKxbhOutyeSqWCnZ0d7O3tYXJyElNTU5bH9Qo1dHl/fx+bm5vamtOtzpE6x/f8+fMYGRnpSvZztV+g+Xc1bKIVkHvc7bwQIAjApnAliKNwbjqCYrkKhvsGRyiv5c1pEq1cp2WbxK1MtApCjcNwDOewvqVz3fFHEn29FpX9FIwtw3ftbB/DuQxeASKvwd4bi5OLGIZ5XA8Aqg137tzB+++/r/Oi2X1wdrlciEQiuHr1ale8F4wxzM7OYnt7u+W5MT4cFotF/OQnP8Hk5CRmZma6mim0U8ySB7XyzvTjmvB6vZifn9fmE5t52oz2ptPpgfC4yhBDM0/ag3W3x3P27FmEQiH84Ac/0IRrq2tTXdN1ZWUFiqJgamqq7/cwNVR1a2sLm5ubWhZ0wNpbzDmH1+vF+Pg4PvGJT8DhcHTNJqt+ZZ+HCbNoBoKQcbLdEcRAMBb2Yn4siPGwD26HmCJfVrv+VlHblEhOuy8WmWGz3ReS3b5vSjRZ3zhqfxa/K+ttq+N7ROASEH6ljx0OBqJQTafT+PjjjxGLxVAqlY7NplKphFu3buHu3btYXV3V7TNLwmTklVdewWuvvQaPx9MV74WiKFheXsbk5KQt0SqiZkb+8MMP8YMf/GAgvBycc2xubuLRo0d48OABKpVKSw9yvx4K1XUsrUJqZd6X/f39gVh6yGyu3bA+VFv9DnpxLXs8HoyOjuJnfuZnMD8/b2mL8Rq5d+8efvKTn2BnZ6fv6ydXKhWk02n86Z/+KW7dutW03+xlEQBcvXoVb775Zt+F9jDRKvpj0Oe+E8cLCVei57idDoR8bpybiSDgdaHmUVU9ZbzmEeXQyjhH3UtqFEMNL562lzfKG6VmHjwTuPBP9OB1GtLKTbYNY2j0xQWPZotts3astnmHfaFF+1Zj7cTDaty2076uLwfgnqrNb3VFcdoQRUmhUMDa2hru3r2LBw8e4PDwEOVyua/2FItFpNNp3L17Fzs7O00etFYPdowxeL1eLCwsYG5urqthucFgECMjI4hGo7bChEUqlQq2t7fx6NEjrK2tIZ1Od8WmdlDDluPxONbW1nD79m2srq7i8PBQC2mUHdNvnE4nAoEAXC6X5n0ySwokks/nkclkkEgkTMfTD06aV9XqWu/FOB0OBzweDy5evIipqSl4vV7LlyfGua77+/u4c+cOEokEKpVKz69hzrmW/fzBgwd49uyZ9AWKzHY1i/D8/DwWFhb6et0M8zUqE7H9fsFGDBcUKkz0haDPhc9dX0QslUMsnat7QOtCFaiv3wrd/2Es4bV6tfqN41XRKsK5fl1X06873rTRmcgyNmgUwa22O+2L29judfvdHNNRzqXTA4RvAO4ZQPHgNJPNZnHv3j08fvwYc3NzeOutt/DCCy/okhv16qFAffhIp9N49uwZvvWtbzXNb7MT+udyuTAxMYFz587hzJkzXbOPMQa3243JyUlcvHgR7777LkqlUlvnY2dnB/F4HC6XC5/+9Kdx/vx5Xfu9wPjQnk6n8fjxY3zwwQd4//33dXNCZedXDHHtF263G+Pj4/D7/XA6nSgWi7b6LxaLODg4wJMnT3D16tWuzRPsBJprdzRcLhdeeeUVHB4eYmtrC1tbW5YCVDzfiUQCf/Inf4JQKKQlVOvV70O1qVAo4N69e/j2t7+NdDpt+8WJy+XCCy+8gEuXLmFhYaH1AacYs98h/Z0RdiDhSvQFv8eJT12dw08fbGEnnkE80wj9EW9VTPhCY+DyOuKG5PtP9BUy4ae0srGsly90JZpsuOGGn3bqtlnH9nnigBIEpn8d8C3aPWiosfMAVyqVsLm5iT/90z/F22+/jZmZGVy7dg2XLl3SkhN1m3K5jLt37+K9997D/fv3pUlZ7DygjI6O4ld/9VcxPj7eCzMxPz+PN998Ex9//HGTN9rOuS0Wi/joo4+QSCRw5swZ/NzP/RwCgUBXs4jK+tzZ2cHDhw/x/vvvIxaLIZVKaeHgZnMvxXLZUjm95syZMyiVSlq4uNX5Vfdtb2/jpz/9KS5cuKDNkT0OzM4lCdr2eO655xAMBvGHf/iHyOfzWpZeI0YPXKFQwDe/+U08fvwYX/jCFzQPfrepVCrI5/P467/+azx69Ai7u7u2RWsoFMLk5CQ+/elPIxrtT7TPMF9/ZqHCwzoeor+QcCX6gkNREPa7cX4mgoNkDj9d2a7vMfEOyjyranXGhXxOBk8r9OIX4DamV3bqLeQGYdUnr2S/vbpN+83GZNaX2XmR1RFeN1ieP0O5cwTwzADeOcDRG0E2aLT6klcfCIrFIorFIgqFArLZLBRFQS6XQyQS0bLjGtc1bRfOOUqlEg4PD7G/v4/bt2/jyZMn2N/ft/WAZawzOTmJM2fOYG5uDh5Pb7znPp8PExMTmJmZwe7uri4ksNXcV5VsNoutrS2USiVMTExgamoKkUgEkUgETqfzyMlZ1MymuVwOqVQKu7u72Nvbw/r6OjY2NpDL5XQvBuyEPR/Hw+HU1BRisZgmXK1sUPdlMhnNQzcxMYFwONwXW1txksMYeymGRkZGMD8/j+XlZWxsbCAWi9nqu1qtYnd3F5xzfPTRRzh37hyi0Sh8Pl9XbOWcI5PJIB6PY3t7G48fP8be3l7TyyDZcYwxOBwOTE1N4dy5cxgdHe3piyuRk3L9nZRxEP2DhCvRV964PIeQ142bK9sSyanfricE1mAAwLi2vCdXhQtrFqxcaEMqxIz0OzzYUui10a+lUBbbNxOM4raZEBfFKzMpb7NtqzrMQuibbfuWgPD12txW1r1MjsOCWWiouC+bzSKbzWJjYwNvv/02pqen8fzzz+Oll15CJBJBMBi05QGRUa1WkUwm8f777+ODDz7A6uqqzrPXrnh98cUXceXKFYyNjfXswcbtdmNkZAQvv/yylkBKZo/V+qecc8TjcSQSCWxubuLatWs4e/Ysrl+/jlAoZBriavYgLPu8s7ODzc1NrKys4IMPPmiZbddupuR+srS0hFQqhZs3b9q2KZPJIJ/P47333sPVq1dx9epVbV+/E9/IvK7HYcswo0YjfOYzn8F3v/tdTbjauR5TqZQ29eCXf/mXcfHiRSwuNiJr2v0diL+/arWK7e1t3L17F++++y62trZ0nlYr0coYg8fjweXLl/Hqq6/q5vD2ArNlZIbR+2r2dzVs4yD6DwlXoq/MjwcBcHz62jw+frKHw1ReH/LLeSMWWNg0hel+yHYJmIjWtrHTjqROU1GH7XRyLDeJq9aVyeoY93fYv906LbMXG8tZzcM6+hYw9vM4DfnmZOtIWj0AyB4EisUinj17hoODA7zzzjsIBoMYHR3F3NwcotEoxsfHMTk5CZ/PB7/frzu2UCgglUohmUxib28Pz549w7Nnz7C9vY1sNot8Pq89GJqt22m2FqbP58OlS5dw/fp13YNpr3A4HHjjjTfgcrmQyWSwsbGhPbSK589or4pYls/n8fHHH+P+/fv4zne+g4WFBUxMTGB2dhbRaBTBYBBjY2Nwu91Nnlh16Y10Oo1MJoNnz55p60em02md11y0QXYO7SS9Mo6j18zMzCCbzeLy5ct4/Pixbi1f1SbZea1UKvjhD3+I1dVV3LlzB5///Ofh9/v7Gjpsde2eNHo9LqfTiYsXLwKoXRNf//rXm8Jxze4NatjwX/3VX+Htt9/G4uIirl+/jpmZGczMzLRty6NHj7C9vY2nT5/i3r17yGQyyOVyqFarll5WoHaenE4nQqEQ/ubf/JtYWFjA2NhY2za0i0y0GsuHhVZ/S5RRmDCDhCvRV9xOB6JBL25cmMZ+Mot8qYxsXl2ug4Mz6MKA1W3BzaoPD+ZCSLH2kzfmthr2S5F6As08hAZvoG5T5ik0Kz+Cp1UV97Lw3abto3hDW9gi+2IxO09tncsW4xDLFTcQegHwnQXcvZkLOWgYvX+yB2rjXEbZm/pSqYRSqYR0Oo1kMolUKoVMJoNwOKxl3PV4PE2huqVSCdlsVguv29vbw8HBQVPon9FWmS3idjgcxvj4OK5du4bx8fG+rJHKGEMwGMTc3Byee+45xONx5HK5piWEzLzZ4hhU8ZrP55FMJjVvbDweRygUgs/nw8jICFwul/Rh7eDgALlcDrlcDvv7+0gkElrIovgQZ9frMigCi7FaMqzR0VFcvXoVOzs7KJfLqFarliHMqv2ZTAY7OzuoVqu4efMmRkdHMTIygpGREXg8HrhcLrjd7p6P9aSJ1uOY68xYLVP49PQ0qtUqHj16hJ2dHaRSKakIM9qmRnaoIfJOpxM7OzvY3t6G1+uFy+WyvG+oL4EKhQLW19exv7+PnZ0d7O3ttVxnVrTH4XBgZmYGi4uLWFxcRDgc7tvLFLOXleq+YcBsDMc9pYEYDki4En0n7PfgizeW8WQ3gXS+iKd5wzqTTTdfLmgZNZxUf5OuFcmFlq5cCm9TPB5BaGnbVoJO3TarIwjNpn4k49HKJO2Y2iAbh41tYxtN4t2kH+lYWtgAAI4AMPXrQOAC4DgdmYRloatm4lVWX0ahUECxWMT+/n6TR84qVM6qfTt1xLK5uTlcuHABn/70p/v+0HL27FlMTk5qD9KJROLIbe7u7mJ3dxcPHz5sW2jaFaGy8GIzgWXm3ewX4+PjeOutt/DBBx8gm83qvMdm15y6L5lMIpFI4NGjR5ifn8fS0hKuXLmC0dFRbU6x+CBs9mDfCbLfhcowPly3ug76cW1MTk5iZGQE+XweP/jBD7CysmJqi+xeVyqVtPnPLpcL4XBYa9MqmduTJ08Qj8dxcHCAQqGgiwxR25f9ro02eL1ePP/88/jEJz6BiYmJvmS9tnOfPQmYRbcQhAoJV6LvKAzwu534Oz97Fa9emMb/+hcfYDuWRr5YNmQVtsa4nxvKmLCDmzUmE3jyii2ssVHH6F1su50Oj+UAmC4eu8P+7dCF82S3TvBazdsaeQ1QTkdCJsDa86Pu8/l8mJmZwbNnz1rOiTQe3249O+FrVm/SvV4vrly5grfeequry960gxr293f+zt/Be++9h/feew/r6+u2HpxaeWfMyu16G+w+sMq2zeocx4Ouoijwer349V//ddy+fRvf/va3NfFqtM+4rX7mnGN7exv7+/v4+OOPoSgKHA4HFEWBz+dDMBjEuXPn8Pzzz2NpaakrdrfzAmjQsbpm+o3b7caLL76IcDiMK1eu4K//+q9RKBRsJRsT95XLZcTjcaTTae16MKNUKqFardpeE9bYvxru/8UvfhFzc3MYHx/v+zkcxKiKdrHze21Vjzi9kHAl+k7trSYwFvJheTqCT19bwMdPdrF9mMbWYRqAqvHqbzsBfXhw3XunCVUOXdImtbZ6nFbHjCavoNETKQtpteMdNDvezPsobpt5K9v0gHLDWOy0b7cv3Re/0aPcwbbdMTFnzcM68kpNuDoCADv5c1tFWoken8+Hc+fOYWxsDMlkEtvb26ZrEtoJjevUrlaomXiff/55TE9P69aZ7SeMMSiKgrGxMZw7dw7VahXVahWxWAyZTKapbjt0+nBp5k01ehUdDgcikYgW4i0u72MWfnscqP1OT09r4dD3799HNpu1tRyRWlYul1Eul5HL5XSi0uPxwO/3IxAI6NbV7bb9wyoWAHvXbr/Gpt6npqam4HA4sLu7i7W1NS1kvh37KpWK7t7WiXeylZianJzE/Pw8FhcXsbCwgFAodKRM7O1iZZ9V+PAwYDVnnyCMkHAljg2304G5sSD+7mefw1/e9OGDxzvYjWdQ5Q1hChhEK2+IUqbuYeoUWHE+a/1GWC+3Fq+8d4JRO1RV2mbiWD2mS+KuG6Ky077sCOKOzmvttw6HHxj/AhD5BBC4iNOOGB4KNOZtvvTSS/B4PNjd3cX3v/99PH36FPl83nRtQrvJZ8xCO822ZcepD1kXL17E1atX8eqrrx77gwpjtbmYly9fxtmzZ7X1aJ8+fWoq+NXjjJ9b7WtVR7RJ3Sceq5arc/rOnTuHixcv4sKFC1hZWUGpVDLt+7jF68jICC5evIjR0VFtCZJ0Oq2zzUy0ykI61bJisYhKpYJYLNZWlEErWiWQOe7rthOOY36rGWNjY4hEIhgfH8e3vvUt3L59uymzrxGj/ca/q1bjaqcuYwwulwtXr17F9evXddmtjxPZGIYltLadex1BGCHhShwrCmPwu134hRvLeP3yLN56fhHf/OAJVrfjeLIjn2emBr0y1IUp0++DvgiAKo+O66Ze75dZ9T8cXzjHBnMAgUvAyI2al3XklVMVHmyGWWiuytjYGKanp3HhwgWsra1hfX0dH374ITY3N7XMrsaHhFbhpK3COcU2jYIDqHlZp6en8dZbb2FmZmZg1ucUcbvd+PznP48XX3wRm5ub+PM//3Ok02ldaKs4Lrthu1b7zM6l8XcTiUQwNzeH8+fPY2pqCrOzswgGg3C73eCcQ1GUlrYcN16vFzMzM/id3/kdrK6u4r333sPt27ebQodVzK7Jfjy0t4puGEYGyRMP1MLII5EIvvjFL+LNN9/Exx9/jHfffRebm5vSudCtvKOtplLYudcxxrC4uIhLly7hypUrWFxc7EvCOCuMtg9S6Hc72PmbGpaxEP2HhCtxrNTenANBrxtORcGleaBQquDcdBTrewnsJ3NI54tIZgsol6solmtZiEsV4W2s4NFUBSrTdnHVT9eCHnsiu+GVHDRvaC+8umC1tVjV35prFHCOAM4IELwE+C8A3kXAETx14cEqrR5eRBwOBzweD9xuN+bm5rTMm9PT04jH4zg8PEQ8Htfmfol9qO3L+pV9thJefr8fPp8P4+PjWFxcxMzMDBYWFjTBNWgwxhAIBDAxMQGXy4VPfvKT2N3dxcHBATY3N1EqlaRZSI3eglZJksT+jCiKApfLhVAoBK/Xi0AgoCUiUsV/JBJBNBrVMhWbeRrNPGyM1dahtJoX2AtUcT06OopKpYJKpYJAIICDgwMcHBwgHo9rmYftJPnqpfCyCls+CVgln+oXjNVC3tX7gRquPzs7q2UtV5erEbEzp95Yt9X1MzExgVAohHA4jOXlZczPz2Nubg7BYLAvSZjMsHq5NUyc9L8noveQcCUGBq/bicWJMBYnwiiUyoil8vjg8Q4291N4tB1DNl9CMlPAViyFbL5UCykGmoWQiUByOhQoTTfHenhuL8Wj0Tbb4g4AHIBqM4P5WO1uM2FMum2hbbv9MDSP06wdq/bFdphSW9qGOWpzWYPPAf5ztTLf0qkVqyJWX/BWnoaxsTGMjY3hwoUL2N3dxcbGBm7duoX79+8jk8loWTbNHlzbfWhSHxJVgTI1NYUXX3wRFy5cwMTEhI2RHj+hUAihUAgLCwt48uQJVldX8e1vfxvJZFJbr1bmdbV6mDaKMNkxjNXClkOhEM6cOYPx8XHMzMzg8uXL8Pv9tsS+7AWHEcZqYeX9XBdV7NvlcmF2dhYzMzO4evUqVldX8dFHH+HevXvIZrMoFoum16RdD1y7qAl+2hHzRi/3INHJeBwOx7GNRxWuCwsLSKfTWFlZwUcffYRnz55hZ2dHux7M7lN2RZBYRz03DocDy8vLOHPmDM6ePYulpaW+zmOVof7u7NqhJisbxOuRMdb2+XQ6nQM5FuL4IOFKDCRupwMTET/eeuEMKpUqytUqOK99KVXUbfEA8w8aXrcTPo8LDoU1qnHT6nI00WUXM/FnBwWY+grgWQJcU20ea9MkeUFvaBLDMhigOKH5yJmrJmCZUEZodBoqNj4+jmg0isuXL6NUKiEej2N9fR3r6+s4ODjA3t4ednd3dRk+ZX3L+lUUBZOTk5idncX09DTOnz+P8fFxhEIhuFyuY38Q7JT5+XlMT0/j5ZdfRiwWw+HhIW7duoVYLIZEIoH19XVd/VYJRxhjmJ2dRSAQgNvtxvj4OMLhMEZHRzExMYFAIIBQKKQ9iDocDtM1YGXIXjQYvZeKomjr9R43IyMjuHbtGi5evIhMJoNYLIa9vT3s7Ozg8PBQW9s2m80in89Lx9MNPvGJT+DKlSuWCYKMhEIhTE9PD+S1/dprr+Hy5ctDNx6Xy4VIJIKXXnoJ165dQ7FYRDwex9OnT7G/v4/NzU3s7+8jk8kgnU5rx7Wa4qBeM/Pz8wgGgwgEArh48SLGxsYwMTGhvchxOp19j0QwMjExgV/6pV9CNpu1nPMrwhjD2bNn4fMN3lSaubk5/O7v/m5bx6gv1whCZfDusgSBeugQY/C5++VhO0J4ay/DdB0hwBkFXMPhoSL6h1mInx1PqPoWX/Xcud1uOJ1OhMNhZDIZJJNJJJNJ5HI5ZLNZALXkN+pyFWrfHo9He8hzOp3weDzweDwIh8OIRqMYGRnB1NQUAoHAQIijo6COUQ23DgQCYIwhk8kgm83i4sVaorBKpYJkMik9PhQK6coikQg8Ho+2z+fzIRAIIBwOw+12tzWnLp/PI5FI6ASc2YsNsXwQvDOil9nlcsHj8cDr9SIYDGJ0dBSZTAapVErzwpZKJWk7amboo3j0I5EI/H5/U6ZjK9xuNzwez7GfRxmdjEf9HRzneNRrQr2nVCoVLaxdDZVX553n83mkUqmWY1T/1txuN8bGxrS2p6enteiKQfLwud1uTE5Oolwut/VixufzHUsURSu8Xi8WFhbaPm4QXwgRxwddDQQB2BeSwBFEa5viuF+eUGIo6US0muH3++H3+zE/P68rj8fj2NvbAwCkUikkEgnNc8NYLVFQIBCA3++Hx+NBJBJpEmcnEfV8yQRSoVDA2tpaU7nX68X8/HzPHopzuRz29vZ0nhmzebaD8mAuQw0hHhkZwcjISNM12WtOmnfnpIzH4XAgEAggEAg07eOcY3Nzs6VXeWRkBH6/f2jOicvlQjQaPW4zuoYqxAniKJBwJYhBFIgcaKzhM4D2EcdOPzJKhsNh7SGPc64ly1FRvXXiv9OO2+3G2bNnm8qP69zQ74Q46TDGMDMz09IrSfcoghh+SLgSRCfe0F6FCot9cQ5k7wKlQ8D5UF8H8o8WhYa+zA6zIZLFeuFXAVek9TFE1+lHdlVFUY41k+Yw0kkCkm5jlSnWOCfU6XTS75gYeo57PipBEP2BhCtxyuG6Hz0JFda22w0VrgKZdwHmrSUpktpstL+F7UbxajdUWVcsfPCdI+HaZ2jpAKIVrRLUqNvqvFISrgRBEMQwQMKVON3Y9TIehbYzEdfhAAob0LLp2vXgaj+O4C22FMdCf9WsraEQ3UOWYKcXy4IQw4udpExAzUs1PT099ImzCIIgiNMBCVeC6GuocAsPb1NfvLuhyFY26NZlNXpmTTzIx7BYPVGDRCphhh2Pq8pxrtlJEARBEO1AwpUgAPuiEuiCaDWG4HZRHFu12SosWNOj7YhXgiAGAc65NDmNWWg5Y4xChAmCIIihgr61CAJAT8OFm0SimXfDjg1HqdPusUexk+gn7azxR5xMCoUCYrGYbjkcwNz7Gg6HaWkKgiAIYqggjytBNCUhknkoDcLANHRWVsfM0yoec5T2Je2YhgcbbJCGBxvbsKhDDARmcxiJ00OpVEI2m7XtdXU6nXC73XS9EARBEEMDCVfilFMXYnZFpWk4b7uitdvi2E5fkvY6Cg8m0TpokMeVKJVKyGQyWsiw7GWGuO10OikpE0EQBDFUtAwVZoz9HmNslzF2Syj7fzPG7jHGPmKM/QljLFIvX2KM5RhjH9T//a89tJ0gjk7L531DBalzoluhub2o06P2SScdC0aBqn5WlzYh79npJZ/PIx6Pm2YUNm6rocJ0zRAEQRDDgp05rv8SwC8Yyr4O4Brn/AUAKwD+W2HfI8759fq/r3THTILoE5qHkktCcYUybZtr1fV1hHLTY4zlYney0FwbXl1Zm2bbujZl2yY2iOeD6CtGkSF60sjreroRPa5GxOtD/enxeBAIBPpqI0EQBEEchZahwpzz7zHGlgxlfy18fAfA3+iyXQTRP4zCURcO3MZ8T7Nt3TE2t6X9GLcNdUzbZABzAMxV+6n7s68A1XL9X96iLws7iWND9a7RHFeiVCohnU5LhavM6+p2u0m4EgRBEENFN+a4/lcA/lD4fJYx9j6AJID/G+f8+13ogyB6hEyEWYXdshZ1ukW3QnwBMCcQuAwEXwL8V4DQywAYwCtAaQ9IfwRk7gF7f3oUg4k+IxOpJFpPL4lEAk+ePGma4yoillOoMEEQBDFsHEm4Msb+rwDKAP51vWgLwCLn/IAx9gqA/8gYe45znpQc+2UAXwaAxcXFo5hBEN1DmqlX3DaG7Lbhoezluqxm7bgnAc88MPbLgGcGcI4Big9gDOBVAJNA8EXAPVUTuPEfAYWNNvoi72u/kXlZgZpoJY/r6YNzju3tbW1+q2y/ek2I10YoFMLExARdLwRBEMTQ0LFwZYz9PQBfAvBZXv+25JwXABTq2+8yxh4BuAjgpvF4zvlXAXwVAG7cuEFPv8QxYpyv2YZg7GZ4cEvR3GJb1w6r/XPPAoEXgOhnasJUhCmAM1D755kGHAEgvwmUDoBKrkX7xHEh87JaedmIkw3nHDs7O0gmG++HzRIyqTgcDgQCAYyMjPTFRoIgCILoBnaSMzXBGPsFAP8XAL/COc8K5ROMMUd9exnABQCPu2EoQfQEMd/QiYIBrnEg8ilg8m8AcLSo7gECzwHjXwDGPtcXC4mjY8wqTJw+qtUqVlZWsL29bau+w+HA2bNnSbQSBEEQQ4ed5XD+AMCPAFxijG0wxv4+gH8GIATg64Zlbz4N4CPG2AcA/hjAVzjnh70xnSB6ARfEbAfbWjMmocJ2ti0z/ZpsG9thTiB0HfAsAIq/9bAZq3lg/ReA4DXtVOj60iWxcgCOcLMXl+g5xuywxOmlUqmgWCxic3MTsVjM1jGKomB+fh6hUKjH1hEEQRBEd7GTVfi3JcX/m0ndfw/g3x/VKILoG6z+7mYQQoWN7RwlVFhxAL6LNa+r4oJtPLNAOV3LQMxLzXNZVdsUJ+AMo6Unl+g6svmKxOmkWCwiHo9jb28P6XRaWscYQu5wODAzM4NgMNgvMwmCIAiiK5C7hDjFMID5aiKtS80NTtixAngX6+KyDZgHcEZrYcO5h0BJyKumTp3lqJ0z90RNwBIEcSw8fvwY3/3udxGPx1Eul6V1jC84HA4HLl++jGg02g8TCYIgCKJr0FMncXphrJZll/kaZZ0kSJIdaxYqbNaP9qNLHl4AWoKmdmCsfhiT96V2wRRA8aLDafLEEaFETKcbzjnW1tbw6NEjPH36FJVKRXdNmF0fo6OjmJubQygUgsvVpRd2BEEQBNEnSLgSpxgFcIzWlofphpAEOgsPbjck2K5orpYAXkFb8GrtmGq+vm3SF3MAjmDtJ9F3zJa+IUF78imXyygWi1hdXcX6+joOD2tpJMRrwuwaGB0dxZkzZ+Dz+eB00tc/QRAEMVzQNxdxilEAzznAMcQhc2r4rpFqGcg/ra/jOmO/vWoBKMWA1B0AVfO+lEAtiZPDRuInoquI4sSOl404WRweHmJlZQVf+9rXkEqldPta/f7PnTuHN998Ew4HvXAiCIIghg8SrsTpRX3Ic00B3ktAfqXmZdTosge0Y68uNxxm4rHV1SsByZ8AnnkgcAm2yawAqY8AVOU2qF04AkDoSs1bTfQVUajKyomTCeccd+7cwePHj3Hr1i3kcrmWLy7Ua8TpdOLatWs4c+YMJWUiCIIghhYSrgThHAc8y0D+IWpexh6HCrcliA2CUS1nYpscei3LAVSA7CpQ2ARKh7WES1bChnOAF4HsIyBz3zCnVegHqC2v44oA3nlAcZu3SfQMVaSI4lVWRgw3nHOUSiUUi0Vks1msrKzg0aNHePToUZNIlQlYxhgcDgd8Ph8uXryIyclJeDyevo+DIAiCILoBCVeC8JyrZclNfMOww44A6KVIMGmb2ajDq0D5ADj8FlDcBxb+G+vsybwI5J4Ch98EEj8xab/ecfg6MPJqbakd4liQLYlDy+ScTDY3N/HgwQO888472N7eRqlUsvxdi2Wcc0QiEczPz+PTn/40iVaCIAhiqCHhShBKEHDNAoFXgPwjoLQDU0+nbtsihFfmDW0rVNjMcyv2I/O0Grbz60AlCzi8gP8C4F0AfOfrWYMrQDlZ88zmnwIH3wIyD+WhzZwDUGoe1shrQPhlaw8u0TNUjyoJ1JMJ5xz7+/vY3d3Fo0eP8OTJE8RiMRwcHJgueSNrQ1EURKNRPP/883jppZfgcrnomiEIgiCGGhKuBKG4AYQB3xWgkgLKhzUPpJlgNBOYxs8dhwqbtA1hn2kosXgcrwnTShaI/xAoJ4BSvLafKTXhWjoE0neB7EMg8U49ZNjEBsUNeGcA//lamDDRd4yilRIyDT/VahWlUgmFQkELC15fX8fm5iZu376NZ8+eoVgsavWNHlXZteBwOOByubCwsIDl5WWcO3cOikJLVxEEQRDDDQlXggAA5gbCXwCqRaAcB4rrMA8D7lYIsZUw7WLbvFSbt5q5X//MoBemJnNZjbgngbnfAQIXAeeIDRuIbmM2r1GE5rgOF7lcDhsbG7h//z62t7fx+PFjJBIJnXfV7PcsCxUHgEAggMnJSfztv/23EQ6HaekbgiAI4kRA32YEAQBggOIFgq/Xsgzv/+ua57WSRT0TkkUoL0ySJRm8odJQ4V4mdhK2Na2qCtU2+x37DBB6AYi8CjjDFCZMEG1wcHCATCaDbDaLRCKBTCaDRCKB/f195HI55HI5pNNp5PN5ZDIZVCqt1182m9967tw5nD9/HpcuXUIoFILD4SCvPEEQBHEiIOFKEEBdiDkB93xtjVLvRaCwCvAdoJprPf+0SZxazD/tRDgeJdOxbp5ti3HothVAcQGuUSB0HQi/ALinSbQeM0ZPG4ULHz/VahXFYhHFYhGlUgmlUgnVahXVahWVSgW7u7tIpVJIp9OIxWJIp9M4PDzEzs4OCoWCrT6s5jYzxuDz+RAKhXD+/HlcvHgRFy5coHmtBEEQxImChCtB6HDWlseZ+ge1LMPpHwOZ99Hb8OB+0kb/igfwzQOz/zsg8jrgniDROgDIwoVJvB4v5XIZT58+xZMnT7C7u4vt7W2kUinNw9oNrOY1ezweXL16Fa+//jquXr1KocEEQRDEiYS+3QhChLGatlPDhj1nAdcMkL0NFJ8B1XytntFDqQvFlW2Lx7Tpbe0kVNhoQzt9KZ5a9uHRT9Xms4avA64REq0DjCheScD2H845isUiHj9+jCdPnqBQKKBcLpuG/LbzosHK0zo2NoYzZ87g5ZdfxsTEBMbHxyk0mCAIgjixkHAlCCNa2PA04IwAlTgADig+oLABVLMAr2f55AaRaCkqjyhYxX7sbttuH7V1Xl3hemjwi8DIDcB/tpaUiR6EBwqZ8CGxcnwwxuB2u5HNZnF4eGia7Vesb0e8GkUrYwwejwderxfBYBCzs7M4d+4crly5Ar/fT55WgiAI4kRD33IEYQXzAKFP17yvpX1g/w+A9AdAdfu4LesuzAm4xoCJz9c8rNFPC/tIEA0aJFIHC6fTicXFRfj9fgD634/Z78rO71AWEjw9PY0LFy7gjTfeQCQSQSAQOILlBEEQBDE8kHAlCCu0B0dXTdiN/U0g/CmgtFcLH87eAYq7AC+09oAaQ3mB3oUK2+mLuQDfGSB4DfAvAaFrtTE6Q4axE8fJ0tISOOdwOp3Y3t5GOp029dbRXNfjQfW4RqNRRKNRxGIxANZhvnaJRqOYnJzE0tISzp49i3A4jHA4jJGREUq+RBAEQZwqSLgShB2YAjAv4D1TE3eVRE34MRfg2qp5YyvZ2hzYShpAdfBChRVP7Z8jBDj8gCMIBC4AwecB3xIQvExidQAZHR1FtVpFqVRCMBhEPB7XllZRs9gSxwtjDA6HA8FgEOFwWBOuVuHCsjacTifcbje8Xq/2c3JyEpOTk1heXsaZM2fg9Xrhcrl6PiaCIAiCGDRIuBJEuyiB2r/ILBD5PFDNAJkPgcxtIP8UyHxUX/+19VqMtuGoJYA6Cu5JwLcIhF8DglcA73wtU7AKidaBZGpqClNTU3juuedQKpWQTCbx4Ycf4u7du9je3sbOzo5Wl7xvx8vY2BhmZmbw9OlTAA3BKpvTavzsdrsRDocxPz+PM2fOYGpqCmfPnsXIyAgcDkffx0IQBEEQgwYJV4JoF6M4UHyA/xrgXa57XDO18OFyrJbMKb9Z2y7tAeUEwMuNY9vxvLZKAKXa4hwBnFHAdxbwTNXEqe8s4PDWPa5hwOGrbcvGQwwUorhxOp0YGRnByy+/jMuXLyOfzyMWi+HJkyfY2dnB48ePkclkjtHa083Y2Bimp6e1z6LH1el0IhAIIBAIwOfzIRqNIhgMIhAIYGJiAmNjY/B6vfB4PPB4PJrHlbIEEwRBEEQNEq4EcVSYoyYWMdIocx8A5TjgmgBck7Xt0h5QigPVAsDzQLVUE7HVeqgnL9c9tTBf2oa5AMUFwAGA1UOYfYDiqCVYcoRrmZBdkVr4r1sVrmdqdYmhRlEUKIqizaWsVCqYmJiAx+NBNBqF2+1GPB5HNBql+Y/HQCgUwuTkJM6cOQPGGBRFgdvtBgA4HA6dcI1EIppwHR8f135nBEEQBEHIYdy4JMYxcOPGDX7z5s3jNoMgegvndY9sCihs1ufFpmveWA6gdAjkn+imp+o/cMARqYX8Ovy1tWYVX02gOkcAZ7i+dA0J1NPM7u4uSqUSvF4vwuEwiaE+UiwWkcvlsLOzo3lMp6am6AUCQRAEQQBgjL3LOb/R6fHkcSWIfqJ46p7RAMDPArxS+wfUva8FwwHGF0vOusdVqQtUpd6mo/bvyBNhiWEnGo1q8ydpXc/+ooYDLywsaB5XgiAIgiC6Az3VEES/YAxaeC/IC0b0BvKwHh9qKDe9MCAIgiCI7kOvgwmCIAiCIAiCIIiBhoQrQRAEQRAEQRAEMdBQPBNBdBMh2dnxpz2zjzYzlpLIEARBEARBEAMICVeCMIPzzsXnEAlYBoCrgrWNLOMkdgmCIAiCIIh+0TJUmDH2e4yxXcbYLaHsf2CMbTLGPqj/+0Vh33/LGHvIGLvPGPtCrwwnCKKLDMCyWARBEARBEARhhh2P678E8M8A/L6h/J9yzv8nsYAxdhXA3wLwHIBZAN9gjF3kXF3vgyAGBLve1Lqga1vWDYnHlaEz+9r10tI6lgRBEARBEMRRaClcOeffY4wt2WzvVwH8W855AcAqY+whgNcA/KhzEwmic7iVqOK8FuZqR8R2EDZs2feAwFEP+a2fBztoorVFfa1dmJ8LsQ5BEARBEARBmHGUrML/kDH2UT2UOFovmwOwLtTZqJcRxODRhmCyI+k457p/duv141+rsbVbn0KLCYIgCIIgiH7SaXKmfw7gf0TtGfZ/BPBPAPxX7TTAGPsygC8DwOLiYodmEEQdC48o57x1SGwLj6od72k7HtZh8Maa0epcqq8DzLyyYlInMxFMocUEQRAEQRCESEfClXO+o24zxv4FgP9U/7gJYEGoOl8vk7XxVQBfBYAbN24M71M80X9azTuViFCu7WrhTTTZ38qD2sm+duq0i1H4WfUhE4lqfek+tdxElPLagU37dYK21knzfpPQYhKyBEEQBEEQp5uOhCtjbIZzvlX/+GsA1IzDXwPwbxhj/zNqyZkuAPjJka0kiD5gFEvtilhpeZtJoICjJXPSxKHd+oxJ7WYSASmKR9PyRgUSmwRBEARBEETXaClcGWN/AOAzAMYZYxsA/nsAn2GMXUftOfUJgP8aADjntxlj/w7AHQBlAP+AMgoT3cDUY9jC+yo7rh3hedTjW4UwdxtROALd81RaCVzb5RJPq87LKuwzJm3ixmMM+wmCIAiCIIiTDRuEuXY3btzgN2/ePG4ziAHDTDSazrE0EbFHEZ+Wn81Ecxfs6BZG4WomZNspt9Nmqzpm4rNt+0zaIQiCIAiCIAYLxti7nPMbnR7faXImgjgWVE9ep+uPymgpKI1eU8NnuyHGxn39eGnEDSG7xs9m5Vahwsb5r2Z1rPrVluEhCIIgCIIgCBuQcCUGD4kwNIqkVt5MO95NKxHZSrgabbArSPstXLuFWUiwWR1jfelnQB82LOmjSSzXCrX9NKeWIAiCIAjidEDClRgcDIJQFawM9uaPHkWsWh5rIVStjrMTjtyPUGErD6mx3MqravTIGuedmo3EjoiV9Sm1o1Yotc0qEzJBEARBEAQx3JBwJQYKUfx0W4DYFYntiFuzcs55SyHdydI7rZAtg2MmPtX6Zl5SY5stl6jhXLecjVW/du03K2t0SZ5WgiAIgiCI0wAJV2JgMA0PbqooeEDrYqlTz6qd4zppu91jZZ+tys3EWje9jp1mDDbz4Mo8pGbC2c68XLPwYBKzBEEQBEEQJw8SrsRgIAg9Mw+fVtW43UZyJLE/O3NUOxWmsv12QpPtilcrYSbzuqrl4rZRgGqh2QaBWa1WpeWmglNt06JOq99Rk/gU2tSKADDhJQaJVYIgCIIgiJMLCVdiIBEFkJXEaSVyRdoNwbUrItudy2pH1NoRdkZEMSqbk2p1vJWHs5OQX7t0GlJshEQrQRAEQRDEyYaEKzEQqDLKuGRKy+MsRKOVZ0/MRttOH1aCshOhalVmJZCt5qoaP3ciDM0SHsnaMJZpv8sWbRvHYTmXtUWbkk7URu0eQRAEQRAEQQwwynEbQBC9wLYHzkY9u15ds/mWdsqM+2QeWNk/u22biWErjpIkyur4dsRzt20iCIIgCIIghhPyuBIDxVHDUTsRNnaPsDM301jXbG1SO6LO6O00Oy/i/FOzeq3aaGXLUTH7vVr9vtvNMEwQBEEQBEGcXEi4EgOBug6oToDVf7aSiq2EYKvMuFZrkBrrtxKiZplz7WC2vqndY82OMwpbsU4r0Wh2nFWZ9HfZhjg1Q1rT7HgSuARBEARBECcKEq7EYCBmo62jiR+jUDTu1zVjHtZr3Ke1w1hj2zhf00RIyryhYviurFw2T9ROX+3QruC0U25H9OrqCOfTrE6745PaaVa3rZYJgiAIgiCIYYCEKzGw9CosVPNsSkSxrl6tsva5lYg1K2+VSMm4RI2s3VZ05Bm1IXLbFq1dgkKCCYIgCIIgCBESrsTAYBYa3BSeq4onznXZiGUC0syDKopXqbeVc0tPLCTl7cxdbZXpt5O5vu2IVVnZUcKGu7avVmhpM2vskNpLYcIEQRAEQRAnDxKuxOCgCiSYrzNqFLCtQnztCkbjPq3tY1pWpRsex3aSG5mFDYvHtOvVtStepfVrBc112iwnCIIgCIIgTgYkXIlTicxD2+SNrX2o/UCzGO40A7IsmZPaXjexIxZl+5oEZK3QVn27+wmCIAiCIAiiHUi4EgOJmJSJ2y2vY8f7aqf/VsvYmNWR2dDKlqMuAySjHY+raf3ajrbatNuv7TKDDa3qEwRBEARBECcPEq7E4CJ4O3Vi1eAFNZvfCnR3DmkvxGUvaStUuLazZXmnwrVb9lgdQxAEQRAEQZxcSLgSQ02nIoahdRIo47xaO15SK++ulfCVlrcTOtyFRE7tlB/1eFuomZ8JgiAIgiCIUw8JV2I4EL2vdUzXPTVkG9ahem6FxE66fSZeQat1YUVb2gkPNqsndGBZX0Y3MhHb3d+2eK3tlJeb7LM6jiAIgiAIgjg9kHAlhhYzbydkorRWAVwigNV9nYqjQQohHmThauvYRoWWbRAEQRAEQRCnBxKuBGED6XI8hn0qZgmbzOgkeVQrOg6hthKqjUqt61j10ZZFBEEQBEEQBEHClThBmM0T5Y0K7XtiDfXseALNwpZt1e8z7Xo42xKvR/C8EgRBEARBEIQICVfiZGMmVgHr+a6SurbqGdsfcGHWSWhuW8K0g/YJgiAIgiAIwggJV4LoFfU1Z1XJ1kaO4J6hk4+dJH86wrEEQRAEQRAE0SkkXInTi93wYNj0ylo106qfPnNUAVs7bJBGRBAEQRAEQZxkSLgShBV2xK0Z9VDhQROtKt0QrwRBEARBEATRD5TjNoAgCIIgCIIgCIIgrGjpcWWM/R6ALwHY5Zxfq5f9IYBL9SoRAHHO+XXG2BKAuwDu1/e9wzn/SreNJoih4CjeWoIgCIIgCIIgNOyECv9LAP8MwO+rBZzz31K3GWP/BEBCqP+Ic369S/YRBEEQBEEQBEEQp5yWwpVz/r26J7UJVsvO8psAfq7LdhEEQRAEQRAEQRAEgKPPcf0UgB3O+QOh7Cxj7H3G2HcZY586YvsEQRAEQRAEQRDEKeeoWYV/G8AfCJ+3ACxyzg8YY68A+I+Msec450njgYyxLwP4MgAsLi4e0QyCIAiCIAiCIAjipNKxx5Ux5gTw6wD+UC3jnBc45wf17XcBPAJwUXY85/yrnPMbnPMbExMTnZpBEARBEARBEARBnHCOEir88wDucc431ALG2ARjzFHfXgZwAcDjo5lIEARBEARBEARBnGZaClfG2B8A+BGAS4yxDcbY36/v+lvQhwkDwKcBfMQY+wDAHwP4Cuf8sIv2EgRBEARBEARBEKcMO1mFf9uk/O9Jyv49gH9/dLMIgiAIgiAIgiAIosZRswoTBEEQBEEQBEEQRE8h4UoQBEEQBEEQBEEMNCRcCYIgCIIgCIIgiIGGhCtBEARBEARBEAQx0JBwJQiCIAiCIAiCIAYaEq4EQRAEQRAEQRDEQEPClSAIgiAIgiAIghhoSLgSBEEQBEEQBEEQAw0JV4IgCIIgCIIgCGKgIeFKEARBEARBEARBDDQkXAmCIAiCIAiCIIiBhoQrQRAEQRAEQRAEMdCQcCUIgiAIgiAIgiAGGhKuBEEQBEEQBEEQxEBDwpUgCIIgCIIgCIIYaEi4EgRBEARBEARBEAMNCVeCIAiCIAiCIAhioCHhShAEQRD///buN+TOuo7j+PvD/mTMcDrHkG2h0Uj2ILchMVHElEJNmg9EjMoxhD3ZAwUjlk+iQKgnWlII4qwZ9kempkREMgf1xNX8k1NXuIbixv6Vf1NQrG8Prt/0tJLtPvfVzrXt/YKbc/2+1znsd8OHc+17n9/vOpIkadBsXCVJkiRJg2bjKkmSJEkaNBtXSZIkSdKg2bhKkiRJkgbNxlWSJEmSNGg2rpIkSZKkQbNxlSRJkiQNmo2rJEmSJGnQbFwlSZIkSYNm4ypJkiRJGrQjNq5JFifZkuT5JM8lubHVz0jyaJIX2uPprZ4kdyTZmeSZJCv+37+EJEmSJOnEdTSfuL4H3FxVS4GVwLokS4H1wOaqWgJsbmOAK4Al7WctcGfvs5YkSZIknTSO2LhW1d6qerIdvwnsABYCq4CN7Wkbgavb8Srg3uo8DsxNclbfE5ckSZIknRymtMc1ydnAcmArsKCq9rZT+4AF7Xgh8PLIy3a3miRJkiRJU3bUjWuSU4EHgJuq6o3Rc1VVQE3lH06yNsm2JNsOHjw4lZdKkiRJkk4iR9W4JplF17TeV1UPtvL+Q0uA2+OBVt8DLB55+aJW+w9VdVdVnV9V58+fP3/c+UuSJEmSTnBHc1fhABuAHVV128ipR4DV7Xg18PBI/fp2d+GVwOsjS4olSZIkSZqSmUfxnAuBrwLbkzzdarcA3wHuT3ID8BJwbTv3a+BKYCfwNrCmzwlLkiRJkk4u6banTngSyUHgLeBvk56LTihnYqbUH/Okvpkp9ck8qW9mSn06E5hTVWPvER1E4wqQZFtVnT/peejEYabUJ/Okvpkp9ck8qW9mSn3qI09T+jocSZIkSZKONRtXSZIkSdKgDalxvWvSE9AJx0ypT+ZJfTNT6pN5Ut/MlPo07TwNZo+rJEmSJEn/y5A+cZUkSZIk6b8MonFNcnmSvyTZmWT9pOej4UtyT5IDSZ4dqZ2R5NEkL7TH01s9Se5o+XomyYrJzVxDlGRxki1Jnk/yXJIbW91MaSxJTknyhyR/apn6Vqufk2Rry84vksxu9Y+08c52/uyJ/gIapCQzkjyV5FdtbJ40tiQvJtme5Okk21rN657GlmRukk1J/pxkR5IL+szUxBvXJDOAHwJXAEuBLyVZOtlZ6TjwY+Dyw2rrgc1VtQTY3MbQZWtJ+1kL3HmM5qjjx3vAzVW1FFgJrGvvQ2ZK43oHuLSqzgOWAZcnWQl8F7i9qj4JvArc0J5/A/Bqq9/enicd7kZgx8jYPGm6PltVy0a+psTrnqbj+8Bvqupc4Dy696veMjXxxhX4DLCzqnZV1bvAz4FVE56TBq6qfge8clh5FbCxHW8Erh6p31udx4G5Sc46JhPVcaGq9lbVk+34Tbo32oWYKY2pZeMfbTir/RRwKbCp1Q/P1KGsbQIuS5JjM1sdD5IsAr4A3N3GwTypf173NJYkpwEXAxsAqurdqnqNHjM1hMZ1IfDyyHh3q0lTtaCq9rbjfcCCdmzGdNTakrrlwFbMlKahLet8GjgAPAr8FXitqt5rTxnNzfuZaudfB+Yd0wlr6L4HfB34VxvPwzxpegr4bZInkqxtNa97Gtc5wEHgR21Lw91J5tBjpobQuEq9q+522d4yW1OS5FTgAeCmqnpj9JyZ0lRV1T+rahmwiG510bmTnZGOV0muAg5U1ROTnotOKBdV1Qq6JZvrklw8etLrnqZoJrACuLOqlgNv8cGyYGD6mRpC47oHWDwyXtRq0lTtP7TEoD0eaHUzpiNKMouuab2vqh5sZTOlaWtLpbYAF9AthZrZTo3m5v1MtfOnAX8/tjPVgF0IfDHJi3Rbqi6l20tmnjS2qtrTHg8AD9H9gc3rnsa1G9hdVVvbeBNdI9tbpobQuP4RWNLujDcbuA54ZMJz0vHpEWB1O14NPDxSv77dvWwl8PrIkgXp0F6xDcCOqrpt5JSZ0liSzE8ytx1/FPgc3d7pLcA17WmHZ+pQ1q4BHiu/aF1NVX2jqhZV1dl0/096rKq+jHnSmJLMSfKxQ8fA54Fn8bqnMVXVPuDlJJ9qpcuA5+kxUxnC+1iSK+n2bswA7qmqWyc7Iw1dkp8BlwBnAvuBbwK/BO4HPg68BFxbVa+0puQHdHchfhtYU1XbJjBtDVSSi4DfA9v5YP/YLXT7XM2UpizJp+luQjGD7o/E91fVt5N8gu4TszOAp4CvVNU7SU4BfkK3v/oV4Lqq2jWZ2WvIklwCfK2qrjJPGlfLzkNtOBP4aVXdmmQeXvc0piTL6G4gNxvYBayhXQPpIVODaFwlSZIkSfowQ1gqLEmSJEnSh7JxlSRJkiQNmo2rJEmSJGnQbFwlSZIkSYNm4ypJkiRJGjQbV0mSJEnSoNm4SpIkSZIGzcZVkiRJkjRo/wY+mRUNM9/E+wAAAABJRU5ErkJggg==\n"
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No requests installed\n"
+ ]
}
],
"source": [
@@ -713,8 +939,10 @@
"outputs": [
{
"data": {
- "text/plain": "<Figure size 1152x432 with 1 Axes>",
- "image/png": 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\n"
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
},
"metadata": {
"needs_background": "light"
@@ -745,8 +973,149 @@
"outputs": [
{
"data": {
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- "text/plain": "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_img, int64_t const _size_dst_0, int64_t const _size_dst_1, int64_t const _stride_dst_0, int64_t const _stride_dst_1, int64_t const _stride_img_0, int64_t const _stride_img_1, int64_t const _stride_img_2, double w_2)\n{\n double * RESTRICT _data_img_22 = _data_img + 2*_stride_img_2;\n for (int64_t ctr_0 = 1; ctr_0 < _size_dst_0 - 1; ctr_0 += 1)\n {\n double * RESTRICT _data_dst_00 = _data_dst + _stride_dst_0*ctr_0;\n double * RESTRICT _data_img_22_01 = _stride_img_0*ctr_0 + _stride_img_0 + _data_img_22;\n double * RESTRICT _data_img_22_0m1 = _stride_img_0*ctr_0 - _stride_img_0 + _data_img_22;\n for (int64_t ctr_1 = 1; ctr_1 < _size_dst_1 - 1; ctr_1 += 1)\n {\n _data_dst_00[_stride_dst_1*ctr_1] = ((w_2*_data_img_22_01[_stride_img_1*ctr_1] - w_2*_data_img_22_0m1[_stride_img_1*ctr_1] - 0.5*_data_img_22_01[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 - _stride_img_1] + 0.5*_data_img_22_01[_stride_img_1*ctr_1 - _stride_img_1])*(w_2*_data_img_22_01[_stride_img_1*ctr_1] - w_2*_data_img_22_0m1[_stride_img_1*ctr_1] - 0.5*_data_img_22_01[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 - _stride_img_1] + 0.5*_data_img_22_01[_stride_img_1*ctr_1 - _stride_img_1]));\n }\n }\n}",
- "text/html": "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span> <span class=\"kt\">void</span> <span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_dst</span><span class=\"p\">,</span> <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"k\">const</span> <span class=\"n\">_data_img</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_size_dst_0</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_size_dst_1</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_img_0</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_img_2</span><span class=\"p\">,</span> <span class=\"kt\">double</span> <span class=\"n\">w_2</span><span class=\"p\">)</span>\n<span class=\"p\">{</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_img_22</span> <span class=\"o\">=</span> <span class=\"n\">_data_img</span> <span class=\"o\">+</span> <span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">_stride_img_2</span><span class=\"p\">;</span>\n <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int64_t</span> <span class=\"n\">ctr_0</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\"><</span> <span class=\"n\">_size_dst_0</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"p\">{</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_dst_00</span> <span class=\"o\">=</span> <span class=\"n\">_data_dst</span> <span class=\"o\">+</span> <span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_img_22_01</span> <span class=\"o\">=</span> <span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_0</span> <span class=\"o\">+</span> <span class=\"n\">_data_img_22</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_img_22_0m1</span> <span class=\"o\">=</span> <span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_0</span> <span class=\"o\">+</span> <span class=\"n\">_data_img_22</span><span class=\"p\">;</span>\n <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int64_t</span> <span class=\"n\">ctr_1</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\"><</span> <span class=\"n\">_size_dst_1</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"p\">{</span>\n <span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">((</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">])</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]));</span>\n <span class=\"p\">}</span>\n <span class=\"p\">}</span>\n<span class=\"p\">}</span>\n</pre></div>\n"
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+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_2</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">_stride_img_2</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">pow</span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">],</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">);</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "</pre></div>\n"
+ ],
+ "text/plain": [
+ "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_img, int64_t const _size_dst_0, int64_t const _size_dst_1, int64_t const _stride_dst_0, int64_t const _stride_dst_1, int64_t const _stride_img_0, int64_t const _stride_img_1, int64_t const _stride_img_2, double w_2)\n",
+ "{\n",
+ " double * RESTRICT _data_img_22 = _data_img + 2*_stride_img_2;\n",
+ " for (int64_t ctr_0 = 1; ctr_0 < _size_dst_0 - 1; ctr_0 += 1)\n",
+ " {\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + _stride_dst_0*ctr_0;\n",
+ " double * RESTRICT _data_img_22_01 = _stride_img_0*ctr_0 + _stride_img_0 + _data_img_22;\n",
+ " double * RESTRICT _data_img_22_0m1 = _stride_img_0*ctr_0 - _stride_img_0 + _data_img_22;\n",
+ " for (int64_t ctr_1 = 1; ctr_1 < _size_dst_1 - 1; ctr_1 += 1)\n",
+ " {\n",
+ " _data_dst_00[_stride_dst_1*ctr_1] = pow(w_2*-1.0*_data_img_22_0m1[_stride_img_1*ctr_1] + w_2*_data_img_22_01[_stride_img_1*ctr_1] - 0.5*_data_img_22_01[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 - _stride_img_1] + 0.5*_data_img_22_01[_stride_img_1*ctr_1 - _stride_img_1], 2);\n",
+ " }\n",
+ " }\n",
+ "}"
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -804,16 +1281,132 @@
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- "text/plain": "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_img, int64_t const _size_dst_0, int64_t const _size_dst_1, int64_t const _stride_dst_0, int64_t const _stride_dst_1, int64_t const _stride_img_0, int64_t const _stride_img_1, int64_t const _stride_img_2, double w_2)\n{\n #pragma omp parallel num_threads(2)\n {\n double * RESTRICT _data_img_22 = _data_img + 2*_stride_img_2;\n #pragma omp for schedule(static)\n for (int64_t ctr_0 = 1; ctr_0 < _size_dst_0 - 1; ctr_0 += 1)\n {\n double * RESTRICT _data_dst_00 = _data_dst + _stride_dst_0*ctr_0;\n double * RESTRICT _data_img_22_01 = _stride_img_0*ctr_0 + _stride_img_0 + _data_img_22;\n double * RESTRICT _data_img_22_0m1 = _stride_img_0*ctr_0 - _stride_img_0 + _data_img_22;\n for (int64_t ctr_1 = 1; ctr_1 < _size_dst_1 - 1; ctr_1 += 1)\n {\n _data_dst_00[_stride_dst_1*ctr_1] = ((w_2*_data_img_22_01[_stride_img_1*ctr_1] - w_2*_data_img_22_0m1[_stride_img_1*ctr_1] - 0.5*_data_img_22_01[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 - _stride_img_1] + 0.5*_data_img_22_01[_stride_img_1*ctr_1 - _stride_img_1])*(w_2*_data_img_22_01[_stride_img_1*ctr_1] - w_2*_data_img_22_0m1[_stride_img_1*ctr_1] - 0.5*_data_img_22_01[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 - _stride_img_1] + 0.5*_data_img_22_01[_stride_img_1*ctr_1 - _stride_img_1]));\n }\n }\n }\n}",
- "text/html": "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span> <span class=\"kt\">void</span> <span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_dst</span><span class=\"p\">,</span> <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"k\">const</span> <span class=\"n\">_data_img</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_size_dst_0</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_size_dst_1</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_img_0</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">,</span> <span class=\"kt\">int64_t</span> <span class=\"k\">const</span> <span class=\"n\">_stride_img_2</span><span class=\"p\">,</span> <span class=\"kt\">double</span> <span class=\"n\">w_2</span><span class=\"p\">)</span>\n<span class=\"p\">{</span>\n <span class=\"cp\">#pragma omp parallel num_threads(2)</span>\n <span class=\"p\">{</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_img_22</span> <span class=\"o\">=</span> <span class=\"n\">_data_img</span> <span class=\"o\">+</span> <span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">_stride_img_2</span><span class=\"p\">;</span>\n <span class=\"cp\">#pragma omp for schedule(static)</span>\n <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int64_t</span> <span class=\"n\">ctr_0</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\"><</span> <span class=\"n\">_size_dst_0</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"p\">{</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_dst_00</span> <span class=\"o\">=</span> <span class=\"n\">_data_dst</span> <span class=\"o\">+</span> <span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_img_22_01</span> <span class=\"o\">=</span> <span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_0</span> <span class=\"o\">+</span> <span class=\"n\">_data_img_22</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_img_22_0m1</span> <span class=\"o\">=</span> <span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_0</span> <span class=\"o\">+</span> <span class=\"n\">_data_img_22</span><span class=\"p\">;</span>\n <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int64_t</span> <span class=\"n\">ctr_1</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\"><</span> <span class=\"n\">_size_dst_1</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"p\">{</span>\n <span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">((</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">])</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_img_1</span><span class=\"p\">]));</span>\n <span class=\"p\">}</span>\n <span class=\"p\">}</span>\n <span class=\"p\">}</span>\n<span class=\"p\">}</span>\n</pre></div>\n"
+ "text/html": [
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_img_2</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"cp\">#pragma omp parallel num_threads(2)</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_img</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">_stride_img_2</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"cp\">#pragma omp for schedule(static)</span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_img_22_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_img_22</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">pow</span><span class=\"p\">(</span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">w_2</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">0.5</span><span class=\"o\">*</span><span class=\"n\">_data_img_22_01</span><span class=\"p\">[</span><span class=\"n\">_stride_img_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_img_1</span><span class=\"p\">],</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">);</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "</pre></div>\n"
+ ],
+ "text/plain": [
+ "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_img, int64_t const _size_dst_0, int64_t const _size_dst_1, int64_t const _stride_dst_0, int64_t const _stride_dst_1, int64_t const _stride_img_0, int64_t const _stride_img_1, int64_t const _stride_img_2, double w_2)\n",
+ "{\n",
+ " #pragma omp parallel num_threads(2)\n",
+ " {\n",
+ " double * RESTRICT _data_img_22 = _data_img + 2*_stride_img_2;\n",
+ " #pragma omp for schedule(static)\n",
+ " for (int64_t ctr_0 = 1; ctr_0 < _size_dst_0 - 1; ctr_0 += 1)\n",
+ " {\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + _stride_dst_0*ctr_0;\n",
+ " double * RESTRICT _data_img_22_01 = _stride_img_0*ctr_0 + _stride_img_0 + _data_img_22;\n",
+ " double * RESTRICT _data_img_22_0m1 = _stride_img_0*ctr_0 - _stride_img_0 + _data_img_22;\n",
+ " for (int64_t ctr_1 = 1; ctr_1 < _size_dst_1 - 1; ctr_1 += 1)\n",
+ " {\n",
+ " _data_dst_00[_stride_dst_1*ctr_1] = pow(w_2*-1.0*_data_img_22_0m1[_stride_img_1*ctr_1] + w_2*_data_img_22_01[_stride_img_1*ctr_1] - 0.5*_data_img_22_01[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 + _stride_img_1] - 0.5*_data_img_22_0m1[_stride_img_1*ctr_1 - _stride_img_1] + 0.5*_data_img_22_01[_stride_img_1*ctr_1 - _stride_img_1], 2);\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}"
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -832,7 +1425,9 @@
"outputs": [
{
"data": {
- "text/plain": "False"
+ "text/plain": [
+ "False"
+ ]
},
"execution_count": 35,
"metadata": {},
@@ -842,7 +1437,7 @@
"source": [
"loops = list(ast.atoms(ps.astnodes.LoopOverCoordinate))\n",
"l1 = loops[0]\n",
- "l1.prefix_lines.append(\"#pragma someting\")\n",
+ "l1.prefix_lines.append(\"#pragma something\")\n",
"l1.is_outermost_loop"
]
},
@@ -863,16 +1458,124 @@
"outputs": [
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"data": {
- "text/plain": "FUNC_PREFIX void kernel(double * RESTRICT const _data_I, double * RESTRICT _data_dst)\n{\n double * RESTRICT _data_I_21 = _data_I + 1;\n for (int64_t ctr_0 = 1; ctr_0 < 202; ctr_0 += 1)\n {\n double * RESTRICT _data_dst_00 = _data_dst + 601*ctr_0;\n double * RESTRICT _data_I_21_01 = _data_I_21 + 2404*ctr_0 + 2404;\n double * RESTRICT _data_I_21_0m1 = _data_I_21 + 2404*ctr_0 - 2404;\n for (int64_t ctr_1 = 1; ctr_1 < 600; ctr_1 += 1)\n {\n _data_dst_00[ctr_1] = -2.0*_data_I_21_0m1[4*ctr_1] + 2.0*_data_I_21_01[4*ctr_1] - _data_I_21_01[4*ctr_1 + 4] + _data_I_21_01[4*ctr_1 - 4] - _data_I_21_0m1[4*ctr_1 + 4] - _data_I_21_0m1[4*ctr_1 - 4];\n }\n }\n}",
- "text/html": "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span> <span class=\"kt\">void</span> <span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"k\">const</span> <span class=\"n\">_data_I</span><span class=\"p\">,</span> <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_dst</span><span class=\"p\">)</span>\n<span class=\"p\">{</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_I_21</span> <span class=\"o\">=</span> <span class=\"n\">_data_I</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">;</span>\n <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int64_t</span> <span class=\"n\">ctr_0</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\"><</span> <span class=\"mi\">202</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"p\">{</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_dst_00</span> <span class=\"o\">=</span> <span class=\"n\">_data_dst</span> <span class=\"o\">+</span> <span class=\"mi\">601</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_I_21_01</span> <span class=\"o\">=</span> <span class=\"n\">_data_I_21</span> <span class=\"o\">+</span> <span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">+</span> <span class=\"mi\">2404</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_I_21_0m1</span> <span class=\"o\">=</span> <span class=\"n\">_data_I_21</span> <span class=\"o\">+</span> <span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">-</span> <span class=\"mi\">2404</span><span class=\"p\">;</span>\n <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int64_t</span> <span class=\"n\">ctr_1</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\"><</span> <span class=\"mi\">600</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"p\">{</span>\n <span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">-2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"mi\">4</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"mi\">4</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"mi\">4</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"mi\">4</span><span class=\"p\">];</span>\n <span class=\"p\">}</span>\n <span class=\"p\">}</span>\n<span class=\"p\">}</span>\n</pre></div>\n"
+ "text/html": [
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_21</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">81</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">290</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I_21</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1160</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1160</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I_21</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1160</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1160</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">289</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "</pre></div>\n"
+ ],
+ "text/plain": [
+ "FUNC_PREFIX void kernel(double * RESTRICT const _data_I, double * RESTRICT _data_dst)\n",
+ "{\n",
+ " double * RESTRICT _data_I_21 = _data_I + 1;\n",
+ " for (int64_t ctr_0 = 1; ctr_0 < 81; ctr_0 += 1)\n",
+ " {\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + 290*ctr_0;\n",
+ " double * RESTRICT _data_I_21_01 = _data_I_21 + 1160*ctr_0 + 1160;\n",
+ " double * RESTRICT _data_I_21_0m1 = _data_I_21 + 1160*ctr_0 - 1160;\n",
+ " for (int64_t ctr_1 = 1; ctr_1 < 289; ctr_1 += 1)\n",
+ " {\n",
+ " _data_dst_00[ctr_1] = -1.0*_data_I_21_01[4*ctr_1 + 4] - 1.0*_data_I_21_0m1[4*ctr_1 + 4] - 1.0*_data_I_21_0m1[4*ctr_1 - 4] - 2.0*_data_I_21_0m1[4*ctr_1] + 2.0*_data_I_21_01[4*ctr_1] + _data_I_21_01[4*ctr_1 - 4];\n",
+ " }\n",
+ " }\n",
+ "}"
+ ]
},
"metadata": {},
"output_type": "display_data"
@@ -909,43 +1612,151 @@
"cell_type": "code",
"execution_count": 37,
"metadata": {},
- "outputs": [],
- "source": [
- "gpu_ast = create_kernel(update_rule, target=ps.Target.GPU, gpu_indexing=ps.gpucuda.indexing.BlockIndexing,\n",
- " gpu_indexing_params={'blockSize': (8,8,4)})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
"outputs": [
{
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"data": {
- "text/plain": "FUNC_PREFIX __launch_bounds__(256) void kernel(double * RESTRICT const _data_I, double * RESTRICT _data_dst)\n{\n if (blockDim.x*blockIdx.x + threadIdx.x + 1 < 202 && blockDim.y*blockIdx.y + threadIdx.y + 1 < 600)\n {\n const int64_t ctr_0 = blockDim.x*blockIdx.x + threadIdx.x + 1;\n const int64_t ctr_1 = blockDim.y*blockIdx.y + threadIdx.y + 1;\n double * RESTRICT _data_dst_10 = _data_dst + ctr_1;\n double * RESTRICT _data_I_11_21 = _data_I + 4*ctr_1 + 5;\n double * RESTRICT _data_I_1m1_21 = _data_I + 4*ctr_1 - 3;\n double * RESTRICT _data_I_10_21 = _data_I + 4*ctr_1 + 1;\n _data_dst_10[601*ctr_0] = -2.0*_data_I_10_21[2404*ctr_0 - 2404] + 2.0*_data_I_10_21[2404*ctr_0 + 2404] - _data_I_11_21[2404*ctr_0 + 2404] - _data_I_11_21[2404*ctr_0 - 2404] + _data_I_1m1_21[2404*ctr_0 + 2404] - _data_I_1m1_21[2404*ctr_0 - 2404];\n } \n}",
- "text/html": "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span> <span class=\"nf\">__launch_bounds__</span><span class=\"p\">(</span><span class=\"mi\">256</span><span class=\"p\">)</span> <span class=\"kt\">void</span> <span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"k\">const</span> <span class=\"n\">_data_I</span><span class=\"p\">,</span> <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_dst</span><span class=\"p\">)</span>\n<span class=\"p\">{</span>\n <span class=\"k\">if</span> <span class=\"p\">(</span><span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">x</span> <span class=\"o\">+</span> <span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">x</span> <span class=\"o\">+</span> <span class=\"mi\">1</span> <span class=\"o\"><</span> <span class=\"mi\">202</span> <span class=\"o\">&&</span> <span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">y</span> <span class=\"o\">+</span> <span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">y</span> <span class=\"o\">+</span> <span class=\"mi\">1</span> <span class=\"o\"><</span> <span class=\"mi\">600</span><span class=\"p\">)</span>\n <span class=\"p\">{</span>\n <span class=\"k\">const</span> <span class=\"kt\">int64_t</span> <span class=\"n\">ctr_0</span> <span class=\"o\">=</span> <span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">x</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">x</span> <span class=\"o\">+</span> <span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">x</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">;</span>\n <span class=\"k\">const</span> <span class=\"kt\">int64_t</span> <span class=\"n\">ctr_1</span> <span class=\"o\">=</span> <span class=\"n\">blockDim</span><span class=\"p\">.</span><span class=\"n\">y</span><span class=\"o\">*</span><span class=\"n\">blockIdx</span><span class=\"p\">.</span><span class=\"n\">y</span> <span class=\"o\">+</span> <span class=\"n\">threadIdx</span><span class=\"p\">.</span><span class=\"n\">y</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_dst_10</span> <span class=\"o\">=</span> <span class=\"n\">_data_dst</span> <span class=\"o\">+</span> <span class=\"n\">ctr_1</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_I_11_21</span> <span class=\"o\">=</span> <span class=\"n\">_data_I</span> <span class=\"o\">+</span> <span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"mi\">5</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_I_1m1_21</span> <span class=\"o\">=</span> <span class=\"n\">_data_I</span> <span class=\"o\">+</span> <span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"mi\">3</span><span class=\"p\">;</span>\n <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_I_10_21</span> <span class=\"o\">=</span> <span class=\"n\">_data_I</span> <span class=\"o\">+</span> <span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">;</span>\n <span class=\"n\">_data_dst_10</span><span class=\"p\">[</span><span class=\"mi\">601</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">-2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_10_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">-</span> <span class=\"mi\">2404</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_10_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">+</span> <span class=\"mi\">2404</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">_data_I_11_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">+</span> <span class=\"mi\">2404</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">_data_I_11_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">-</span> <span class=\"mi\">2404</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">_data_I_1m1_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">+</span> <span class=\"mi\">2404</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">_data_I_1m1_21</span><span class=\"p\">[</span><span class=\"mi\">2404</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">-</span> <span class=\"mi\">2404</span><span class=\"p\">];</span>\n <span class=\"p\">}</span> \n<span class=\"p\">}</span>\n</pre></div>\n"
+ "text/html": [
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_21</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">81</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">290</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I_21</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1160</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1160</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_I_21</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1160</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1160</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">289</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">1.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_0m1</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">2.0</span><span class=\"o\">*</span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_I_21_01</span><span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">4</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "</pre></div>\n"
+ ],
+ "text/plain": [
+ "FUNC_PREFIX void kernel(double * RESTRICT const _data_I, double * RESTRICT _data_dst)\n",
+ "{\n",
+ " double * RESTRICT _data_I_21 = _data_I + 1;\n",
+ " for (int64_t ctr_0 = 1; ctr_0 < 81; ctr_0 += 1)\n",
+ " {\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + 290*ctr_0;\n",
+ " double * RESTRICT _data_I_21_01 = _data_I_21 + 1160*ctr_0 + 1160;\n",
+ " double * RESTRICT _data_I_21_0m1 = _data_I_21 + 1160*ctr_0 - 1160;\n",
+ " for (int64_t ctr_1 = 1; ctr_1 < 289; ctr_1 += 1)\n",
+ " {\n",
+ " _data_dst_00[ctr_1] = -1.0*_data_I_21_01[4*ctr_1 + 4] - 1.0*_data_I_21_0m1[4*ctr_1 + 4] - 1.0*_data_I_21_0m1[4*ctr_1 - 4] - 2.0*_data_I_21_0m1[4*ctr_1] + 2.0*_data_I_21_01[4*ctr_1] + _data_I_21_01[4*ctr_1 - 4];\n",
+ " }\n",
+ " }\n",
+ "}"
+ ]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
- "ps.show_code(gpu_ast)"
+ "try:\n",
+ " import pycuda\n",
+ " from pystencils.gpucuda import BlockIndexing\n",
+ "\n",
+ " gpu_ast = create_kernel(update_rule, target=ps.Target.GPU,\n",
+ " gpu_indexing=BlockIndexing,\n",
+ " gpu_indexing_params={'blockSize': (64, 1, 1)})\n",
+ "\n",
+ " ps.show_code(ast)\n",
+ "except ImportError:\n",
+ " print(\"Please install pycuda for GPU support\")"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -959,9 +1770,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.2"
+ "version": "3.10.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/doc/notebooks/02_tutorial_basic_kernels.ipynb b/doc/notebooks/02_tutorial_basic_kernels.ipynb
index 413572375e075500898e146cc4c46a5dbb058f8c..eceb7117d1d861c9d904376082d85f0931d521f2 100644
--- a/doc/notebooks/02_tutorial_basic_kernels.ipynb
+++ b/doc/notebooks/02_tutorial_basic_kernels.ipynb
@@ -207,7 +207,7 @@
"<div>Subexpressions:</div><table style=\"border:none; width: 100%; \"><tr style=\"border:none\"> <td style=\"border:none\">$$a \\leftarrow {src}_{(0,1)} + {src}_{(-1,0)}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$b \\leftarrow 2 {src}_{(1,0)} + {src}_{(0,-1)}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$c \\leftarrow - {src}_{(0,0)} + 2 {src}_{(1,0)} + {src}_{(0,1)} + {src}_{(0,-1)} + {src}_{(-1,0)}$$</td> </tr> </table><div>Main Assignments:</div><table style=\"border:none; width: 100%; \"><tr style=\"border:none\"> <td style=\"border:none\">$${dst}_{(0,0)} \\leftarrow a + b + c$$</td> </tr> </table>"
],
"text/plain": [
- "AssignmentCollection: dst_C, <- f(src_W, src_S, src_N, src_C, src_E)"
+ "AssignmentCollection: dst_C, <- f(src_N, src_E, src_W, src_C, src_S)"
]
},
"execution_count": 7,
@@ -274,7 +274,7 @@
"<div>Subexpressions:</div><table style=\"border:none; width: 100%; \"><tr style=\"border:none\"> <td style=\"border:none\">$$a \\leftarrow {src}_{(0,1)} + {src}_{(-1,0)}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$b \\leftarrow 2 {src}_{(1,0)} + {src}_{(0,-1)}$$</td> </tr> <tr style=\"border:none\"> <td style=\"border:none\">$$c \\leftarrow - {src}_{(0,0)} + a + b$$</td> </tr> </table><div>Main Assignments:</div><table style=\"border:none; width: 100%; \"><tr style=\"border:none\"> <td style=\"border:none\">$${dst}_{(0,0)} \\leftarrow a + b + c$$</td> </tr> </table>"
],
"text/plain": [
- "AssignmentCollection: dst_C, <- f(src_W, src_S, src_N, src_C, src_E)"
+ "AssignmentCollection: dst_C, <- f(src_N, src_E, src_W, src_C, src_S)"
]
},
"execution_count": 9,
@@ -415,11 +415,11 @@
{
"data": {
"text/html": [
- "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
"<span class=\"p\">{</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">18</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_02</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">60</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">28</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
@@ -431,11 +431,11 @@
"</pre></div>\n"
],
"text/plain": [
- "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_src)\n",
+ "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_src)\n",
"{\n",
" for (int64_t ctr_0 = 2; ctr_0 < 18; ctr_0 += 1)\n",
" {\n",
- " double * RESTRICT _data_dst_00 = _data_dst + 30*ctr_0;\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + 30*ctr_0;\n",
" double * RESTRICT _data_src_02 = _data_src + 30*ctr_0 + 60;\n",
" double * RESTRICT _data_src_0m1 = _data_src + 30*ctr_0 - 30;\n",
" for (int64_t ctr_1 = 2; ctr_1 < 28; ctr_1 += 1)\n",
@@ -556,11 +556,11 @@
{
"data": {
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- "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
"<span class=\"p\">{</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">0</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">18</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
- "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_02</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">60</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
"<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
@@ -572,11 +572,11 @@
"</pre></div>\n"
],
"text/plain": [
- "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_src)\n",
+ "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_src)\n",
"{\n",
" for (int64_t ctr_0 = 0; ctr_0 < 18; ctr_0 += 1)\n",
" {\n",
- " double * RESTRICT _data_dst_00 = _data_dst + 30*ctr_0;\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + 30*ctr_0;\n",
" double * RESTRICT _data_src_02 = _data_src + 30*ctr_0 + 60;\n",
" double * RESTRICT _data_src_0m1 = _data_src + 30*ctr_0 - 30;\n",
" for (int64_t ctr_1 = 1; ctr_1 < 30; ctr_1 += 1)\n",
@@ -716,31 +716,183 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "However, for right hand sides that are Field.Accesses this is allowed:"
+ "Also it is not allowed to write a field at the same location"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Field dst is written twice at the same location\n"
+ ]
+ }
+ ],
+ "source": [
+ "@ps.kernel\n",
+ "def not_allowed():\n",
+ " dst[0, 0] @= src[0, 1] + src[1, 0]\n",
+ " dst[0, 0] @= 2 * dst[0, 0]\n",
+ "\n",
+ "try:\n",
+ " ps.create_kernel(not_allowed)\n",
+ " assert False\n",
+ "except ValueError as e:\n",
+ " print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This situation should be resolved by introducing temporary variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
"outputs": [
{
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- "KernelFunction kernel([_data_dst, _data_src])"
+ "<IPython.core.display.HTML object>"
]
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+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">19</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">30</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">29</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">a</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src_00</span><span class=\"p\">[</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_src_01</span><span class=\"p\">[</span><span class=\"n\">ctr_1</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">a</span><span class=\"o\">*</span><span class=\"mf\">2.0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "</pre></div>\n"
+ ],
+ "text/plain": [
+ "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_src)\n",
+ "{\n",
+ " for (int64_t ctr_0 = 1; ctr_0 < 19; ctr_0 += 1)\n",
+ " {\n",
+ " double * RESTRICT _data_src_01 = _data_src + 30*ctr_0 + 30;\n",
+ " double * RESTRICT _data_src_00 = _data_src + 30*ctr_0;\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + 30*ctr_0;\n",
+ " for (int64_t ctr_1 = 1; ctr_1 < 29; ctr_1 += 1)\n",
+ " {\n",
+ " const double a = _data_src_00[ctr_1 + 1] + _data_src_01[ctr_1];\n",
+ " _data_dst_00[ctr_1] = a*2.0;\n",
+ " }\n",
+ " }\n",
+ "}"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
+ "tmp_var = sp.Symbol(\"a\")\n",
+ "\n",
"@ps.kernel\n",
"def allowed():\n",
- " dst[0, 0] @= src[0, 1] + src[1, 0]\n",
- " dst[0, 0] @= 2 * dst[0, 0]\n",
- "ps.create_kernel(allowed)"
+ " tmp_var @= src[0, 1] + src[1, 0]\n",
+ " dst[0, 0] @= 2 * tmp_var\n",
+ "\n",
+ "\n",
+ "ast = ps.create_kernel(allowed)\n",
+ "ps.show_code(ast)"
]
}
],
@@ -760,9 +912,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/doc/notebooks/06_tutorial_phasefield_dentritic_growth.ipynb b/doc/notebooks/06_tutorial_phasefield_dentritic_growth.ipynb
index 0f8bfaf95cf0da3745f55389f1a892ca61c0fc50..1e11abbaf409d2b657d60ff7b3e4e5a008d33d07 100644
--- a/doc/notebooks/06_tutorial_phasefield_dentritic_growth.ipynb
+++ b/doc/notebooks/06_tutorial_phasefield_dentritic_growth.ipynb
@@ -52,11 +52,28 @@
"execution_count": 3,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_png function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_rgba function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_mask function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The MathtextBackendBitmap class was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n"
+ ]
+ },
{
"data": {
- "image/png": "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\n",
"text/latex": [
- "$\\displaystyle \\frac{{{φ}_{(0,0)}}^{4}}{4} - {{φ}_{(0,0)}}^{3} \\left(\\frac{1}{2} - \\frac{m}{3}\\right) + {{φ}_{(0,0)}}^{2} \\left(\\frac{1}{4} - \\frac{m}{2}\\right) + \\frac{ε^{2} \\left({\\partial_{0} {{φ}_{(0,0)}}}^{2} + {\\partial_{1} {{φ}_{(0,0)}}}^{2}\\right)}{2}$"
+ "$\\displaystyle \\frac{{φ}_{(0,0)}^{4}}{4} - {φ}_{(0,0)}^{3} \\left(\\frac{1}{2} - \\frac{m}{3}\\right) + {φ}_{(0,0)}^{2} \\left(\\frac{1}{4} - \\frac{m}{2}\\right) + \\frac{ε^{2} \\left({\\partial_{0} {φ}_{(0,0)}}^{2} + {\\partial_{1} {φ}_{(0,0)}}^{2}\\right)}{2}$"
],
"text/plain": [
" 4 2 ⎛ 2 2⎞\n",
@@ -99,7 +116,7 @@
"outputs": [
{
"data": {
- "image/png": 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z7s4DnD8mzetyRESCTuHBw9TWN/X4+TlQzygB8bmxaSTFR/OcLkoREelQTz+apy0FXQDEREXwhclDeG3LPqrr2t8qKCIiW0qqiYmKYHR6z/WIcoyCLkCumppJfVMLyzaWel2KiEjQ2VRUxbhB/Yju4QtRQEEXMJMzE8lOTVDzpYhIO845NpdUedJsCQq6gDEzrp6ayarCCgoPHPa6HBGRoLGn4gg1dU0KulDwxTMyiY40Hn1/l9eliIgEjWMXonhxxSUo6AIqvX8sV+RksDhvLxWHG7wuR0QkKGwuriY60jgtvZ8n21fQBdiCc7Kpa2zh8ZWFXpciIhIUNhdXMWZQP2KivIkcBV2AjU7vx+fGpvH4yt0cbdDTx0UkvDnn2FRc5VmzJSjousWCc7KpONzAM+r/UkTCXFHlUaqONnp2IQoo6LrFjBHJTM5M5JF3C2hu6ewTi0REQs/aPZUATM5M8qwGBV03MDMWnDOS3QeP8NqWfV6XIyLimdWFFfTtE8W4wf09q0FB103mTBjE0OR4HlpegHM6qhOR8LR6VyVThw0gMqKj52z3DAVdN4mMMG49ewTr9x5idWGl1+WIiPS4Q0cayN9fw/ThAzytQ0HXja45I4sB8dEsXP6x16WIiPS4Nbtb/8jPHZ7saR0Kum4UFxPJDWcO541tZewsq/G6HBGRHrWqsILoSCMny7sLUSBAQWdmc8ws38x2mtndHUw3M7vPN32jmU1tM63QzDaZ2XozywtEPcHkxjOH0ScqgoeXq1swEQkveYWVTMxIJDY60tM6/A46M4sE7gfmAuOB68xsfLvZ5gKjfa8FwAPtpp/vnMtxzuX6W0+wSenbhy+ekcnz64opq67zuhwRkR5R19jMxqJDTBvhbbMlBOaIbjqw0zlX4JxrAJ4C5rWbZx7wuGv1AZBkZoMDsO1e4dazs2lsaeGxFYVelyIi0iM27D1EY7Nj2rDQCLoMYG+b4SLfuM7O44DXzGyNmS0IQD1BZ8TABC4eP4i/fLCb2vomr8sREel2qwsrAMj1+IpLCEzQdXRzRPsbx040z2zn3FRamzfvNLNzOtyI2QIzyzOzvPLy8lOv1iO3nZtNdV2TOnsWkbCwurCSMen9SIqP8bqUgARdEZDVZjgTKOnsPM65Y+9lwPO0NoV+hnNuoXMu1zmXm5qaGoCye9aUoQO4cFwa9/9jp87ViUhIa25xrN1dGRRHcxCYoFsNjDazEWYWA8wHlrabZynwVd/VlzOBKudcqZklmFk/ADNLAD4PbA5ATUHpnkvH09js+MUr+V6XIiLSbbaVVlNT38T0ILgQBQIQdM65JuAu4FVgG7DYObfFzG43s9t9sy0DCoCdwMPA133j04H3zGwDsAp4yTn3ir81BavhAxP4p7NG8OzaItbtUW8pIhKa8j45PxccQRcViJU455bRGmZtxz3Y5rMD7uxguQJgciBq6C3u+twonltbxL1Lt/D812cT4WH/byIi3WF1YSUZSXFkJMV5XQqgnlF6XN8+UXx/zlg2FFXxrJ5XJyIhxjnH6sKKoDk/Bwo6T1w5JYOcrCR+8Uo+NXWNXpcjIhIweyqOUFZTz7QgabYEBZ0nIiKM//jC6RyorecP/9jpdTkiIgFz7GktwXIhCijoPDM5K4lrzsjk0fd3UVBe63U5IiIBsXpXBYlx0YxK7et1KZ9Q0Hnoe3PG0Ccqkp+8uNXrUkREAmL17gqmDR8QVBfaKeg8lNYvlm9cMIq38st5a3uZ1+WIiPjlQG09BeWHg+a2gmMUdB67adYIsgcm8JMXt9LQ1OJ1OSIipyzPd34umC5EAQWd52KiIvh/l42n4MBh/vSenlknIr3X6sIK+kRFMDEj0etSPkVBFwTOH5vG58en8+vX81m/95DX5YiInJLVhRXkZCURExVc0RJc1YSxX35xEun9Y7nzibVUHm7wuhwRkS45XN/ElpLqoLqt4BgFXZBIio/hj1+ZSnlNPd9evJ6WlvZPOhIRCV7r9hyiucUF3YUooKALKpMyk/jh5eN5O7+c+9/SjeQi0nusLqwgwmDq0CSvS/kMBV2Q+cqMoVyRM4Rfv7GD9z464HU5IiKdsrqwgnGD+9MvNtrrUj5DQRdkzIz/umoio9P68s2n1rGvSg9pFZHg1tjcwro9h4LutoJjFHRBKD4mij9+5QyONjZz55NraWzW/XUiEry2lFRztLFZQSddMyqtL7+4ehJrdlfyi5e3e12OiMhxfVhwEIBpQfRonrYUdEHs8slDuGnWcB55bxcvbyr1uhwRkQ69s6OcsYP6kdY/1utSOhSQoDOzOWaWb2Y7zezuDqabmd3nm77RzKZ2dtlw9++XjCMnK4nvLN6gi1NEJOjU1DWyurCC88akeV3KcfkddGYWCdwPzAXGA9eZ2fh2s80FRvteC4AHurBsWIuJimDhV89gaHI8//TYal7fut/rkkREPvH+zoM0NjvOG5PqdSnHFRWAdUwHdjrnCgDM7ClgHtD22TPzgMedcw74wMySzGwwMLwTy4a9tH6xPH3bTG58dBW3/2UNv/7SZOblZHhdVkhraXHsq66jqPIo1Ucbqa5r9L03UX20kZq6Jmrrm4iIMPpERRAbHUGfqEj6RPneoyNISYhhaHI8w1ISSOvXJ6geWyISKO/sKKNfnyjOGBac5+cgMEGXAextM1wEzOjEPBmdXBYAM1tA69EgQ4cO9a/iXigpPoYnvjaTWx5bzbeeXs+Rhmaumx5++yHQDtbWk7+/hsIDRyg8eJjCA4cpPHiY3QePUH+cp0nEx0TSLzaKvn2icA7qm1qoa2ymvqmF+qZmGps/26tNTFQEWQPiGJaSwNDkeEam9WX68GRGp/VVAEqv5Zzjre3lnH3aQKIjg/eSj0AEXUf/Stv/Sz/ePJ1ZtnWkcwuBhQC5ublh2T9W3z5RPHbzdO54Yg3/9twmDtc3cevZ2V6X1Wscrm9iU3EVG4sOsWFvFRuKDlFUefST6TFREQxLjmf4wATOPS2V4QMTyBoQz4D4GPrFRtE/Lpp+sVEn/Qfd3OKob2qmrLqePRVH/u91sPV91a4KauubAEiKj2ba8GRmjEhm+ohkxg/uT1QQ/4ch0tb2fTXsq67jvNOC9/wcBCboioCsNsOZQEkn54npxLLSRlxMJAtvyOVbT6/jpy9to7a+iW9eMBozHRW0V1Zdx8qCg6z8+CBr91Sys6yWY12IZg6IY3JmEl89cxjjBycyIjWBwf1jA3J0FRlhxMdEMXxgFMMHJnxmunOOvRVH+XDXQVbtqmBVYcUn514TYiKZNiKZSycO5uIJg+gfhL1MiBzzVn7rA6PPDeLzcxCYoFsNjDazEUAxMB/4crt5lgJ3+c7BzQCqnHOlZlbeiWWlnZioCO6bP4X4mE389o2PqK1r4t8uGUdkmDeBVR5u4IOCg6z4+CArPj7Ax+WHAegf23r+4JKJg5mcmcSkzERS+vbxrE4zY2hKPENT4rkmt/XvvP3Vda2ht6uCd3aU871nNvKDFzZz4bg0vjA5g/PGpBIbHelZzSIdeTu/nNOH9Cc9SG8rOMbvoHPONZnZXcCrQCTwqHNui5nd7pv+ILAMuATYCRwBbj7Rsv7WFA6iIiP45dWT6Nsnikfe20Xe7kp++cVJnJbez+vSekxjcwtrdlfyzo5y3skvZ2tpNdB6Dm36iGS+lJvFrJEDGT+kf9D/EZDeP5bLJw/h8slDcM6xfu8hlqwv4cWNJSzbtI9+sVHMnTCIK3IymJmdovN64rmqo42s2V3JHeeO9LqUk7LWCyF7l9zcXJeXl+d1GUHBOceS9SX8x9+3UFvfxB3njeLO80fSJyo0//ovOXSUd3aU83Z+Ge/vPEhtfRNREcbUYQM4e9RAZo1KYVJmUlCfGO+KpuYWVnx8kCXrS3h1yz5q65sYldaXBedkc0VORtA94FLCx0sbS7nzybU8c/uZQfNoHjNb45zL/cx4BV1oOFhbz09e3MoL60t83YdN5IxhwfHl80dDUwt5hRW87Qu3HftrARiSGMu5Y9I497RUZo9KCcoe0wOtrrGZVzbv46HlBWwrrSa9fx9uOWsE100fGhY/vwSX7/5tA69v3c+aey4MmguoFHRh4q38Mn7w3CZKq+v46sxhfG/OWPr2CcSp2J5TfOgob+eX8XZ+OSt2HuBwQzPRkcb0Ecmcd1oa545JZXRa37C9AMc5x7sfHeDBdz5mxccH6RcbxfUzh3Hz7OGk9QvucyUSGlpaHDN+9iYzs1P4/XVTvC7nEwq6MFJb38T/vJrPopWFDO4fyx3njeQLORkkxgXnX/3HuhB6f+dB3v2o/JOjtoykOM4bk8p5Y9KYNTKFhF4W2D1hY9EhHlpewMubSomKiODLM4byzQtGMyAhxuvSJIRtLq7ist+/x6+umczVZ2R6Xc4nFHRhaM3uSu5duoVNxVX0iYpg7oRBXDttKDOzkz09GqprbGbtnkpW7Gy9OnJDURXNLY6YqAimDR/Aeaelcd6YVEaF8VFbVxUeOMxDyz/m6dV76RcbzTcvGM0NZw4LmXOVElx+/+ZH/Or1HeTdcyEDPbyCuT0FXZhyzrG5uJqn8/awZH0JNXVNDEuJ50u5WVw9NZNBid3b1HXsnrGNxYfYWFTFhr2HWL/3EPVNLUQYTMpMYvaoFGaNHMgZwwboEno/5e+r4acvbeXdjw6QnZrAPZeO4/wxafqDQQLq6gdW0NTcwpK7zvK6lE9R0Al1jc28vLmUp1fv5YOCik+CZtzgfoxJ78eYQf0ZO6jfKTV7Oec4UNtAadVRiiqPsqWkio1FVWwqruLQkUYAYiIjGDe4H1OHDWD2yIFMz07WDdHdwDnHW/ll/PTFbRQcOMzZowfy/y4bH1a3nkj3qTzcwBk/fZ27Pjea71x0mtflfIqCTj5l98HDPLOmiLzCSrbvq6bSF0YA6f37MHZQf7KS44iKiCDCjMgIiIgwIs2IjDCafZ0elx6qo6TqKKVVdTS06RsyMsIYk96PSZmJTMxMZHJmEqel99Pl8D2osbmFP6/czW/f2EFtfRM3zBzGv84Zq3Od4pcl64v55lPref7rs5gyNLg6clbQyXE55yivqWfbvhry91WzvbSG7ftqKK06SnOLo8W19t/Y7BwtvvcIM9L79WFIUhyDk+IYkhTLkMQ4BifGMiQpjlFpfdUMGSQqDzfwmzd28OcPdpORFMcvvziJWSMHel2W9FLffno97+woZ/UPLgy6jhiOF3T6004wM9L6x5LWP5ZzT+tcn3XOOZ336SUGJMTw43kTuHzyEL73tw18+eEPufHMYXx/7ljiY/RfgHReS4vjnR3lnDN6YNCF3ImoHUlOiUKu95k2PJmXv3kON88ezuMf7GbOb9/lw4KDXpclvcjG4ioqDjdw/tjgflpBewo6kTASFxPJjy4/nae+NhOAaxd+wL1Lt3CkocnjyqQ3eGt7GWZwzujgflpBewo6kTA0IzuFV751NjfNGs5jKwq55Hfvsrm4yuuyJMi9nV9GTlZSr+uQQEEnEqbiY6K49wun89evzaS+qYWrHljBEx/upjdeoCbd70BtPRuLqzh/TO9qtgQFnUjYO3NkCi/+81nMzE7hB89v5ttPr+dwvZoy5dOW7yjHORR0ItI7pfTtw2M3TeM7F53Gkg0lzLv/fT7aX+N1WRJE3txexsC+MZw+pL/XpXSZgk5EgNYOAb5xwWj+cssMDh1p4At/eJ/n1xV5XZYEgdr6Jt7ctp+LTx/UKx/661fQmVmymb1uZh/53ju8Td7M5phZvpntNLO724y/18yKzWy973WJP/WIiP9mjxrIS984m4kZiXz76Q3823ObqGts9ros8dBrW/ZR19jClVMyvC7llPh7RHc38KZzbjTwpm/4U8wsErgfmAuMB64zs/FtZvmNcy7H91rmZz0iEgDp/WN58mszuP3ckfx11R6+/PAHlNfUe12WeOT5dcVkDojjjGHB1eVXZ/kbdPOARb7Pi4ArOphnOrDTOVfgnGsAnvItJyJBLCoygrvnjuWBr0xla2k1V9z/Ptv3VXtdlvSwspo63t95gCtyMnptRxH+Bl26c64UwPfe0eU4GcDeNsNFvnHH3GVmG83s0eM1fYqId+ZOHMzfboC8yXgAABKESURBVJtFU0sLV/9xBW9u2+91SdKD/r6hlBYHV0wZ4nUpp+ykQWdmb5jZ5g5enT0q6+hPgGM36jwAjARygFLgVyeoY4GZ5ZlZXnl5eSc3LSKBMDEzkSV3nkV2al9ufTyPh5cX6H67MPHCumImZiQyKq33PubppEHnnLvQOTehg9cSYL+ZDQbwvZd1sIoiIKvNcCZQ4lv3fudcs3OuBXiY1mbO49Wx0DmX65zLTU3tXd3PiISCQYmxLL7tTOacPoj/XLaNu5/d9KlHM0no2VlWy6biKubl9N6jOfC/6XIpcKPv843Akg7mWQ2MNrMRZhYDzPctdywcj7kS2OxnPSLSjeJiIrn/y1O56/xRPJ23lxv+9CGVhxu8Lku6yZL1xUQYfGFyeAfdz4GLzOwj4CLfMGY2xMyWATjnmoC7gFeBbcBi59wW3/K/NLNNZrYROB/4tp/1iEg3i4gwvnvxGH57bQ7r9h7i6gdWsLfiiNdlSYA553h+XTGzRw0krX+s1+X4xa+HUTnnDgIXdDC+BLikzfAy4DO3DjjnbvBn+yLinSumZJAxII5bHlvNVQ+sYNHN0xnfC3vNkI6t2V1JUeVRvn3haV6X4jf1jCIip2za8GSeuWMWURHGtQ+tZOXHer5dqHh+XTGx0RFcPGGQ16X4TUEnIn45Lb0fz94xi/TEWG58dBUvbSz1uiTxU0NTCy9tKuXz4wfRt0/vfwq9gk5E/DYkKY5nbj+TiZmJ3PXXtSxaUeh1SeKHd3aUc+hIY6/t8qs9BZ2IBERSfAxP3DqDC8am86OlW/jvV7frXrte6oV1xSQnxHDW6IFelxIQCjoRCZjY6EgevH4q103P4v63PuZfn9lIU7PutetNqusaeWPbfi6fNJjoyNCIiN7f+CoiQSUqMoL/unIiqX37cN8/dlJb38Rv5+fQJyrS69KkE17ZvI/6phauCJFmS9ARnYh0AzPjO58fwz2XjuPlzfv42uNrONqgR/30Bi+sK2ZYSjw5WUlelxIwCjoR6Ta3np3Nz6+ayLsflXPjo6uormv0uiQ5gX1VdawsONirn1TQEQWdiHSr+dOHct/8KazdU8lXHv6QCnUZFrSWbijGOUKq2RIUdCLSAy6fPISFXz2D/P01XPvQSvZX13ldkrTjnOO5tcVMzkpixMAEr8sJKAWdiPSIz41N57Gbp1Fy6CjXPLhS/WMGmQ8KKti+r4Yv5WZ6XUrAKehEpMfMGjmQJ742k6qjjVzz4Ep2ltV6XZL4LFz+MSkJMVw9VUEnIuKXnKwknr5tJk0tLcxfuJLt+6q9Lins5e+r4a38cm6aNZzY6NC7DURBJyI9buyg/jy14EwiI4z5Cz9gc3GV1yWFtYXLC4iLjuT6mcO8LqVbKOhExBOj0vqy+LYzSYiJ4rqHP2DN7kqvSwpLpVVHWbK+mGunZTEgIcbrcrqFgk5EPDMsJYHFt59JSkIMX/3Th3xQoMf89LT/fb8QB9xy1givS+k2CjoR8VRGUhxP33Ymg5PiuOl/V7F8R7nXJYWN6rpGnvxwD5dOHExWcrzX5XQbv4LOzJLN7HUz+8j3PuA48z1qZmVmtvlUlheR0JbeP5anFsxkeEoCty7K481t+70uKSw8+eEeauubWHBOtteldCt/j+juBt50zo0G3vQNd+QxYI4fy4tIiBvYtw9PLZjJ2MH9uO3Pa1i2SQ9w7U71Tc08+t4uzho1kAkZiV6X0638Dbp5wCLf50XAFR3N5JxbDlSc6vIiEh6S4mP4y60zyMlK4q4n1/L8uiKvSwpZS9aXUFZTH/JHc+B/0KU750oBfO9p3bW8mS0wszwzyysvVxu+SKjqHxvNon+azszsFL6zeANPfrjH65JCTkuLY+HyAsYN7s/ZIfJw1RM5adCZ2RtmtrmD17yeKPAY59xC51yucy43NTW1JzctIj0soU8Uj940jfNOS+Xfn9/Eo+/t8rqkkPJWfhk7y2q57ZzskHpKwfGc9MGrzrkLjzfNzPab2WDnXKmZDQbKurh9f5cXkRAVGx3JQzfk8o2/ruPHL27laGMzd54/yuuyQsJD7xSQkRTHpZMGe11Kj/C36XIpcKPv843Akh5eXkRCWExUBH/48hTm5Qzhv1/N51ev5eOc87qsXm3tnkpWFVbwT2eNIDoyPO4w8/en/DlwkZl9BFzkG8bMhpjZsmMzmdlfgZXAGDMrMrNbTrS8iMgxUZER/PpLOVybm8Xv/7GT/3xpm8LODwvfKSAxLpr507K8LqXHnLTp8kSccweBCzoYXwJc0mb4uq4sLyLSVmSE8bOrJhIXE8kj7+3iSGMzP5k3gciI0D+/FEg79tfw6tZ9fP28kST08eu//14lfH5SEenVIiKMH10+nriYSB54+2Oqjzby6y/lEBMVHs1v/nLOcc8Lm0mMi+aWs0L/loK2FHQi0muYGd+fM5akuGh+9vJ2auqaePD6M4iLCb1HywTas2uLWbWrgp9fNZHkEO28+Xj0p5CI9Dq3nTuSn181kXc/Kuf6P31I1ZFGr0sKaoeONPBfy7YxdWgSX8oNn3NzxyjoRKRXmj99KPd/eSqbiqq4duFKymrqvC4paP3ilXyqjjby0ysmEhGG5zUVdCLSa82dOJhHb5rGnoojXPPgSvZWHPG6pKCzdk8lT63ew02zhjN+SH+vy/GEgk5EerWzRg/kiVtncOhII1c/sIL8fTVelxQ0mppbuOf5zaT3i+XbF53mdTmeUdCJSK83ZegAFt92JgDXPLiClR/rAa4Aj6/czdbSan54+Xj6htHtBO0p6EQkJIwZ1I9n75hFWv9Yvvrohzy7JryffLC/uo5fv76Dc09LZe6EQV6X4ykFnYiEjKzkeJ69YxbThifzL3/bwG9e3xG2vaj8+MWtNDa38ON5p4dFx80noqATkZCSGBfNYzdP54tnZPK7Nz/iXxZvoKGpxeuyetTyHeW8tLGUO88fxbCUBK/L8Vz4NtqKSMiKiYrgv784iWHJ8fzq9R2UVB3loetzSYyP9rq0blfX2MwPl2xmxMAEbjs3vHpAOR4d0YlISDIz/vmC0fz22hzW7j7ElQ+8z56DoX37gXOOn7y4lcKDR/jJvAn0iVKPMaCgE5EQd8WUDP58y3QO1jZw5R/fZ8XOA16X1G0WLi/giQ/3cNs52ZwVBk8O7ywFnYiEvBnZKTz39VkMSIjh+j99yP1v7aSlJbQuUvn7hhJ+9vJ2Lp00mO/PGet1OUFFQSciYWFkal+W3Dmbyya1PsT1lkWrOXSkweuyAmJ1YQX/sngD04YP4FfXTA7Lbr5OREEnImEjoU8Uv5ufw0+umMD7Ow9y6X3vsWHvIa/L8svH5bV87fE8MgfEsfCGXGKjdV6uPb+CzsySzex1M/vI9z7gOPM9amZlZra53fh7zazYzNb7Xpd0tLyISKCYGTfMHMbfbj/Wk8pK/ryysFfeb3egtp6b/3c1kWY8dvN0BoTZ43c6y98juruBN51zo4E3fcMdeQyYc5xpv3HO5fhey/ysR0SkUyZnJfHiP5/F7FEp/L8lW/jmU+upqes9j/s52tDMLYvyKKup45EbcxmaEu91SUHL36CbByzyfV4EXNHRTM655UCFn9sSEQmoAQkx/OnGaXzv4jG8uLGEC3/9Dq9s3ud1WSfV3OL45lPr2Fh0iN/Nn8KUoR02pomPv0GX7pwrBfC9p53COu4ys42+5k39tkSkR0VEGHeeP4rnvj6b5IQ+3P6XNXzt8TxKq456XVqHjjQ08b1nNvDa1v388LLxXHx6ePdj2RknDToze8PMNnfwmheA7T8AjARygFLgVyeoY4GZ5ZlZXnl5eQA2LSLyf3Kyklh612z+be5Y3v2onAt/9Q6Pvb+L5iC6DWFLSRWX//49nl9XzLcuHM3Ns0d4XVKvYP6cgDWzfOA851ypmQ0G3nbOjTnOvMOBF51zE05lelu5ubkuLy/vVMsWETmhvRVH+MELm1m+o5zJWUn87MqJnj60tKXF8b8rCvnFy9tJio/mN9fmMHuUbghvz8zWOOdy24/3t+lyKXCj7/ONwJIuFjW4zeCVwObjzSsi0lOykuNZdPM0fjc/h6KKI1z+h/f49+c3UVBe2+O1lNfUc/Njq/nJi1s557RUXvnWOQq5LvL3iC4FWAwMBfYA1zjnKsxsCPCIc+4S33x/Bc4DBgL7gR855/5kZn+mtdnSAYXAbcfO+Z2IjuhEpKccOtLA/7yWz+LVRTS2tHDRuHQWnJNN7vDkbt/22/llfPdvG6ipa+Key8Zz/YyhYf/InRM53hGdX0HnFQWdiPS0spo6Hl+xmz9/sJuqo41MHZrEgnOyuWj8ICID2BOJc47NxdU8uWoPf121h7GD+nHfdVM4Lb1fwLYRqhR0IiIBcKShib/lFfHIewXsrTjKsJR45k8byozsZCYMSSQm6tTOCBWU17J0QwlL15dQcOAw0ZHGV2YM4+65Y9XbSScp6EREAqi5xfHqln0sXF7Ael83Yn2iIsjJSmLa8GRyhw9g6rAB9I/97DPwmlsc9U3NHKxt4NUt+1iyvoRNxVWYwYwRyczLyWDuhEEkxaunk65Q0ImIdJOymjrWFFayurCSvN0VbCmpprnFYQZZA+I/Cbb6xhbqmpppbP70/7uTMhP5wuQhXDZpCIMSYz36KXq/4wWdnjAuIuKntH6xzJ04mLkTWy8kP1zfxIa9h1hdWMnO8lpiIiPoEx1BbFTkp94TYiKZPWog2al9Pf4JQpuCTkQkwBL6RDFr1EBm6TaAoKDH9IiISEhT0ImISEhT0ImISEhT0ImISEhT0ImISEhT0ImISEhT0ImISEhT0ImISEjrlV2AmVk5sLubNzMQONDN2+httE86pv3SMe2Xz9I+6Vig9ssw51xq+5G9Muh6gpnlddRnWjjTPumY9kvHtF8+S/ukY929X9R0KSIiIU1BJyIiIU1Bd3wLvS4gCGmfdEz7pWPaL5+lfdKxbt0vOkcnIiIhTUd0IiIS0hR0IiIS0hR0PmZ2jZltMbMWMzvuZa5mNsfM8s1sp5nd3ZM19jQzSzaz183sI9/7gOPMV2hmm8xsvZnl9XSdPeVkv3trdZ9v+kYzm+pFnT2pE/vkPDOr8n031pvZD72osyeZ2aNmVmZmm48zPey+J9Cp/dJt3xUF3f/ZDFwFLD/eDGYWCdwPzAXGA9eZ2fieKc8TdwNvOudGA2/6ho/nfOdcTqjeI9TJ3/1cYLTvtQB4oEeL7GFd+Pfwru+7keOc+3GPFumNx4A5J5geVt+TNh7jxPsFuum7oqDzcc5tc87ln2S26cBO51yBc64BeAqY1/3VeWYesMj3eRFwhYe1eK0zv/t5wOOu1QdAkpkN7ulCe1C4/XvoFOfccqDiBLOE2/cE6NR+6TYKuq7JAPa2GS7yjQtV6c65UgDfe9px5nPAa2a2xswW9Fh1Paszv/tw+3509uc908w2mNnLZnZ6z5QW1MLte9IV3fJdiQrUinoDM3sDGNTBpB8455Z0ZhUdjOvV92ecaJ90YTWznXMlZpYGvG5m231/vYWSzvzuQ+77cRKd+XnX0tr/YK2ZXQK8QGuTXTgLt+9JZ3XbdyWsgs45d6GfqygCstoMZwIlfq7TUyfaJ2a238wGO+dKfU0rZcdZR4nvvczMnqe1SSvUgq4zv/uQ+36cxEl/XudcdZvPy8zsj2Y20DkXzh0bh9v3pFO687uipsuuWQ2MNrMRZhYDzAeWelxTd1oK3Oj7fCPwmaNeM0sws37HPgOfp/XCnlDTmd/9UuCrvqvqZgJVx5p+Q9RJ94mZDTIz832eTuv/OQd7vNLgEm7fk07pzu9KWB3RnYiZXQn8HkgFXjKz9c65i81sCPCIc+4S51yTmd0FvApEAo8657Z4WHZ3+zmw2MxuAfYA1wC03SdAOvC87/sZBTzpnHvFo3q7zfF+92Z2u2/6g8Ay4BJgJ3AEuNmrentCJ/fJF4E7zKwJOArMdyHeHZOZ/RU4DxhoZkXAj4BoCM/vyTGd2C/d9l1RF2AiIhLS1HQpIiIhTUEnIiIhTUEnIiIhTUEnIiIhTUEnIiIhTUEnIiIhTUEnIiIhTUEn0guZ2S/M7JtmttnMVpvZOK9rEglWCjqRXsbMZtHa1dp6oBj4D+B3nhYlEsQUdCK9z3Tg77T2gt8IvAKc4WlFIkFMQSfS+3TUb19zj1ch0kso6ER6n3eBS/F1iAtc7RsnIh3Q0wtEehnn3FozewZ4GEgGEoHrva1KJHjp6QUivZSZnQd81zl3mde1iAQzNV2KiEhI0xGdiIiENB3RiYhISFPQiYhISFPQiYhISFPQiYhISFPQiYhISPv/XhEpxHIj8SUAAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 504x288 with 1 Axes>"
]
@@ -131,11 +148,28 @@
"execution_count": 5,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_png function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_rgba function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_mask function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The MathtextBackendBitmap class was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n"
+ ]
+ },
{
"data": {
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"text/latex": [
- "$\\displaystyle \\bar{\\epsilon} \\left(δ \\cos{\\left(j \\left(- θ_{0} + \\operatorname{atan_{2}}{\\left({\\partial_{1} {{φ}_{(0,0)}}},{\\partial_{0} {{φ}_{(0,0)}}} \\right)}\\right) \\right)} + 1\\right)$"
+ "$\\displaystyle \\bar{\\epsilon} \\left(δ \\cos{\\left(j \\left(- θ_{0} + \\operatorname{atan_{2}}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)}\\right) \\right)} + 1\\right)$"
],
"text/plain": [
"\\bar{\\epsilon}⋅(δ⋅cos(j⋅(-θ₀ + atan2(D(φ[0,0]), D(φ[0,0])))) + 1)"
@@ -180,42 +214,49 @@
"scrolled": false
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_png function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_rgba function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_mask function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The MathtextBackendBitmap class was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n"
+ ]
+ },
{
"data": {
- "image/png": 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\n",
"text/latex": [
- "$\\displaystyle {{φ}_{(0,0)}}^{3} - \\frac{{{φ}_{(0,0)}}^{2} α \\operatorname{atan}{\\left({{T}_{(0,0)}} γ - T_{eq} γ \\right)}}{\\pi} - \\frac{3 {{φ}_{(0,0)}}^{2}}{2} + \\frac{{{φ}_{(0,0)}} α \\operatorname{atan}{\\left({{T}_{(0,0)}} γ - T_{eq} γ \\right)}}{\\pi} + \\frac{{{φ}_{(0,0)}}}{2} - \\bar{\\epsilon}^{2} δ^{2} \\cos^{2}{\\left(j θ_{0} - j \\operatorname{atan_{2}}{\\left({\\partial_{1} {{φ}_{(0,0)}}},{\\partial_{0} {{φ}_{(0,0)}}} \\right)} \\right)} {\\partial_{0} {\\partial_{0} {{φ}_{(0,0)}}}} - \\bar{\\epsilon}^{2} δ^{2} \\cos^{2}{\\left(j θ_{0} - j \\operatorname{atan_{2}}{\\left({\\partial_{1} {{φ}_{(0,0)}}},{\\partial_{0} {{φ}_{(0,0)}}} \\right)} \\right)} {\\partial_{1} {\\partial_{1} {{φ}_{(0,0)}}}} - 2 \\bar{\\epsilon}^{2} δ \\cos{\\left(j θ_{0} - j \\operatorname{atan_{2}}{\\left({\\partial_{1} {{φ}_{(0,0)}}},{\\partial_{0} {{φ}_{(0,0)}}} \\right)} \\right)} {\\partial_{0} {\\partial_{0} {{φ}_{(0,0)}}}} - 2 \\bar{\\epsilon}^{2} δ \\cos{\\left(j θ_{0} - j \\operatorname{atan_{2}}{\\left({\\partial_{1} {{φ}_{(0,0)}}},{\\partial_{0} {{φ}_{(0,0)}}} \\right)} \\right)} {\\partial_{1} {\\partial_{1} {{φ}_{(0,0)}}}} - \\bar{\\epsilon}^{2} {\\partial_{0} {\\partial_{0} {{φ}_{(0,0)}}}} - \\bar{\\epsilon}^{2} {\\partial_{1} {\\partial_{1} {{φ}_{(0,0)}}}}$"
+ "$\\displaystyle {φ}_{(0,0)}^{3} - \\frac{{φ}_{(0,0)}^{2} α \\operatorname{atan}{\\left({T}_{(0,0)} γ - T_{eq} γ \\right)}}{\\pi} - \\frac{3 {φ}_{(0,0)}^{2}}{2} + \\frac{{φ}_{(0,0)} α \\operatorname{atan}{\\left({T}_{(0,0)} γ - T_{eq} γ \\right)}}{\\pi} + \\frac{{φ}_{(0,0)}}{2} - \\bar{\\epsilon}^{2} δ^{2} \\cos^{2}{\\left(j θ_{0} - j \\operatorname{atan_{2}}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)} \\right)} {\\partial_{0} {\\partial_{0} {φ}_{(0,0)}}} - \\bar{\\epsilon}^{2} δ^{2} \\cos^{2}{\\left(j θ_{0} - j \\operatorname{atan_{2}}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)} \\right)} {\\partial_{1} {\\partial_{1} {φ}_{(0,0)}}} - 2 \\bar{\\epsilon}^{2} δ \\cos{\\left(j θ_{0} - j \\operatorname{atan_{2}}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)} \\right)} {\\partial_{0} {\\partial_{0} {φ}_{(0,0)}}} - 2 \\bar{\\epsilon}^{2} δ \\cos{\\left(j θ_{0} - j \\operatorname{atan_{2}}{\\left({\\partial_{1} {φ}_{(0,0)}},{\\partial_{0} {φ}_{(0,0)}} \\right)} \\right)} {\\partial_{1} {\\partial_{1} {φ}_{(0,0)}}} - \\bar{\\epsilon}^{2} {\\partial_{0} {\\partial_{0} {φ}_{(0,0)}}} - \\bar{\\epsilon}^{2} {\\partial_{1} {\\partial_{1} {φ}_{(0,0)}}}$"
],
"text/plain": [
- " 2 2 \n",
- " 3 φ_C ⋅α⋅atan(T_C⋅γ - T_eq⋅γ) 3⋅φ_C φ_C⋅α⋅atan(T_C⋅γ - T_eq⋅γ) φ_C\n",
- "φ_C - ─────────────────────────── - ────── + ────────────────────────── + ───\n",
- " π 2 π 2 \n",
- "\n",
- " \n",
- " 2 2 2 \n",
- " - \\bar{\\epsilon} ⋅δ ⋅cos (j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) \n",
- " \n",
- "\n",
- " \n",
- " 2 2 2 \n",
- "- \\bar{\\epsilon} ⋅δ ⋅cos (j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) -\n",
- " \n",
+ " 2 2 \n",
+ " 3 φ_C ⋅α⋅atan(T_C⋅γ - T_eq⋅γ) 3⋅φ_C φ_C⋅α⋅atan(T_C⋅γ - T_eq⋅γ) φ_C 2 2 2 \n",
+ "φ_C - ─────────────────────────── - ────── + ────────────────────────── + ─── - \\bar{\\epsilon} ⋅δ ⋅cos (j⋅θ₀ - j⋅atan2(D(\n",
+ " π 2 π 2 \n",
"\n",
- " \n",
- " 2 \n",
- " 2⋅\\bar{\\epsilon} ⋅δ⋅cos(j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) - \n",
- " \n",
+ " \n",
+ " 2 2 2 \n",
+ "φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) - \\bar{\\epsilon} ⋅δ ⋅cos (j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) - 2⋅\\bar{\\e\n",
+ " \n",
"\n",
- " \n",
- " 2 \n",
- "2⋅\\bar{\\epsilon} ⋅δ⋅cos(j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) - \\\n",
- " \n",
+ " \n",
+ " 2 2 \n",
+ "psilon} ⋅δ⋅cos(j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[0,0])))⋅D(D(φ[0,0])) - 2⋅\\bar{\\epsilon} ⋅δ⋅cos(j⋅θ₀ - j⋅atan2(D(φ[0,0]), D(φ[\n",
+ " \n",
"\n",
- " \n",
- " 2 2 \n",
- "bar{\\epsilon} ⋅D(D(φ[0,0])) - \\bar{\\epsilon} ⋅D(D(φ[0,0]))\n",
- " "
+ " \n",
+ " 2 2 \n",
+ "0,0])))⋅D(D(φ[0,0])) - \\bar{\\epsilon} ⋅D(D(φ[0,0])) - \\bar{\\epsilon} ⋅D(D(φ[0,0]))\n",
+ " "
]
},
"execution_count": 7,
@@ -246,15 +287,32 @@
"execution_count": 8,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_png function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_rgba function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The to_mask function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n",
+ "/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/IPython/lib/latextools.py:126: MatplotlibDeprecationWarning: \n",
+ "The MathtextBackendBitmap class was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use mathtext.math_to_image instead.\n",
+ " mt.to_png(f, s, fontsize=12, dpi=dpi, color=color)\n"
+ ]
+ },
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/latex": [
"$\\displaystyle \\left\\{ \\pi : 3.14159265358979, \\ T_{eq} : 1.0, \\ \\bar{\\epsilon} : 0.01, \\ j : 6, \\ α : 0.9, \\ γ : 10, \\ δ : 0.02, \\ θ_{0} : 0.2, \\ κ : 1.8, \\ τ : 0.0003\\right\\}$"
],
"text/plain": [
- "{π: 3.14159265358979, T_eq: 1.0, \\bar{\\epsilon}: 0.01, j: 6, α: 0.9, γ: 10, δ:\n",
- " 0.02, θ₀: 0.2, κ: 1.8, τ: 0.0003}"
+ "{π: 3.14159265358979, T_eq: 1.0, \\bar{\\epsilon}: 0.01, j: 6, α: 0.9, γ: 10, δ: 0.02, θ₀: 0.2, κ: 1.8, τ: 0.0003}"
]
},
"execution_count": 8,
@@ -358,6 +416,170 @@
" plt.colorbar()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ ".highlight .hll { background-color: #ffffcc }\n",
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+ ".highlight .cp { color: #BC7A00 } /* Comment.Preproc */\n",
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+ ".highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
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+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_T</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_phi</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_phi_temp</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_phidelta</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"cp\">#pragma omp parallel num_threads(4)</span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"cp\">#pragma omp for schedule(static)</span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">301</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_phi_10</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_phi</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">302</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_T_10</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_T</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">302</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_phi_11</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_phi</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">302</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">302</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_phi_1m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_phi</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">302</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">302</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_phidelta_10</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_phidelta</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">302</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_phi_temp_10</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_phi_temp</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">302</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"mi\">301</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">xi_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">pow</span><span class=\"p\">(</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">],</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">);</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">xi_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">954.92965855137209</span><span class=\"o\">*</span><span class=\"n\">atan</span><span class=\"p\">(</span><span class=\"mf\">-10.0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">10.0</span><span class=\"o\">*</span><span class=\"n\">_data_T_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]);</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">xi_2</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">-2222.2222222222222</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">xi_3</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">xi_2</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">1111.1111111111111</span><span class=\"o\">*</span><span class=\"n\">_data_phi_11</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">1111.1111111111111</span><span class=\"o\">*</span><span class=\"n\">_data_phi_1m1</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">xi_4</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">cos</span><span class=\"p\">(</span><span class=\"mf\">-1.2000000000000002</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">6.0</span><span class=\"o\">*</span><span class=\"n\">atan2</span><span class=\"p\">(</span><span class=\"mf\">-16.666666666666668</span><span class=\"o\">*</span><span class=\"n\">_data_phi_1m1</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">16.666666666666668</span><span class=\"o\">*</span><span class=\"n\">_data_phi_11</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">],</span><span class=\"w\"> </span><span class=\"mf\">-16.666666666666668</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">16.666666666666668</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">]));</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">xi_5</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">xi_4</span><span class=\"o\">*</span><span class=\"mf\">0.013333333333333336</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">xi_6</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">xi_2</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">1111.1111111111111</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">1111.1111111111111</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"n\">xi_7</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">0.00013333333333333334</span><span class=\"o\">*</span><span class=\"n\">pow</span><span class=\"p\">(</span><span class=\"n\">xi_4</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"mi\">2</span><span class=\"p\">);</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_phidelta_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">xi_0</span><span class=\"o\">*</span><span class=\"n\">xi_1</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">xi_0</span><span class=\"o\">*</span><span class=\"mf\">5000.0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">xi_1</span><span class=\"o\">*</span><span class=\"mf\">-1.0</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">xi_3</span><span class=\"o\">*</span><span class=\"n\">xi_5</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">xi_3</span><span class=\"o\">*</span><span class=\"n\">xi_7</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">xi_5</span><span class=\"o\">*</span><span class=\"n\">xi_6</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">xi_6</span><span class=\"o\">*</span><span class=\"n\">xi_7</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">3148.1481481481483</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mf\">3333.3333333333335</span><span class=\"o\">*</span><span class=\"n\">pow</span><span class=\"p\">(</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">],</span><span class=\"w\"> </span><span class=\"mi\">3</span><span class=\"p\">)</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">370.37037037037038</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">370.37037037037038</span><span class=\"o\">*</span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">370.37037037037038</span><span class=\"o\">*</span><span class=\"n\">_data_phi_11</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"mf\">370.37037037037038</span><span class=\"o\">*</span><span class=\"n\">_data_phi_1m1</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_phi_temp_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mf\">1.0000000000000001e-5</span><span class=\"o\">*</span><span class=\"n\">_data_phidelta_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_data_phi_10</span><span class=\"p\">[</span><span class=\"n\">ctr_0</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "</pre></div>\n"
+ ],
+ "text/plain": [
+ "FUNC_PREFIX void kernel(double * RESTRICT const _data_T, double * RESTRICT const _data_phi, double * RESTRICT _data_phi_temp, double * RESTRICT _data_phidelta)\n",
+ "{\n",
+ " #pragma omp parallel num_threads(4)\n",
+ " {\n",
+ " #pragma omp for schedule(static)\n",
+ " for (int64_t ctr_1 = 1; ctr_1 < 301; ctr_1 += 1)\n",
+ " {\n",
+ " double * RESTRICT _data_phi_10 = _data_phi + 302*ctr_1;\n",
+ " double * RESTRICT _data_T_10 = _data_T + 302*ctr_1;\n",
+ " double * RESTRICT _data_phi_11 = _data_phi + 302*ctr_1 + 302;\n",
+ " double * RESTRICT _data_phi_1m1 = _data_phi + 302*ctr_1 - 302;\n",
+ " double * RESTRICT _data_phidelta_10 = _data_phidelta + 302*ctr_1;\n",
+ " double * RESTRICT _data_phi_temp_10 = _data_phi_temp + 302*ctr_1;\n",
+ " for (int64_t ctr_0 = 1; ctr_0 < 301; ctr_0 += 1)\n",
+ " {\n",
+ " const double xi_0 = pow(_data_phi_10[ctr_0], 2);\n",
+ " const double xi_1 = 954.92965855137209*atan(-10.0 + 10.0*_data_T_10[ctr_0]);\n",
+ " const double xi_2 = -2222.2222222222222*_data_phi_10[ctr_0];\n",
+ " const double xi_3 = xi_2 + 1111.1111111111111*_data_phi_11[ctr_0] + 1111.1111111111111*_data_phi_1m1[ctr_0];\n",
+ " const double xi_4 = cos(-1.2000000000000002 + 6.0*atan2(-16.666666666666668*_data_phi_1m1[ctr_0] + 16.666666666666668*_data_phi_11[ctr_0], -16.666666666666668*_data_phi_10[ctr_0 - 1] + 16.666666666666668*_data_phi_10[ctr_0 + 1]));\n",
+ " const double xi_5 = xi_4*0.013333333333333336;\n",
+ " const double xi_6 = xi_2 + 1111.1111111111111*_data_phi_10[ctr_0 + 1] + 1111.1111111111111*_data_phi_10[ctr_0 - 1];\n",
+ " const double xi_7 = 0.00013333333333333334*pow(xi_4, 2);\n",
+ " _data_phidelta_10[ctr_0] = xi_0*xi_1 + xi_0*5000.0 + xi_1*-1.0*_data_phi_10[ctr_0] + xi_3*xi_5 + xi_3*xi_7 + xi_5*xi_6 + xi_6*xi_7 - 3148.1481481481483*_data_phi_10[ctr_0] - 3333.3333333333335*pow(_data_phi_10[ctr_0], 3) + 370.37037037037038*_data_phi_10[ctr_0 + 1] + 370.37037037037038*_data_phi_10[ctr_0 - 1] + 370.37037037037038*_data_phi_11[ctr_0] + 370.37037037037038*_data_phi_1m1[ctr_0];\n",
+ " _data_phi_temp_10[ctr_0] = 1.0000000000000001e-5*_data_phidelta_10[ctr_0] + _data_phi_10[ctr_0];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ps.show_code(φ_kernel)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 12,
@@ -371,14 +593,14 @@
"----------------------------------------------------\n",
" T| ( 0, 0)| ( 0, 0)\n",
" phi| ( 0, 1)| ( 0, 1)\n",
- "phi_temp| ( 0, 0)| ( 0, 0)\n",
- "phidelta| ( 0, 0)| ( 0, 0)\n",
+ "phi_temp| (nan,nan)| (nan,nan)\n",
+ "phidelta| (nan,nan)| (nan,nan)\n",
"\n"
]
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 1152x432 with 6 Axes>"
]
@@ -405,7 +627,7 @@
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type=\"video/mp4\">\n",
" Your browser does not support the video tag.\n",
"</video>"
],
@@ -439,7 +661,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -453,9 +675,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/doc/sphinx/kernel_compile_and_call.rst b/doc/sphinx/kernel_compile_and_call.rst
index 5293dc5a76957de33abb7fbb8526eaaf00d2472a..2ceab6123583c9543dad0597f63bb53609538d10 100644
--- a/doc/sphinx/kernel_compile_and_call.rst
+++ b/doc/sphinx/kernel_compile_and_call.rst
@@ -11,11 +11,11 @@ Creating kernels
.. autoclass:: pystencils.CreateKernelConfig
:members:
-.. autofunction:: pystencils.create_domain_kernel
+.. autofunction:: pystencils.kernelcreation.create_domain_kernel
-.. autofunction:: pystencils.create_indexed_kernel
+.. autofunction:: pystencils.kernelcreation.create_indexed_kernel
-.. autofunction:: pystencils.create_staggered_kernel
+.. autofunction:: pystencils.kernelcreation.create_staggered_kernel
Code printing
diff --git a/pystencils/__init__.py b/pystencils/__init__.py
index ba595db0f6636a34cb37286f4678f6efe3f80784..4b23e64c79dd4ad71bfbd8b34e3a17feb3683a91 100644
--- a/pystencils/__init__.py
+++ b/pystencils/__init__.py
@@ -3,13 +3,13 @@ from .enums import Backend, Target
from . import fd
from . import stencil as stencil
from .assignment import Assignment, assignment_from_stencil
-from .data_types import TypedSymbol
+from pystencils.typing.typed_sympy import TypedSymbol
from .datahandling import create_data_handling
from .display_utils import get_code_obj, get_code_str, show_code, to_dot
from .field import Field, FieldType, fields
+from .config import CreateKernelConfig
from .kernel_decorator import kernel, kernel_config
-from .kernelcreation import (
- CreateKernelConfig, create_domain_kernel, create_indexed_kernel, create_kernel, create_staggered_kernel)
+from .kernelcreation import create_kernel, create_staggered_kernel
from .simp import AssignmentCollection
from .slicing import make_slice
from .spatial_coordinates import x_, x_staggered, x_staggered_vector, x_vector, y_, y_staggered, z_, z_staggered
@@ -18,8 +18,8 @@ from .sympyextensions import SymbolCreator
__all__ = ['Field', 'FieldType', 'fields',
'TypedSymbol',
'make_slice',
- 'create_kernel', 'create_domain_kernel', 'create_indexed_kernel', 'create_staggered_kernel',
'CreateKernelConfig',
+ 'create_kernel', 'create_staggered_kernel',
'Target', 'Backend',
'show_code', 'to_dot', 'get_code_obj', 'get_code_str',
'AssignmentCollection',
diff --git a/pystencils/alignedarray.py b/pystencils/alignedarray.py
index da20a778e276586353a63d3e60e4cb672f19b017..26c3aa5ba90798b7e21b221951d5b75b36fdeaea 100644
--- a/pystencils/alignedarray.py
+++ b/pystencils/alignedarray.py
@@ -1,5 +1,5 @@
import numpy as np
-from pystencils.data_types import BasicType
+from pystencils.typing import numpy_name_to_c
def aligned_empty(shape, byte_alignment=True, dtype=np.float64, byte_offset=0, order='C', align_inner_coordinate=True):
@@ -21,7 +21,7 @@ def aligned_empty(shape, byte_alignment=True, dtype=np.float64, byte_offset=0, o
from pystencils.backends.simd_instruction_sets import (get_supported_instruction_sets, get_cacheline_size,
get_vector_instruction_set)
- type_name = BasicType.numpy_name_to_c(np.dtype(dtype).name)
+ type_name = numpy_name_to_c(np.dtype(dtype).name)
instruction_sets = get_supported_instruction_sets()
if instruction_sets is None:
byte_alignment = 64
diff --git a/pystencils/assignment.py b/pystencils/assignment.py
index 4e51cd4a7bfeefab253872f034b97cd4c2b48eb8..c3ae4b4367da32764224736ea55cbc2bd6acb219 100644
--- a/pystencils/assignment.py
+++ b/pystencils/assignment.py
@@ -10,16 +10,17 @@ def print_assignment_latex(printer, expr):
"""sympy cannot print Assignments as Latex. Thus, this function is added to the sympy Latex printer"""
printed_lhs = printer.doprint(expr.lhs)
printed_rhs = printer.doprint(expr.rhs)
- return r"{printed_lhs} \leftarrow {printed_rhs}".format(printed_lhs=printed_lhs, printed_rhs=printed_rhs)
+ return fr"{printed_lhs} \leftarrow {printed_rhs}"
def assignment_str(assignment):
- return r"{lhs} ↠{rhs}".format(lhs=assignment.lhs, rhs=assignment.rhs)
+ return fr"{assignment.lhs} ↠{assignment.rhs}"
_old_new = sp.codegen.ast.Assignment.__new__
+# TODO Typing Part2 add default type, defult_float_type, default_int_type and use sane defaults
def _Assignment__new__(cls, lhs, rhs, *args, **kwargs):
if isinstance(lhs, (list, tuple, sp.Matrix)) and isinstance(rhs, (list, tuple, sp.Matrix)):
assert len(lhs) == len(rhs), f'{lhs} and {rhs} must have same length when performing vector assignment!'
@@ -34,19 +35,6 @@ LatexPrinter._print_Assignment = print_assignment_latex
sp.MutableDenseMatrix.__hash__ = lambda self: hash(tuple(self))
-# Apparently, in SymPy 1.4 Assignment.__hash__ is not implemented. This has been fixed in current master
-try:
- sympy_version = sp.__version__.split('.')
-
- if int(sympy_version[0]) <= 1 and int(sympy_version[1]) <= 4:
- def hash_fun(self):
- return hash((self.lhs, self.rhs))
-
- Assignment.__hash__ = hash_fun
-except Exception:
- pass
-
-
def assignment_from_stencil(stencil_array, input_field, output_field,
normalization_factor=None, order='visual') -> Assignment:
"""Creates an assignment
diff --git a/pystencils/astnodes.py b/pystencils/astnodes.py
index 689a18b02d0397251aaa838929ec6b22be4296f5..ef0bcc6d758fdb67fc42b708038882360a9eee65 100644
--- a/pystencils/astnodes.py
+++ b/pystencils/astnodes.py
@@ -6,10 +6,10 @@ from typing import Any, List, Optional, Sequence, Set, Union
import sympy as sp
import pystencils
-from pystencils.data_types import TypedImaginaryUnit, TypedSymbol, cast_func, create_type
+from pystencils.typing.utilities import create_type, get_next_parent_of_type
from pystencils.enums import Target, Backend
from pystencils.field import Field
-from pystencils.kernelparameters import FieldPointerSymbol, FieldShapeSymbol, FieldStrideSymbol
+from pystencils.typing.typed_sympy import FieldPointerSymbol, FieldShapeSymbol, FieldStrideSymbol, TypedSymbol
from pystencils.sympyextensions import fast_subs
NodeOrExpr = Union['Node', sp.Expr]
@@ -294,6 +294,8 @@ class SkipIteration(Node):
class Block(Node):
def __init__(self, nodes: List[Node]):
super(Block, self).__init__()
+ if not isinstance(nodes, list):
+ nodes = [nodes]
self._nodes = nodes
self.parent = None
for n in self._nodes:
@@ -542,7 +544,6 @@ class LoopOverCoordinate(Node):
@property
def is_outermost_loop(self):
- from pystencils.transformations import get_next_parent_of_type
return get_next_parent_of_type(self, LoopOverCoordinate) is None
@property
@@ -571,7 +572,8 @@ class SympyAssignment(Node):
self.use_auto = use_auto
def __is_declaration(self):
- if isinstance(self._lhs_symbol, cast_func):
+ from pystencils.typing import CastFunc
+ if isinstance(self._lhs_symbol, CastFunc):
return False
if any(isinstance(self._lhs_symbol, c) for c in (Field.Access, sp.Indexed, TemporaryMemoryAllocation)):
return False
@@ -616,7 +618,6 @@ class SympyAssignment(Node):
if isinstance(symbol, Field.Access):
for i in range(len(symbol.offsets)):
loop_counters.add(LoopOverCoordinate.get_loop_counter_symbol(i))
- result = {r for r in result if not isinstance(r, TypedImaginaryUnit)}
result.update(loop_counters)
result.update(self._lhs_symbol.atoms(sp.Symbol))
return result
diff --git a/pystencils/backends/cbackend.py b/pystencils/backends/cbackend.py
index 8437bdb6801ecc5adc717dec2983e5d6eda0baf0..f425eef885203c6305e69fbdd56226dbf0b36aa9 100644
--- a/pystencils/backends/cbackend.py
+++ b/pystencils/backends/cbackend.py
@@ -8,14 +8,17 @@ import sympy as sp
from sympy.core import S
from sympy.core.cache import cacheit
from sympy.logic.boolalg import BooleanFalse, BooleanTrue
+from sympy.functions.elementary.trigonometric import TrigonometricFunction, InverseTrigonometricFunction
+from sympy.functions.elementary.hyperbolic import HyperbolicFunction
from pystencils.astnodes import KernelFunction, LoopOverCoordinate, Node
from pystencils.cpu.vectorization import vec_all, vec_any, CachelineSize
-from pystencils.data_types import (
- PointerType, VectorType, address_of, cast_func, create_type, get_type_of_expression,
- reinterpret_cast_func, vector_memory_access, BasicType, TypedSymbol)
+from pystencils.typing import (
+ PointerType, VectorType, CastFunc, create_type, get_type_of_expression,
+ ReinterpretCastFunc, VectorMemoryAccess, BasicType, TypedSymbol)
from pystencils.enums import Backend
from pystencils.fast_approximation import fast_division, fast_inv_sqrt, fast_sqrt
+from pystencils.functions import DivFunc, AddressOf
from pystencils.integer_functions import (
bit_shift_left, bit_shift_right, bitwise_and, bitwise_or, bitwise_xor,
int_div, int_power_of_2, modulo_ceil)
@@ -30,8 +33,6 @@ __all__ = ['generate_c', 'CustomCodeNode', 'PrintNode', 'get_headers', 'CustomSy
HEADER_REGEX = re.compile(r'^[<"].*[">]$')
-KERNCRAFT_NO_TERNARY_MODE = False
-
def generate_c(ast_node: Node,
signature_only: bool = False,
@@ -63,6 +64,7 @@ def generate_c(ast_node: Node,
printer = custom_backend
elif dialect == Backend.C:
try:
+ # TODO Vectorization Revamp: instruction_set should not be just slapped on ast
instruction_set = ast_node.instruction_set
except Exception:
instruction_set = None
@@ -125,7 +127,7 @@ def get_headers(ast_node: Node) -> Set[str]:
# --------------------------------------- Backend Specific Nodes -------------------------------------------------------
-
+# TODO future CustomCodeNode should not be backend specific move it elsewhere
class CustomCodeNode(Node):
def __init__(self, code, symbols_read, symbols_defined, parent=None):
super(CustomCodeNode, self).__init__(parent=parent)
@@ -219,7 +221,7 @@ class CBackend:
return getattr(self, method_name)(node)
raise NotImplementedError(f"{self.__class__.__name__} does not support node of type {node.__class__.__name__}")
- def _print_Type(self, node):
+ def _print_AbstractType(self, node):
return str(node)
def _print_KernelFunction(self, node):
@@ -274,9 +276,9 @@ class CBackend:
self.sympy_printer.doprint(node.lhs),
self.sympy_printer.doprint(node.rhs))
else:
- lhs_type = get_type_of_expression(node.lhs)
+ lhs_type = get_type_of_expression(node.lhs) # TOOD: this should have been typed
printed_mask = ""
- if type(lhs_type) is VectorType and isinstance(node.lhs, cast_func):
+ if type(lhs_type) is VectorType and isinstance(node.lhs, CastFunc):
arg, data_type, aligned, nontemporal, mask, stride = node.lhs.args
instr = 'storeU'
if aligned:
@@ -289,12 +291,12 @@ class CBackend:
self._vector_instruction_set['load' + instr[-1]].format('{0}', **self._kwargs),
'{1}', '{2}', **self._kwargs), **self._kwargs)
printed_mask = self.sympy_printer.doprint(mask)
- if data_type.base_type.base_name == 'double':
+ if data_type.base_type.c_name == 'double':
if self._vector_instruction_set['double'] == '__m256d':
printed_mask = f"_mm256_castpd_si256({printed_mask})"
elif self._vector_instruction_set['double'] == '__m128d':
printed_mask = f"_mm_castpd_si128({printed_mask})"
- elif data_type.base_type.base_name == 'float':
+ elif data_type.base_type.c_name == 'float':
if self._vector_instruction_set['float'] == '__m256':
printed_mask = f"_mm256_castps_si256({printed_mask})"
elif self._vector_instruction_set['float'] == '__m128':
@@ -302,7 +304,9 @@ class CBackend:
rhs_type = get_type_of_expression(node.rhs)
if type(rhs_type) is not VectorType:
- rhs = cast_func(node.rhs, VectorType(rhs_type))
+ raise ValueError(f'Cannot vectorize {node.rhs} of type {rhs_type} inside of the pretty printer! '
+ f'This should have happen earlier!')
+ # rhs = CastFunc(node.rhs, VectorType(rhs_type)) # Unknown width
else:
rhs = node.rhs
@@ -322,7 +326,7 @@ class CBackend:
if stride == 1:
offset = offset.subs({node.lhs.args[0].field.spatial_strides[0]: 1})
size = sp.Mul(*node.lhs.args[0].field.spatial_shape)
- element_size = 8 if data_type.base_type.base_name == 'double' else 4
+ element_size = 8 if data_type.base_type.c_name == 'double' else 4
size_cond = f"({offset} + {CachelineSize.symbol/element_size}) < {size}"
pre_code = f"if ({first_cond} && {size_cond}) " + "{\n\t" + \
self._vector_instruction_set['cachelineZero'].format(ptr, **self._kwargs) + ';\n}\n'
@@ -436,19 +440,15 @@ class CustomSympyPrinter(CCodePrinter):
def __init__(self):
super(CustomSympyPrinter, self).__init__()
- self._float_type = create_type("float32")
def _print_Pow(self, expr):
"""Don't use std::pow function, for small integer exponents, write as multiplication"""
if not expr.free_symbols:
- return self._typed_number(expr.evalf(17), get_type_of_expression(expr.base))
+ raise NotImplementedError("This pow should be simplified already?")
+ # return self._typed_number(expr.evalf(), get_type_of_expression(expr.base))
+ return super(CustomSympyPrinter, self)._print_Pow(expr)
- if expr.exp.is_integer and expr.exp.is_number and 0 < expr.exp < 8:
- return f"({self._print(sp.Mul(*[expr.base] * expr.exp, evaluate=False))})"
- elif expr.exp.is_integer and expr.exp.is_number and - 8 < expr.exp < 0:
- return f"1 / ({self._print(sp.Mul(*([expr.base] * -expr.exp), evaluate=False))})"
- else:
- return super(CustomSympyPrinter, self)._print_Pow(expr)
+ # TODO don't print ones in sp.Mul
def _print_Rational(self, expr):
"""Evaluate all rationals i.e. print 0.25 instead of 1.0/4.0"""
@@ -470,7 +470,7 @@ class CustomSympyPrinter(CCodePrinter):
else:
return f'fabs({self._print(expr.args[0])})'
- def _print_Type(self, node):
+ def _print_AbstractType(self, node):
return str(node)
def _print_Function(self, expr):
@@ -483,16 +483,28 @@ class CustomSympyPrinter(CCodePrinter):
}
if hasattr(expr, 'to_c'):
return expr.to_c(self._print)
- if isinstance(expr, reinterpret_cast_func):
+ if isinstance(expr, ReinterpretCastFunc):
arg, data_type = expr.args
return f"*(({self._print(PointerType(data_type, restrict=False))})(& {self._print(arg)}))"
- elif isinstance(expr, address_of):
+ elif isinstance(expr, AddressOf):
assert len(expr.args) == 1, "address_of must only have one argument"
return f"&({self._print(expr.args[0])})"
- elif isinstance(expr, cast_func):
+ elif isinstance(expr, CastFunc):
arg, data_type = expr.args
- if isinstance(arg, sp.Number) and arg.is_finite:
+ if arg.is_Number and not isinstance(arg, (sp.core.numbers.Infinity, sp.core.numbers.NegativeInfinity)):
return self._typed_number(arg, data_type)
+ elif isinstance(arg, (InverseTrigonometricFunction, TrigonometricFunction, HyperbolicFunction)) \
+ and data_type == BasicType('float32'):
+ known = self.known_functions[arg.__class__.__name__.lower()]
+ code = self._print(arg)
+ return code.replace(known, f"{known}f")
+ elif isinstance(arg, (sp.Pow, sp.exp)) and data_type == BasicType('float32'):
+ known = ['sqrt', 'cbrt', 'pow', 'exp']
+ code = self._print(arg)
+ for k in known:
+ if k in code:
+ return code.replace(k, f'{k}f')
+ raise ValueError(f"{code} doesn't give {known=} function back.")
else:
return f"(({data_type})({self._print(arg)}))"
elif isinstance(expr, fast_division):
@@ -505,8 +517,6 @@ class CustomSympyPrinter(CCodePrinter):
return f"({self._print(1 / sp.sqrt(expr.args[0]))})"
elif isinstance(expr, sp.Abs):
return f"abs({self._print(expr.args[0])})"
- elif isinstance(expr, sp.Max):
- return self._print(expr)
elif isinstance(expr, sp.Mod):
if expr.args[0].is_integer and expr.args[1].is_integer:
return f"({self._print(expr.args[0])} % {self._print(expr.args[1])})"
@@ -518,6 +528,8 @@ class CustomSympyPrinter(CCodePrinter):
return f"(1 << ({self._print(expr.args[0])}))"
elif expr.func == int_div:
return f"(({self._print(expr.args[0])}) / ({self._print(expr.args[1])}))"
+ elif expr.func == DivFunc:
+ return f'(({self._print(expr.divisor)}) / ({self._print(expr.dividend)}))'
else:
name = expr.name if hasattr(expr, 'name') else expr.__class__.__name__
arg_str = ', '.join(self._print(a) for a in expr.args)
@@ -540,52 +552,6 @@ class CustomSympyPrinter(CCodePrinter):
else:
return res
- def _print_Sum(self, expr):
- template = """[&]() {{
- {dtype} sum = ({dtype}) 0;
- for ( {iterator_dtype} {var} = {start}; {condition}; {var} += {increment} ) {{
- sum += {expr};
- }}
- return sum;
-}}()"""
- var = expr.limits[0][0]
- start = expr.limits[0][1]
- end = expr.limits[0][2]
- code = template.format(
- dtype=get_type_of_expression(expr.args[0]),
- iterator_dtype='int',
- var=self._print(var),
- start=self._print(start),
- end=self._print(end),
- expr=self._print(expr.function),
- increment=str(1),
- condition=self._print(var) + ' <= ' + self._print(end) # if start < end else '>='
- )
- return code
-
- def _print_Product(self, expr):
- template = """[&]() {{
- {dtype} product = ({dtype}) 1;
- for ( {iterator_dtype} {var} = {start}; {condition}; {var} += {increment} ) {{
- product *= {expr};
- }}
- return product;
-}}()"""
- var = expr.limits[0][0]
- start = expr.limits[0][1]
- end = expr.limits[0][2]
- code = template.format(
- dtype=get_type_of_expression(expr.args[0]),
- iterator_dtype='int',
- var=self._print(var),
- start=self._print(start),
- end=self._print(end),
- expr=self._print(expr.function),
- increment=str(1),
- condition=self._print(var) + ' <= ' + self._print(end) # if start < end else '>='
- )
- return code
-
def _print_ConditionalFieldAccess(self, node):
return self._print(sp.Piecewise((node.outofbounds_value, node.outofbounds_condition), (node.access, True)))
@@ -609,27 +575,6 @@ class CustomSympyPrinter(CCodePrinter):
return f"(({a} < {b}) ? {a} : {b})"
return inner_print_min(expr.args)
- def _print_re(self, expr):
- return f"real({self._print(expr.args[0])})"
-
- def _print_im(self, expr):
- return f"imag({self._print(expr.args[0])})"
-
- def _print_ImaginaryUnit(self, expr):
- return "complex<double>{0,1}"
-
- def _print_TypedImaginaryUnit(self, expr):
- if expr.dtype.numpy_dtype == np.complex64:
- return "complex<float>{0,1}"
- elif expr.dtype.numpy_dtype == np.complex128:
- return "complex<double>{0,1}"
- else:
- raise NotImplementedError(
- "only complex64 and complex128 supported")
-
- def _print_Complex(self, expr):
- return self._typed_number(expr, np.complex64)
-
# noinspection PyPep8Naming
class VectorizedCustomSympyPrinter(CustomSympyPrinter):
@@ -648,40 +593,94 @@ class VectorizedCustomSympyPrinter(CustomSympyPrinter):
return None
def _print_Abs(self, expr):
- if 'abs' in self.instruction_set and isinstance(expr.args[0], vector_memory_access):
+ if 'abs' in self.instruction_set and isinstance(expr.args[0], VectorMemoryAccess):
return self.instruction_set['abs'].format(self._print(expr.args[0]), **self._kwargs)
return super()._print_Abs(expr)
+ def _typed_vectorized_number(self, expr, data_type):
+ basic_data_type = data_type.base_type
+ number = self._typed_number(expr, basic_data_type)
+ instruction = 'makeVecConst'
+ if basic_data_type.is_bool():
+ instruction = 'makeVecConstBool'
+ # TODO Vectorization Revamp: is int, or sint, or uint (my guess is sint)
+ elif basic_data_type.is_int():
+ instruction = 'makeVecConstInt'
+ return self.instruction_set[instruction].format(number, **self._kwargs)
+
+ def _typed_vectorized_symbol(self, expr, data_type):
+ if not isinstance(expr, TypedSymbol):
+ raise ValueError(f'{expr} is not a TypeSymbol. It is {expr.type=}')
+ basic_data_type = data_type.base_type
+ symbol = self._print(expr)
+ if basic_data_type != expr.dtype:
+ symbol = f'(({basic_data_type})({symbol}))'
+
+ instruction = 'makeVecConst'
+ if basic_data_type.is_bool():
+ instruction = 'makeVecConstBool'
+ # TODO Vectorization Revamp: is int, or sint, or uint (my guess is sint)
+ elif basic_data_type.is_int():
+ instruction = 'makeVecConstInt'
+ return self.instruction_set[instruction].format(symbol, **self._kwargs)
+
+ def _print_CastFunc(self, expr):
+ arg, data_type = expr.args
+ if type(data_type) is VectorType:
+ # vector_memory_access is a cast_func itself so it should't be directly inside a cast_func
+ assert not isinstance(arg, VectorMemoryAccess)
+ if isinstance(arg, sp.Tuple):
+ is_boolean = get_type_of_expression(arg[0]) == create_type("bool")
+ is_integer = get_type_of_expression(arg[0]) == create_type("int")
+ printed_args = [self._print(a) for a in arg]
+ instruction = 'makeVecBool' if is_boolean else 'makeVecInt' if is_integer else 'makeVec'
+ if instruction == 'makeVecInt' and 'makeVecIndex' in self.instruction_set:
+ increments = np.array(arg)[1:] - np.array(arg)[:-1]
+ if len(set(increments)) == 1:
+ return self.instruction_set['makeVecIndex'].format(printed_args[0], increments[0],
+ **self._kwargs)
+ return self.instruction_set[instruction].format(*printed_args, **self._kwargs)
+ else:
+ if arg.is_Number and not isinstance(arg, (sp.core.numbers.Infinity, sp.core.numbers.NegativeInfinity)):
+ return self._typed_vectorized_number(arg, data_type)
+ elif isinstance(arg, TypedSymbol):
+ return self._typed_vectorized_symbol(arg, data_type)
+ elif isinstance(arg, (InverseTrigonometricFunction, TrigonometricFunction, HyperbolicFunction)) \
+ and data_type == BasicType('float32'):
+ raise NotImplementedError('Vectorizer is not tested for trigonometric functions yet')
+ # known = self.known_functions[arg.__class__.__name__.lower()]
+ # code = self._print(arg)
+ # return code.replace(known, f"{known}f")
+ elif isinstance(arg, sp.Pow) and data_type == BasicType('float32'):
+ raise NotImplementedError('Vectorizer cannot print casted aka. not double pow')
+ # known = ['sqrt', 'cbrt', 'pow']
+ # code = self._print(arg)
+ # for k in known:
+ # if k in code:
+ # return code.replace(k, f'{k}f')
+ # raise ValueError(f"{code} doesn't give {known=} function back.")
+ else:
+ raise NotImplementedError('Vectorizer cannot cast between different datatypes')
+ # to_type = self.instruction_set['suffix'][data_type.base_type.c_name]
+ # from_type = self.instruction_set['suffix'][get_type_of_expression(arg).base_type.c_name]
+ # return self.instruction_set['cast'].format(from_type, to_type, self._print(arg))
+ else:
+ return self._scalarFallback('_print_Function', expr)
+ # raise ValueError(f'Non VectorType cast "{data_type}" in vectorized code.')
+
def _print_Function(self, expr):
- if isinstance(expr, vector_memory_access):
+ if isinstance(expr, VectorMemoryAccess):
arg, data_type, aligned, _, mask, stride = expr.args
if stride != 1:
return self.instruction_set['loadS'].format(f"& {self._print(arg)}", stride, **self._kwargs)
instruction = self.instruction_set['loadA'] if aligned else self.instruction_set['loadU']
return instruction.format(f"& {self._print(arg)}", **self._kwargs)
- elif isinstance(expr, cast_func):
- arg, data_type = expr.args
- if type(data_type) is VectorType:
- # vector_memory_access is a cast_func itself so it should't be directly inside a cast_func
- assert not isinstance(arg, vector_memory_access)
- if isinstance(arg, sp.Tuple):
- is_boolean = get_type_of_expression(arg[0]) == create_type("bool")
- is_integer = get_type_of_expression(arg[0]) == create_type("int")
- printed_args = [self._print(a) for a in arg]
- instruction = 'makeVecBool' if is_boolean else 'makeVecInt' if is_integer else 'makeVec'
- if instruction == 'makeVecInt' and 'makeVecIndex' in self.instruction_set:
- increments = np.array(arg)[1:] - np.array(arg)[:-1]
- if len(set(increments)) == 1:
- return self.instruction_set['makeVecIndex'].format(printed_args[0], increments[0],
- **self._kwargs)
- return self.instruction_set[instruction].format(*printed_args, **self._kwargs)
- else:
- is_boolean = get_type_of_expression(arg) == create_type("bool")
- is_integer = get_type_of_expression(arg) == create_type("int") or \
- (isinstance(arg, TypedSymbol) and not isinstance(arg.dtype, VectorType) and arg.dtype.is_int())
- instruction = 'makeVecConstBool' if is_boolean else \
- 'makeVecConstInt' if is_integer else 'makeVecConst'
- return self.instruction_set[instruction].format(self._print(arg), **self._kwargs)
+ elif expr.func == DivFunc:
+ result = self._scalarFallback('_print_Function', expr)
+ if not result:
+ result = self.instruction_set['/'].format(self._print(expr.divisor), self._print(expr.dividend),
+ **self._kwargs)
+ return result
elif expr.func == fast_division:
result = self._scalarFallback('_print_Function', expr)
if not result:
@@ -747,12 +746,12 @@ class VectorizedCustomSympyPrinter(CustomSympyPrinter):
# special treatment for all-integer args, for loop index arithmetic until we have proper int vectorization
suffix = ""
- if all([(type(e) is cast_func and str(e.dtype) == self.instruction_set['int']) or isinstance(e, sp.Integer)
+ if all([(type(e) is CastFunc and str(e.dtype) == self.instruction_set['int']) or isinstance(e, sp.Integer)
or (type(e) is TypedSymbol and isinstance(e.dtype, BasicType) and e.dtype.is_int()) for e in args]):
- dtype = set([e.dtype for e in args if type(e) is cast_func])
+ dtype = set([e.dtype for e in args if type(e) is CastFunc])
assert len(dtype) == 1
dtype = dtype.pop()
- args = [cast_func(e, dtype) if (isinstance(e, sp.Integer) or isinstance(e, TypedSymbol)) else e
+ args = [CastFunc(e, dtype) if (isinstance(e, sp.Integer) or isinstance(e, TypedSymbol)) else e
for e in args]
suffix = "int"
@@ -784,19 +783,24 @@ class VectorizedCustomSympyPrinter(CustomSympyPrinter):
one = self.instruction_set['makeVecConst'].format(1.0, **self._kwargs)
- if expr.exp.is_integer and expr.exp.is_number and 0 < expr.exp < 8:
- return "(" + self._print(sp.Mul(*[expr.base] * expr.exp, evaluate=False)) + ")"
- elif expr.exp == -1:
+ if isinstance(expr.exp, CastFunc) and expr.exp.args[0].is_number:
+ exp = expr.exp.args[0]
+ else:
+ exp = expr.exp
+
+ if exp.is_integer and exp.is_number and 0 < exp < 8:
+ return "(" + self._print(sp.Mul(*[expr.base] * exp, evaluate=False)) + ")"
+ elif exp == -1:
one = self.instruction_set['makeVecConst'].format(1.0, **self._kwargs)
return self.instruction_set['/'].format(one, self._print(expr.base), **self._kwargs)
- elif expr.exp == 0.5:
+ elif exp == 0.5:
return self.instruction_set['sqrt'].format(self._print(expr.base), **self._kwargs)
- elif expr.exp == -0.5:
+ elif exp == -0.5:
root = self.instruction_set['sqrt'].format(self._print(expr.base), **self._kwargs)
return self.instruction_set['/'].format(one, root, **self._kwargs)
- elif expr.exp.is_integer and expr.exp.is_number and - 8 < expr.exp < 0:
+ elif exp.is_integer and exp.is_number and - 8 < exp < 0:
return self.instruction_set['/'].format(one,
- self._print(sp.Mul(*[expr.base] * (-expr.exp), evaluate=False)),
+ self._print(sp.Mul(*[expr.base] * (-exp), evaluate=False)),
**self._kwargs)
else:
raise ValueError("Generic exponential not supported: " + str(expr))
@@ -880,12 +884,9 @@ class VectorizedCustomSympyPrinter(CustomSympyPrinter):
result = self._print(expr.args[-1][0])
for true_expr, condition in reversed(expr.args[:-1]):
- if isinstance(condition, cast_func) and get_type_of_expression(condition.args[0]) == create_type("bool"):
- if not KERNCRAFT_NO_TERNARY_MODE:
- result = "(({}) ? ({}) : ({}))".format(self._print(condition.args[0]), self._print(true_expr),
- result, **self._kwargs)
- else:
- print("Warning - skipping ternary op")
+ if isinstance(condition, CastFunc) and get_type_of_expression(condition.args[0]) == create_type("bool"):
+ result = "(({}) ? ({}) : ({}))".format(self._print(condition.args[0]), self._print(true_expr),
+ result, **self._kwargs)
else:
# noinspection SpellCheckingInspection
result = self.instruction_set['blendv'].format(result, self._print(true_expr), self._print(condition),
diff --git a/pystencils/backends/cuda_backend.py b/pystencils/backends/cuda_backend.py
index 0c453f8893183735b6305cf48b941db09814796c..f8fdb16dac8911811915d6d69e5af7af8f149f4f 100644
--- a/pystencils/backends/cuda_backend.py
+++ b/pystencils/backends/cuda_backend.py
@@ -1,14 +1,8 @@
-from os.path import dirname, join
-
from pystencils.astnodes import Node
from pystencils.backends.cbackend import CBackend, CustomSympyPrinter, generate_c
from pystencils.enums import Backend
from pystencils.fast_approximation import fast_division, fast_inv_sqrt, fast_sqrt
-with open(join(dirname(__file__), 'cuda_known_functions.txt')) as f:
- lines = f.readlines()
- CUDA_KNOWN_FUNCTIONS = {l.strip(): l.strip() for l in lines if l}
-
def generate_cuda(ast_node: Node, signature_only: bool = False, custom_backend=None, with_globals=True) -> str:
"""Prints an abstract syntax tree node as CUDA code.
@@ -43,26 +37,13 @@ class CudaBackend(CBackend):
return code
@staticmethod
- def _print_ThreadBlockSynchronization(node):
- code = "__synchtreads();"
- return code
+ def _print_ThreadBlockSynchronization(_):
+ return "__synchtreads();"
def _print_TextureDeclaration(self, node):
-
- # TODO: use fStrings here
- if node.texture.field.dtype.numpy_dtype.itemsize > 4:
- code = "texture<fp_tex_%s, cudaTextureType%iD, cudaReadModeElementType> %s;" % (
- str(node.texture.field.dtype),
- node.texture.field.spatial_dimensions,
- node.texture
- )
- else:
- code = "texture<%s, cudaTextureType%iD, cudaReadModeElementType> %s;" % (
- str(node.texture.field.dtype),
- node.texture.field.spatial_dimensions,
- node.texture
- )
- return code
+ cond = node.texture.field.dtype.numpy_dtype.itemsize > 4
+ return f'texture<{"fp_tex_" if cond else ""}{str(node.texture.field.dtype)}, ' \
+ f'cudaTextureType{node.texture.field.spacial_dimensions}D, cudaReadModeElementType> {node.texture};'
def _print_SkipIteration(self, _):
return "return;"
@@ -73,7 +54,6 @@ class CudaSympyPrinter(CustomSympyPrinter):
def __init__(self):
super(CudaSympyPrinter, self).__init__()
- self.known_functions.update(CUDA_KNOWN_FUNCTIONS)
def _print_Function(self, expr):
if isinstance(expr, fast_division):
diff --git a/pystencils/backends/cuda_known_functions.txt b/pystencils/backends/cuda_known_functions.txt
deleted file mode 100644
index 224f4a49d65322446a6b17c696351f9371435887..0000000000000000000000000000000000000000
--- a/pystencils/backends/cuda_known_functions.txt
+++ /dev/null
@@ -1,294 +0,0 @@
-__prof_trigger
-printf
-
-__syncthreads
-__syncthreads_count
-__syncthreads_and
-__syncthreads_or
-__syncwarp
-__threadfence
-__threadfence_block
-__threadfence_system
-
-atomicAdd
-atomicSub
-atomicExch
-atomicMin
-atomicMax
-atomicInc
-atomicDec
-atomicAnd
-atomicOr
-atomicXor
-atomicCAS
-
-__all_sync
-__any_sync
-__ballot_sync
-__active_mask
-
-__shfl_sync
-__shfl_up_sync
-__shfl_down_sync
-__shfl_xor_sync
-
-__match_any_sync
-__match_all_sync
-
-__isGlobal
-__isShared
-__isConstant
-__isLocal
-
-tex1Dfetch
-tex1D
-tex2D
-tex3D
-
-sqrtf
-rsqrtf
-cbrtf
-rcbrtf
-hypotf
-rhypotf
-norm3df
-rnorm3df
-norm4df
-rnorm4df
-normf
-rnormf
-expf
-exp2f
-exp10f
-expm1f
-logf
-log2f
-log10f
-log1pf
-sinf
-cosf
-tanf
-sincosf
-sinpif
-cospif
-sincospif
-asinf
-acosf
-atanf
-atan2f
-sinhf
-coshf
-tanhf
-asinhf
-acoshf
-atanhf
-powf
-erff
-erfcf
-erfinvf
-erfcinvf
-erfcxf
-normcdff
-normcdfinvf
-lgammaf
-tgammaf
-fmaf
-frexpf
-ldexpf
-scalbnf
-scalblnf
-logbf
-ilogbf
-j0f
-j1f
-jnf
-y0f
-y1f
-ynf
-cyl_bessel_i0f
-cyl_bessel_i1f
-fmodf
-remainderf
-remquof
-modff
-fdimf
-truncf
-roundf
-rintf
-nearbyintf
-ceilf
-floorf
-lrintf
-lroundf
-llrintf
-llroundf
-
-sqrt
-rsqrt
-cbrt
-rcbrt
-hypot
-rhypot
-norm3d
-rnorm3d
-norm4d
-rnorm4d
-norm
-rnorm
-exp
-exp2
-exp10
-expm1
-log
-log2
-log10
-log1p
-sin
-cos
-tan
-sincos
-sinpi
-cospi
-sincospi
-asin
-acos
-atan
-atan2
-sinh
-cosh
-tanh
-asinh
-acosh
-atanh
-pow
-erf
-erfc
-erfinv
-erfcinv
-erfcx
-normcdf
-normcdfinv
-lgamma
-tgamma
-fma
-frexp
-ldexp
-scalbn
-scalbln
-logb
-ilogb
-j0
-j1
-jn
-y0
-y1
-yn
-cyl_bessel_i0
-cyl_bessel_i1
-fmod
-remainder
-remquo
-mod
-fdim
-trunc
-round
-rint
-nearbyint
-ceil
-floor
-lrint
-lround
-llrint
-llround
-
-__fdividef
-__sinf
-__cosf
-__tanf
-__sincosf
-__logf
-__log2f
-__log10f
-__expf
-__exp10f
-__powf
-
-__fadd_rn
-__fsub_rn
-__fmul_rn
-__fmaf_rn
-__frcp_rn
-__fsqrt_rn
-__frsqrt_rn
-__fdiv_rn
-
-__fadd_rz
-__fsub_rz
-__fmul_rz
-__fmaf_rz
-__frcp_rz
-__fsqrt_rz
-__frsqrt_rz
-__fdiv_rz
-
-__fadd_ru
-__fsub_ru
-__fmul_ru
-__fmaf_ru
-__frcp_ru
-__fsqrt_ru
-__frsqrt_ru
-__fdiv_ru
-
-__fadd_rd
-__fsub_rd
-__fmul_rd
-__fmaf_rd
-__frcp_rd
-__fsqrt_rd
-__frsqrt_rd
-__fdiv_rd
-
-__fdividef
-__expf
-__exp10f
-__logf
-__log2f
-__log10f
-__sinf
-__cosf
-__sincosf
-__tanf
-__powf
-
-__dadd_rn
-__dsub_rn
-__dmul_rn
-__fma_rn
-__ddiv_rn
-__drcp_rn
-__dsqrt_rn
-
-__dadd_rz
-__dsub_rz
-__dmul_rz
-__fma_rz
-__ddiv_rz
-__drcp_rz
-__dsqrt_rz
-
-__dadd_ru
-__dsub_ru
-__dmul_ru
-__fma_ru
-__ddiv_ru
-__drcp_ru
-__dsqrt_ru
-
-__dadd_rd
-__dsub_rd
-__dmul_rd
-__fma_rd
-__ddiv_rd
-__drcp_rd
-__dsqrt_rd
diff --git a/pystencils/backends/simd_instruction_sets.py b/pystencils/backends/simd_instruction_sets.py
index 8ac0beeb7d6f0c099b667bd752e7d24764607ebc..f2df619639ac8fe2352f4d61bd37a6841defe406 100644
--- a/pystencils/backends/simd_instruction_sets.py
+++ b/pystencils/backends/simd_instruction_sets.py
@@ -98,12 +98,13 @@ def get_cacheline_size(instruction_set):
return _cachelinesize
import pystencils as ps
+ from pystencils.astnodes import SympyAssignment
import numpy as np
from pystencils.cpu.vectorization import CachelineSize
arr = np.zeros((1, 1), dtype=np.float32)
f = ps.Field.create_from_numpy_array('f', arr, index_dimensions=0)
- ass = [CachelineSize(), ps.Assignment(f.center, CachelineSize.symbol)]
+ ass = [CachelineSize(), SympyAssignment(f.center, CachelineSize.symbol)]
ast = ps.create_kernel(ass, cpu_vectorize_info={'instruction_set': instruction_set})
kernel = ast.compile()
kernel(**{f.name: arr, CachelineSize.symbol.name: 0})
diff --git a/pystencils/backends/x86_instruction_sets.py b/pystencils/backends/x86_instruction_sets.py
index f72b48266195dd1a30149325e5949723a6b9ac7e..7653c7c69cbfef34a06714bb19b8d7976f53400f 100644
--- a/pystencils/backends/x86_instruction_sets.py
+++ b/pystencils/backends/x86_instruction_sets.py
@@ -51,7 +51,7 @@ def get_vector_instruction_set_x86(data_type='double', instruction_set='avx'):
'makeVecConstBool': 'set[]',
'makeVecInt': 'set[]',
'makeVecConstInt': 'set[]',
-
+
'loadU': 'loadu[0]',
'loadA': 'load[0]',
'storeU': 'storeu[0,1]',
@@ -93,7 +93,6 @@ def get_vector_instruction_set_x86(data_type='double', instruction_set='avx'):
("float", "avx512"): 16,
("int", "avx512"): 16,
}
-
result = {
'width': width[(data_type, instruction_set)],
'intwidth': width[('int', instruction_set)],
@@ -114,11 +113,6 @@ def get_vector_instruction_set_x86(data_type='double', instruction_set='avx'):
mask_suffix = '_mask' if instruction_set == 'avx512' and intrinsic_id in comparisons.keys() else ''
result[intrinsic_id] = pre + "_" + name + "_" + suf + mask_suffix + arg_string
- result['dataTypePrefix'] = {
- 'double': "_" + pre + 'd',
- 'float': "_" + pre,
- }
-
bit_width = result['width'] * (64 if data_type == 'double' else 32)
result['double'] = f"__m{bit_width}d"
result['float'] = f"__m{bit_width}"
diff --git a/pystencils/bit_masks.py b/pystencils/bit_masks.py
index 0fab63b25402cd54fb631a8d7a1ff2411c7fdb42..f8b6b7ef0361cf3555ce67375a34328ef2e1157c 100644
--- a/pystencils/bit_masks.py
+++ b/pystencils/bit_masks.py
@@ -1,5 +1,5 @@
import sympy as sp
-from pystencils.data_types import get_type_of_expression
+# from pystencils.typing import get_type_of_expression
# noinspection PyPep8Naming
@@ -22,13 +22,14 @@ class flag_cond(sp.Function):
def __new__(cls, flag_bit, mask_expression, *expressions):
- flag_dtype = get_type_of_expression(flag_bit)
- if not flag_dtype.is_int():
- raise ValueError('Argument flag_bit must be of integer type.')
-
- mask_dtype = get_type_of_expression(mask_expression)
- if not mask_dtype.is_int():
- raise ValueError('Argument mask_expression must be of integer type.')
+ # TODO Jan reintroduce checking
+ # flag_dtype = get_type_of_expression(flag_bit)
+ # if not flag_dtype.is_int():
+ # raise ValueError('Argument flag_bit must be of integer type.')
+ #
+ # mask_dtype = get_type_of_expression(mask_expression)
+ # if not mask_dtype.is_int():
+ # raise ValueError('Argument mask_expression must be of integer type.')
return super().__new__(cls, flag_bit, mask_expression, *expressions)
diff --git a/pystencils/boundaries/boundaryconditions.py b/pystencils/boundaries/boundaryconditions.py
index dc01224d02a04fd466c4dda6000acb87326a7706..65243177dafe6dbce44cfb14bf7f1eb5c53fa39c 100644
--- a/pystencils/boundaries/boundaryconditions.py
+++ b/pystencils/boundaries/boundaryconditions.py
@@ -1,8 +1,8 @@
from typing import Any, List, Tuple
-from pystencils import Assignment
+from pystencils.astnodes import SympyAssignment
from pystencils.boundaries.boundaryhandling import BoundaryOffsetInfo
-from pystencils.data_types import create_type
+from pystencils.typing import create_type
class Boundary:
@@ -14,7 +14,7 @@ class Boundary:
def __init__(self, name=None):
self._name = name
- def __call__(self, field, direction_symbol, index_field) -> List[Assignment]:
+ def __call__(self, field, direction_symbol, index_field) -> List[SympyAssignment]:
"""Defines the boundary behavior and must therefore be implemented by all boundaries.
Here the boundary is defined as a list of sympy assignments, from which a boundary kernel is generated.
@@ -63,13 +63,13 @@ class Neumann(Boundary):
neighbor = BoundaryOffsetInfo.offset_from_dir(direction_symbol, field.spatial_dimensions)
if field.index_dimensions == 0:
- return [Assignment(field.center, field[neighbor])]
+ return [SympyAssignment(field.center, field[neighbor])]
else:
from itertools import product
if not field.has_fixed_index_shape:
raise NotImplementedError("Neumann boundary works only for fields with fixed index shape")
index_iter = product(*(range(i) for i in field.index_shape))
- return [Assignment(field(*idx), field[neighbor](*idx)) for idx in index_iter]
+ return [SympyAssignment(field(*idx), field[neighbor](*idx)) for idx in index_iter]
def __hash__(self):
# All boundaries of these class behave equal -> should also be equal
@@ -103,11 +103,11 @@ class Dirichlet(Boundary):
def __call__(self, field, direction_symbol, index_field, **kwargs):
if field.index_dimensions == 0:
- return [Assignment(field.center, index_field("value") if self.additional_data else self._value)]
+ return [SympyAssignment(field.center, index_field("value") if self.additional_data else self._value)]
elif field.index_dimensions == 1:
assert not self.additional_data
if not field.has_fixed_index_shape:
raise NotImplementedError("Field needs fixed index shape")
assert len(self._value) == field.index_shape[0], "Dirichlet value does not match index shape of field"
- return [Assignment(field(i), self._value[i]) for i in range(field.index_shape[0])]
+ return [SympyAssignment(field(i), self._value[i]) for i in range(field.index_shape[0])]
raise NotImplementedError("Dirichlet boundary not implemented for fields with more than one index dimension")
diff --git a/pystencils/boundaries/boundaryhandling.py b/pystencils/boundaries/boundaryhandling.py
index 5705d3d53ad4941137e59819383c8d606e49afb2..2be86510ede07d50d2abfb4868ecf92157bb5c6d 100644
--- a/pystencils/boundaries/boundaryhandling.py
+++ b/pystencils/boundaries/boundaryhandling.py
@@ -1,16 +1,17 @@
+from functools import lru_cache
+
import numpy as np
import sympy as sp
from pystencils import create_kernel, CreateKernelConfig, Target
-from pystencils.assignment import Assignment
+from pystencils.astnodes import SympyAssignment
from pystencils.backends.cbackend import CustomCodeNode
from pystencils.boundaries.createindexlist import (
create_boundary_index_array, numpy_data_type_for_boundary_object)
-from pystencils.cache import memorycache
-from pystencils.data_types import TypedSymbol, create_type
+from pystencils.typing import TypedSymbol, create_type
from pystencils.datahandling.pycuda import PyCudaArrayHandler
from pystencils.field import Field
-from pystencils.kernelparameters import FieldPointerSymbol
+from pystencils.typing.typed_sympy import FieldPointerSymbol
try:
# noinspection PyPep8Naming
@@ -378,15 +379,15 @@ class BoundaryDataSetter:
assert coord < self.dim
return self.index_array[self.coord_map[coord]] + self.offset[coord] - self.ghost_layers + 0.5
- @memorycache()
+ @lru_cache()
def link_offsets(self):
return self.stencil[self.index_array['dir']]
- @memorycache()
+ @lru_cache()
def link_positions(self, coord):
return self.non_boundary_cell_positions(coord) + 0.5 * self.link_offsets()[:, coord]
- @memorycache()
+ @lru_cache()
def boundary_cell_positions(self, coord):
return self.non_boundary_cell_positions(coord) + self.link_offsets()[:, coord]
@@ -423,29 +424,29 @@ class BoundaryOffsetInfo(CustomCodeNode):
code = "\n"
for i in range(dim):
offset_str = ", ".join([str(d[i]) for d in stencil])
- code += "const int64_t %s [] = { %s };\n" % (offset_sym[i].name, offset_str)
+ code += "const int32_t %s [] = { %s };\n" % (offset_sym[i].name, offset_str)
inv_dirs = []
for direction in stencil:
inverse_dir = tuple([-i for i in direction])
inv_dirs.append(str(stencil.index(inverse_dir)))
- code += "const int64_t %s [] = { %s };\n" % (self.INV_DIR_SYMBOL.name, ", ".join(inv_dirs))
+ code += "const int32_t %s [] = { %s };\n" % (self.INV_DIR_SYMBOL.name, ", ".join(inv_dirs))
offset_symbols = BoundaryOffsetInfo._offset_symbols(dim)
super(BoundaryOffsetInfo, self).__init__(code, symbols_read=set(),
symbols_defined=set(offset_symbols + [self.INV_DIR_SYMBOL]))
@staticmethod
def _offset_symbols(dim):
- return [TypedSymbol(f"c{d}", create_type(np.int64)) for d in ['x', 'y', 'z'][:dim]]
+ return [TypedSymbol(f"c{d}", create_type(np.int32)) for d in ['x', 'y', 'z'][:dim]]
- INV_DIR_SYMBOL = TypedSymbol("invdir", np.int64)
+ INV_DIR_SYMBOL = TypedSymbol("invdir", np.int32)
def create_boundary_kernel(field, index_field, stencil, boundary_functor, target=Target.CPU, **kernel_creation_args):
elements = [BoundaryOffsetInfo(stencil)]
- dir_symbol = TypedSymbol("dir", np.int64)
- elements += [Assignment(dir_symbol, index_field[0]('dir'))]
+ dir_symbol = TypedSymbol("dir", np.int32)
+ elements += [SympyAssignment(dir_symbol, index_field[0]('dir'))]
elements += boundary_functor(field, direction_symbol=dir_symbol, index_field=index_field)
config = CreateKernelConfig(index_fields=[index_field], target=target, **kernel_creation_args)
return create_kernel(elements, config=config)
diff --git a/pystencils/boundaries/createindexlist.py b/pystencils/boundaries/createindexlist.py
index be8fee7e5aaee82f5515dad0e9eb33005a958b1c..8619a31d6646ea7b5ce97500f0478388c0e2bcca 100644
--- a/pystencils/boundaries/createindexlist.py
+++ b/pystencils/boundaries/createindexlist.py
@@ -25,12 +25,13 @@ except ImportError:
boundary_index_array_coordinate_names = ["x", "y", "z"]
direction_member_name = "dir"
+default_index_array_dtype = np.int32
def numpy_data_type_for_boundary_object(boundary_object, dim):
coordinate_names = boundary_index_array_coordinate_names[:dim]
- return np.dtype([(name, np.int32) for name in coordinate_names]
- + [(direction_member_name, np.int32)]
+ return np.dtype([(name, default_index_array_dtype) for name in coordinate_names]
+ + [(direction_member_name, default_index_array_dtype)]
+ [(i[0], i[1].numpy_dtype) for i in boundary_object.additional_data], align=True)
@@ -45,7 +46,8 @@ def _create_index_list_python(flag_field_arr, boundary_mask,
nr_of_ghost_layers = 0
coordinate_names = boundary_index_array_coordinate_names[:len(flag_field_arr.shape)]
- index_arr_dtype = np.dtype([(name, np.int32) for name in coordinate_names] + [(direction_member_name, np.int32)])
+ index_arr_dtype = np.dtype([(name, default_index_array_dtype) for name in coordinate_names]
+ + [(direction_member_name, default_index_array_dtype)])
# boundary cells are extracted via np.where. To ensure continous memory access in the compute kernel these cells
# have to be sorted.
@@ -117,9 +119,10 @@ def create_boundary_index_list(flag_field, stencil, boundary_mask, fluid_mask,
"""
dim = len(flag_field.shape)
coordinate_names = boundary_index_array_coordinate_names[:dim]
- index_arr_dtype = np.dtype([(name, np.int32) for name in coordinate_names] + [(direction_member_name, np.int32)])
+ index_arr_dtype = np.dtype([(name, default_index_array_dtype) for name in coordinate_names]
+ + [(direction_member_name, default_index_array_dtype)])
- stencil = np.array(stencil, dtype=np.int32)
+ stencil = np.array(stencil, dtype=default_index_array_dtype)
args = (flag_field, nr_of_ghost_layers, boundary_mask, fluid_mask, stencil, single_link)
args_no_gl = (flag_field, boundary_mask, fluid_mask, stencil, single_link)
diff --git a/pystencils/boundaries/inkernel.py b/pystencils/boundaries/inkernel.py
index 1d78814db6fadcc8a161353638f4dc61be36c0e4..479f30d2269b2ed5ca7e8c6c0163b494309f218a 100644
--- a/pystencils/boundaries/inkernel.py
+++ b/pystencils/boundaries/inkernel.py
@@ -1,7 +1,7 @@
import sympy as sp
from pystencils.boundaries.boundaryhandling import DEFAULT_FLAG_TYPE
-from pystencils.data_types import TypedSymbol, create_type
+from pystencils.typing import TypedSymbol, create_type
from pystencils.field import Field
from pystencils.integer_functions import bitwise_and
diff --git a/pystencils/cache.py b/pystencils/cache.py
index d8988f48bc6973f72f987ca361c08b030ce34dec..34db1d6583b3eeeabd583c9af6f59225a59ee742 100644
--- a/pystencils/cache.py
+++ b/pystencils/cache.py
@@ -3,10 +3,7 @@ from collections.abc import Hashable
from functools import partial, wraps
from itertools import chain
-try:
- from functools import lru_cache as memorycache
-except ImportError:
- from backports.functools_lru_cache import lru_cache as memorycache
+from functools import lru_cache as memorycache
from joblib import Memory
from appdirs import user_cache_dir
diff --git a/pystencils/config.py b/pystencils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef7f3b17de7c0867a99e85d9e73c7795f37f6ec8
--- /dev/null
+++ b/pystencils/config.py
@@ -0,0 +1,160 @@
+import warnings
+from copy import copy
+from collections import defaultdict
+from dataclasses import dataclass, field
+from types import MappingProxyType
+from typing import Union, Tuple, List, Dict, Callable, Any
+
+from pystencils import Target, Backend, Field
+from pystencils.typing.typed_sympy import BasicType
+
+import numpy as np
+
+
+# TODO: CreateKernelConfig is bloated think of more classes better usage, factory whatever ...
+# Proposition: CreateKernelConfigs Classes for different targets?
+@dataclass
+class CreateKernelConfig:
+ """
+ **Below all parameters for the CreateKernelConfig are explained**
+ """
+ target: Target = Target.CPU
+ """
+ All targets are defined in :class:`pystencils.enums.Target`
+ """
+ backend: Backend = None
+ """
+ All backends are defined in :class:`pystencils.enums.Backend`
+ """
+ function_name: str = 'kernel'
+ """
+ Name of the generated function - only important if generated code is written out
+ """
+ # TODO Sane defaults: config should check that the datatype is a Numpy type
+ # TODO Sane defaults: QoL default_number_float and default_number_int should be data_type if they are not specified
+ data_type: Union[str, Dict[str, BasicType]] = 'float64'
+ """
+ Data type used for all untyped symbols (i.e. non-fields), can also be a dict from symbol name to type
+ """
+ default_number_float: Union[str, np.dtype, BasicType] = 'float64'
+ """
+ Data type used for all untyped floating point numbers (i.e. 0.5)
+ """
+ default_number_int: Union[str, np.dtype, BasicType] = 'int64'
+ """
+ Data type used for all untyped integer numbers (i.e. 1)
+ """
+ iteration_slice: Tuple = None
+ """
+ Rectangular subset to iterate over, if not specified the complete non-ghost layer part of the field is iterated over
+ """
+ ghost_layers: Union[bool, int, List[Tuple[int]]] = None
+ """
+ A single integer specifies the ghost layer count at all borders, can also be a sequence of
+ pairs ``[(x_lower_gl, x_upper_gl), .... ]``. These layers are excluded from the iteration.
+ If left to default, the number of ghost layers is determined automatically from the assignments.
+ """
+ cpu_openmp: Union[bool, int] = False
+ """
+ `True` or number of threads for OpenMP parallelization, `False` for no OpenMP. If set to `True`, the maximum number
+ of available threads will be chosen.
+ """
+ cpu_vectorize_info: Dict = None
+ """
+ A dictionary with keys, 'vector_instruction_set', 'assume_aligned' and 'nontemporal'
+ for documentation of these parameters see vectorize function. Example:
+ '{'instruction_set': 'avx512', 'assume_aligned': True, 'nontemporal':True}'
+ """
+ cpu_blocking: Tuple[int] = None
+ """
+ A tuple of block sizes or `None` if no blocking should be applied
+ """
+ omp_single_loop: bool = True
+ """
+ If OpenMP is active: whether multiple outer loops are permitted
+ """
+ gpu_indexing: str = 'block'
+ """
+ Either 'block' or 'line' , or custom indexing class, see `pystencils.gpucuda.AbstractIndexing`
+ """
+ gpu_indexing_params: MappingProxyType = field(default=MappingProxyType({}))
+ """
+ Dict with indexing parameters (constructor parameters of indexing class)
+ e.g. for 'block' one can specify '{'block_size': (20, 20, 10) }'.
+ """
+ # TODO Markus rework this docstring
+ default_assignment_simplifications: bool = False
+ """
+ If `True` default simplifications are first performed on the Assignments. If problems occur during the
+ simplification a warning will be thrown.
+ Furthermore, it is essential to know that this is a two-stage process. The first stage of the process acts
+ on the level of the `pystencils.AssignmentCollection`. In this part,
+ `pystencil.simp.create_simplification_strategy` from pystencils.simplificationfactory will be used to
+ apply optimisations like insertion of constants to
+ remove pressure from the registers. Thus the first part of the optimisations can only be executed if
+ an `AssignmentCollection` is passed. The second part of the optimisation acts on the level of each Assignment
+ individually. In this stage, all optimisations from `sympy.codegen.rewriting.optims_c99` are applied
+ to each Assignment. Thus this stage can also be applied if a list of Assignments is passed.
+ """
+ cpu_prepend_optimizations: List[Callable] = field(default_factory=list)
+ """
+ List of extra optimizations to perform first on the AST.
+ """
+ use_auto_for_assignments: bool = False
+ """
+ If set to `True`, auto can be used in the generated code for data types. This makes the type system more robust.
+ """
+ index_fields: List[Field] = None
+ """
+ List of index fields, i.e. 1D fields with struct data type. If not `None`, `create_index_kernel`
+ instead of `create_domain_kernel` is used.
+ """
+ coordinate_names: Tuple[str, Any] = ('x', 'y', 'z')
+ """
+ Name of the coordinate fields in the struct data type.
+ """
+ allow_double_writes: bool = False
+ """
+ If True, don't check if every field is only written at a single location. This is required
+ for example for kernels that are compiled with loop step sizes > 1, that handle multiple
+ cells at once. Use with care!
+ """
+ skip_independence_check: bool = False
+ """
+ Don't check that loop iterations are independent. This is needed e.g. for
+ periodicity kernel, that access the field outside the iteration bounds. Use with care!
+ """
+
+ class DataTypeFactory:
+ """Because of pickle, we need to have a nested class, instead of a lambda in __post_init__"""
+ def __init__(self, dt):
+ self.dt = dt
+
+ def __call__(self):
+ return BasicType(self.dt)
+
+ def __post_init__(self):
+ # ---- Legacy parameters
+ # TODO Sane defaults: Check for abmigous types like "float", python float, which are dangerous for users
+ if isinstance(self.target, str):
+ new_target = Target[self.target.upper()]
+ warnings.warn(f'Target "{self.target}" as str is deprecated. Use {new_target} instead',
+ category=DeprecationWarning)
+ self.target = new_target
+ # ---- Auto Backend
+ if not self.backend:
+ if self.target == Target.CPU:
+ self.backend = Backend.C
+ elif self.target == Target.GPU:
+ self.backend = Backend.CUDA
+ else:
+ raise NotImplementedError(f'Target {self.target} has no default backend')
+
+ # Normalise data types
+ if not isinstance(self.data_type, dict):
+ dt = copy(self.data_type) # The copy is necessary because BasicType has sympy shinanigans
+ self.data_type = defaultdict(self.DataTypeFactory(dt))
+ if not isinstance(self.default_number_float, BasicType):
+ self.default_number_float = BasicType(self.default_number_float)
+ if not isinstance(self.default_number_int, BasicType):
+ self.default_number_int = BasicType(self.default_number_int)
diff --git a/pystencils/cpu/cpujit.py b/pystencils/cpu/cpujit.py
index 240cddd495fcbcb491bc313b5dc5abf526428622..ca4f267944de80d45e1754ca6d32cf7741b7000a 100644
--- a/pystencils/cpu/cpujit.py
+++ b/pystencils/cpu/cpujit.py
@@ -60,7 +60,7 @@ from appdirs import user_cache_dir, user_config_dir
from pystencils import FieldType
from pystencils.astnodes import LoopOverCoordinate
from pystencils.backends.cbackend import generate_c, get_headers, CFunction
-from pystencils.data_types import cast_func, VectorType, vector_memory_access
+from pystencils.typing import CastFunc, VectorType, VectorMemoryAccess
from pystencils.include import get_pystencils_include_path
from pystencils.kernel_wrapper import KernelWrapper
from pystencils.utils import atomic_file_write, recursive_dict_update
@@ -265,6 +265,7 @@ def clear_cache():
create_folder(cache_config['object_cache'], False)
+# TODO don't hardcode C type. [1] of tuple output
type_mapping = {
np.float32: ('PyFloat_AsDouble', 'float'),
np.float64: ('PyFloat_AsDouble', 'double'),
@@ -274,8 +275,6 @@ type_mapping = {
np.uint16: ('PyLong_AsUnsignedLong', 'uint16_t'),
np.uint32: ('PyLong_AsUnsignedLong', 'uint32_t'),
np.uint64: ('PyLong_AsUnsignedLong', 'uint64_t'),
- np.complex64: (('PyComplex_RealAsDouble', 'PyComplex_ImagAsDouble'), 'ComplexFloat'),
- np.complex128: (('PyComplex_RealAsDouble', 'PyComplex_ImagAsDouble'), 'ComplexDouble'),
}
template_extract_scalar = """
@@ -285,14 +284,6 @@ if( obj_{name} == NULL) {{ PyErr_SetString(PyExc_TypeError, "Keyword argument '
if( PyErr_Occurred() ) {{ return NULL; }}
"""
-template_extract_complex = """
-PyObject * obj_{name} = PyDict_GetItemString(kwargs, "{name}");
-if( obj_{name} == NULL) {{ PyErr_SetString(PyExc_TypeError, "Keyword argument '{name}' missing"); return NULL; }};
-{target_type} {name}{{ ({real_type}) {extract_function_real}( obj_{name} ),
- ({real_type}) {extract_function_imag}( obj_{name} ) }};
-if( PyErr_Occurred() ) {{ return NULL; }}
-"""
-
template_extract_array = """
PyObject * obj_{name} = PyDict_GetItemString(kwargs, "{name}");
if( obj_{name} == NULL) {{ PyErr_SetString(PyExc_TypeError, "Keyword argument '{name}' missing"); return NULL; }};
@@ -388,7 +379,7 @@ def create_function_boilerplate_code(parameter_info, name, ast_node, insert_chec
aligned = False
if ast_node.assignments:
aligned = any([a.lhs.args[2] for a in ast_node.assignments
- if hasattr(a, 'lhs') and isinstance(a.lhs, cast_func)
+ if hasattr(a, 'lhs') and isinstance(a.lhs, CastFunc)
and hasattr(a.lhs, 'dtype') and isinstance(a.lhs.dtype, VectorType)])
if ast_node.instruction_set and aligned:
@@ -398,7 +389,7 @@ def create_function_boilerplate_code(parameter_info, name, ast_node, insert_chec
for loop in ast_node.atoms(LoopOverCoordinate):
has_openmp = has_openmp or any(['#pragma omp' in p for p in loop.prefix_lines])
has_nontemporal = has_nontemporal or any([a.args[0].field == field and a.args[3] for a in
- loop.atoms(vector_memory_access)])
+ loop.atoms(VectorMemoryAccess)])
if has_openmp and has_nontemporal:
byte_width = ast_node.instruction_set['cachelineSize']
offset = max(max(ast_node.ghost_layers)) * item_size
@@ -453,17 +444,9 @@ def create_function_boilerplate_code(parameter_info, name, ast_node, insert_chec
continue
else:
extract_function, target_type = type_mapping[param.symbol.dtype.numpy_dtype.type]
- if np.issubdtype(param.symbol.dtype.numpy_dtype, np.complexfloating):
- pre_call_code += template_extract_complex.format(extract_function_real=extract_function[0],
- extract_function_imag=extract_function[1],
- target_type=target_type,
- real_type="float" if target_type == "ComplexFloat"
- else "double",
- name=param.symbol.name)
- else:
- pre_call_code += template_extract_scalar.format(extract_function=extract_function,
- target_type=target_type,
- name=param.symbol.name)
+ pre_call_code += template_extract_scalar.format(extract_function=extract_function,
+ target_type=target_type,
+ name=param.symbol.name)
parameters.append(param.symbol.name)
diff --git a/pystencils/cpu/kernelcreation.py b/pystencils/cpu/kernelcreation.py
index 865beefa9b793233ea74c5b02b315371dcd6ce8e..4cf0955a5af86a5e0ccb65ed96d85f6e171006b8 100644
--- a/pystencils/cpu/kernelcreation.py
+++ b/pystencils/cpu/kernelcreation.py
@@ -1,26 +1,25 @@
-from typing import List, Union
+from typing import Union
import sympy as sp
-import numpy as np
import pystencils.astnodes as ast
-from pystencils.assignment import Assignment
+from pystencils.simp.assignment_collection import AssignmentCollection
+from pystencils.config import CreateKernelConfig
from pystencils.enums import Target, Backend
from pystencils.astnodes import Block, KernelFunction, LoopOverCoordinate, SympyAssignment
from pystencils.cpu.cpujit import make_python_function
-from pystencils.data_types import StructType, TypedSymbol, create_type
+from pystencils.typing import StructType, TypedSymbol, create_type
+from pystencils.typing.transformations import add_types
from pystencils.field import Field, FieldType
+from pystencils.node_collection import NodeCollection
from pystencils.transformations import (
- add_types, filtered_tree_iteration, get_base_buffer_index, get_optimal_loop_ordering, make_loop_over_domain,
+ filtered_tree_iteration, get_base_buffer_index, get_optimal_loop_ordering, make_loop_over_domain,
move_constants_before_loop, parse_base_pointer_info, resolve_buffer_accesses,
resolve_field_accesses, split_inner_loop)
-AssignmentOrAstNodeList = List[Union[Assignment, ast.Node]]
-
-def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "kernel", type_info='double',
- split_groups=(), iteration_slice=None, ghost_layers=None,
- skip_independence_check=False, allow_double_writes=False) -> KernelFunction:
+def create_kernel(assignments: Union[AssignmentCollection, NodeCollection],
+ config: CreateKernelConfig) -> KernelFunction:
"""Creates an abstract syntax tree for a kernel function, by taking a list of update rules.
Loops are created according to the field accesses in the equations.
@@ -28,39 +27,25 @@ def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "ke
Args:
assignments: list of sympy equations, containing accesses to :class:`pystencils.field.Field`.
Defining the update rules of the kernel
- function_name: name of the generated function - only important if generated code is written out
- type_info: a map from symbol name to a C type specifier. If not specified all symbols are assumed to
- be of type 'double' except symbols which occur on the left hand side of equations where the
- right hand side is a sympy Boolean which are assumed to be 'bool' .
- split_groups: Specification on how to split up inner loop into multiple loops. For details see
- transformation :func:`pystencils.transformation.split_inner_loop`
- iteration_slice: if not None, iteration is done only over this slice of the field
- ghost_layers: a sequence of pairs for each coordinate with lower and upper nr of ghost layers
- that should be excluded from the iteration.
- if None, the number of ghost layers is determined automatically and assumed to be equal for a
- all dimensions
- skip_independence_check: don't check that loop iterations are independent. This is needed e.g. for
- periodicity kernel, that access the field outside the iteration bounds. Use with care!
- allow_double_writes: If True, don't check if every field is only written at a single location. This is required
- for example for kernels that are compiled with loop step sizes > 1, that handle multiple
- cells at once. Use with care!
+ config: create kernel config
Returns:
AST node representing a function, that can be printed as C or CUDA code
"""
- def type_symbol(term):
- if isinstance(term, Field.Access) or isinstance(term, TypedSymbol):
- return term
- elif isinstance(term, sp.Symbol):
- if isinstance(type_info, str) or not hasattr(type_info, '__getitem__'):
- return TypedSymbol(term.name, create_type(type_info))
- else:
- return TypedSymbol(term.name, type_info[term.name])
- else:
- raise ValueError("Term has to be field access or symbol")
+ function_name = config.function_name
+ iteration_slice = config.iteration_slice
+ ghost_layers = config.ghost_layers
+ fields_written = assignments.bound_fields
+ fields_read = assignments.rhs_fields
+
+ split_groups = ()
+ if 'split_groups' in assignments.simplification_hints:
+ split_groups = assignments.simplification_hints['split_groups']
+ assignments = assignments.all_assignments
+
+ # TODO Cleanup: move add_types to create_domain_kernel or create_kernel
+ assignments = add_types(assignments, config)
- fields_read, fields_written, assignments = add_types(
- assignments, type_info, not skip_independence_check, check_double_write_condition=not allow_double_writes)
all_fields = fields_read.union(fields_written)
read_only_fields = set([f.name for f in fields_read - fields_written])
@@ -75,6 +60,19 @@ def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "ke
ghost_layers=ghost_layer_info, function_name=function_name, assignments=assignments)
if split_groups:
+ type_info = config.data_type
+
+ def type_symbol(term):
+ if isinstance(term, Field.Access) or isinstance(term, TypedSymbol):
+ return term
+ elif isinstance(term, sp.Symbol):
+ if isinstance(type_info, str) or not hasattr(type_info, '__getitem__'):
+ return TypedSymbol(term.name, create_type(type_info))
+ else:
+ return TypedSymbol(term.name, type_info[term.name])
+ else:
+ raise ValueError("Term has to be field access or symbol")
+
typed_split_groups = [[type_symbol(s) for s in split_group] for split_group in split_groups]
split_inner_loop(ast_node, typed_split_groups)
@@ -90,13 +88,14 @@ def create_kernel(assignments: AssignmentOrAstNodeList, function_name: str = "ke
if any(FieldType.is_buffer(f) for f in all_fields):
resolve_buffer_accesses(ast_node, get_base_buffer_index(ast_node), read_only_fields)
+ # TODO think about typing
resolve_field_accesses(ast_node, read_only_fields, field_to_base_pointer_info=base_pointer_info)
move_constants_before_loop(ast_node)
return ast_node
-def create_indexed_kernel(assignments: AssignmentOrAstNodeList, index_fields, function_name="kernel",
- type_info=None, coordinate_names=('x', 'y', 'z')) -> KernelFunction:
+def create_indexed_kernel(assignments: Union[AssignmentCollection, NodeCollection],
+ config: CreateKernelConfig) -> KernelFunction:
"""
Similar to :func:`create_kernel`, but here not all cells of a field are updated but only cells with
coordinates which are stored in an index field. This traversal method can e.g. be used for boundary handling.
@@ -108,12 +107,17 @@ def create_indexed_kernel(assignments: AssignmentOrAstNodeList, index_fields, fu
Args:
assignments: list of assignments
- index_fields: list of index fields, i.e. 1D fields with struct data type
- type_info: see documentation of :func:`create_kernel`
- function_name: see documentation of :func:`create_kernel`
- coordinate_names: name of the coordinate fields in the struct data type
+ config: Kernel configuration
"""
- fields_read, fields_written, assignments = add_types(assignments, type_info, check_independence_condition=False)
+ function_name = config.function_name
+ index_fields = config.index_fields
+ coordinate_names = config.coordinate_names
+ fields_written = assignments.bound_fields
+ fields_read = assignments.rhs_fields
+
+ assignments = assignments.all_assignments
+ assignments = add_types(assignments, config)
+
all_fields = fields_read.union(fields_written)
for index_field in index_fields:
@@ -132,7 +136,7 @@ def create_indexed_kernel(assignments: AssignmentOrAstNodeList, index_fields, fu
data_type = idx_field.dtype
if data_type.has_element(name):
rhs = idx_field[0](name)
- lhs = TypedSymbol(name, np.int64)
+ lhs = TypedSymbol(name, data_type.get_element_type(name))
return SympyAssignment(lhs, rhs)
raise ValueError(f"Index {name} not found in any of the passed index fields")
diff --git a/pystencils/cpu/vectorization.py b/pystencils/cpu/vectorization.py
index f4d4730c3a5c1b8efc9e6f30decfc4b7dda70a53..b3236a3c5cab7925116431ae343e20ca0fea0f1f 100644
--- a/pystencils/cpu/vectorization.py
+++ b/pystencils/cpu/vectorization.py
@@ -3,13 +3,14 @@ from typing import Container, Union
import numpy as np
import sympy as sp
-from sympy.logic.boolalg import BooleanFunction
+from sympy.logic.boolalg import BooleanFunction, BooleanAtom
import pystencils.astnodes as ast
from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets, get_vector_instruction_set
-from pystencils.data_types import (
- PointerType, TypedSymbol, VectorType, cast_func, collate_types, get_type_of_expression, vector_memory_access)
+from pystencils.typing import (BasicType, PointerType, TypedSymbol, VectorType, CastFunc, collate_types,
+ get_type_of_expression, VectorMemoryAccess)
from pystencils.fast_approximation import fast_division, fast_inv_sqrt, fast_sqrt
+from pystencils.functions import DivFunc
from pystencils.field import Field
from pystencils.integer_functions import modulo_ceil, modulo_floor
from pystencils.sympyextensions import fast_subs
@@ -76,6 +77,8 @@ class CachelineSize(ast.Node):
def vectorize(kernel_ast: ast.KernelFunction, instruction_set: str = 'best',
assume_aligned: bool = False, nontemporal: Union[bool, Container[Union[str, Field]]] = False,
assume_inner_stride_one: bool = False, assume_sufficient_line_padding: bool = True):
+ # TODO Vectorization Revamp we first introduce the remainder loop and then check if we can even vectorise.
+ # Maybe first copy the ast and return the copied version on failure
"""Explicit vectorization using SIMD vectorization via intrinsics.
Args:
@@ -123,19 +126,22 @@ def vectorize(kernel_ast: ast.KernelFunction, instruction_set: str = 'best',
assert float_size in (8, 4)
default_float_type = 'double' if float_size == 8 else 'float'
vector_is = get_vector_instruction_set(default_float_type, instruction_set=instruction_set)
- vector_width = vector_is['width']
kernel_ast.instruction_set = vector_is
strided = 'storeS' in vector_is and 'loadS' in vector_is
keep_loop_stop = '{loop_stop}' in vector_is['storeA' if assume_aligned else 'storeU']
- vectorize_inner_loops_and_adapt_load_stores(kernel_ast, vector_width, assume_aligned, nontemporal,
- strided, keep_loop_stop, assume_sufficient_line_padding)
- insert_vector_casts(kernel_ast, default_float_type)
+ vectorize_inner_loops_and_adapt_load_stores(kernel_ast, assume_aligned, nontemporal,
+ strided, keep_loop_stop, assume_sufficient_line_padding,
+ default_float_type)
+ # is in vectorize_inner_loops_and_adapt_load_stores.. insert_vector_casts(kernel_ast, default_float_type)
-def vectorize_inner_loops_and_adapt_load_stores(ast_node, vector_width, assume_aligned, nontemporal_fields,
- strided, keep_loop_stop, assume_sufficient_line_padding):
+def vectorize_inner_loops_and_adapt_load_stores(ast_node, assume_aligned, nontemporal_fields,
+ strided, keep_loop_stop, assume_sufficient_line_padding,
+ default_float_type):
"""Goes over all innermost loops, changes increment to vector width and replaces field accesses by vector type."""
+ vector_width = ast_node.instruction_set['width']
+
all_loops = filtered_tree_iteration(ast_node, ast.LoopOverCoordinate, stop_type=ast.SympyAssignment)
inner_loops = [n for n in all_loops if n.is_innermost_loop]
zero_loop_counters = {l.loop_counter_symbol: 0 for l in all_loops}
@@ -157,6 +163,7 @@ def vectorize_inner_loops_and_adapt_load_stores(ast_node, vector_width, assume_a
if len(loop_nodes) == 0:
continue
loop_node = loop_nodes[0]
+ # loop_node is the vectorized one
# Find all array accesses (indexed) that depend on the loop counter as offset
loop_counter_symbol = ast.LoopOverCoordinate.get_loop_counter_symbol(loop_node.coordinate_to_loop_over)
@@ -180,8 +187,8 @@ def vectorize_inner_loops_and_adapt_load_stores(ast_node, vector_width, assume_a
nontemporal = False
if hasattr(indexed, 'field'):
nontemporal = (indexed.field in nontemporal_fields) or (indexed.field.name in nontemporal_fields)
- substitutions[indexed] = vector_memory_access(indexed, vec_type, use_aligned_access, nontemporal, True,
- stride if strided else 1)
+ substitutions[indexed] = VectorMemoryAccess(indexed, vec_type, use_aligned_access, nontemporal, True,
+ stride if strided else 1)
if nontemporal:
# insert NontemporalFence after the outermost loop
parent = loop_node.parent
@@ -197,12 +204,13 @@ def vectorize_inner_loops_and_adapt_load_stores(ast_node, vector_width, assume_a
loop_node.step = vector_width
loop_node.subs(substitutions)
vector_int_width = ast_node.instruction_set['intwidth']
- vector_loop_counter = cast_func(loop_counter_symbol, VectorType(loop_counter_symbol.dtype, vector_int_width)) \
- + cast_func(tuple(range(vector_int_width if type(vector_int_width) is int else 2)),
- VectorType(loop_counter_symbol.dtype, vector_int_width))
+ arg_1 = CastFunc(loop_counter_symbol, VectorType(loop_counter_symbol.dtype, vector_int_width))
+ arg_2 = CastFunc(tuple(range(vector_int_width if type(vector_int_width) is int else 2)),
+ VectorType(loop_counter_symbol.dtype, vector_int_width))
+ vector_loop_counter = arg_1 + arg_2
fast_subs(loop_node, {loop_counter_symbol: vector_loop_counter},
- skip=lambda e: isinstance(e, ast.ResolvedFieldAccess) or isinstance(e, vector_memory_access))
+ skip=lambda e: isinstance(e, ast.ResolvedFieldAccess) or isinstance(e, VectorMemoryAccess))
mask_conditionals(loop_node)
@@ -214,6 +222,7 @@ def vectorize_inner_loops_and_adapt_load_stores(ast_node, vector_width, assume_a
substitutions.update({s[0]: s[1] for s in zip(rng.result_symbols, new_result_symbols)})
rng._symbols_defined = set(new_result_symbols)
fast_subs(loop_node, substitutions, skip=lambda e: isinstance(e, RNGBase))
+ insert_vector_casts(loop_node, ast_node.instruction_set, default_float_type)
def mask_conditionals(loop_body):
@@ -232,8 +241,8 @@ def mask_conditionals(loop_body):
node.condition_expr = vec_any(node.condition_expr)
elif isinstance(node, ast.SympyAssignment):
if mask is not True:
- s = {ma: vector_memory_access(*ma.args[0:4], sp.And(mask, ma.args[4]), *ma.args[5:])
- for ma in node.atoms(vector_memory_access)}
+ s = {ma: VectorMemoryAccess(*ma.args[0:4], sp.And(mask, ma.args[4]), *ma.args[5:])
+ for ma in node.atoms(VectorMemoryAccess)}
node.subs(s)
else:
for arg in node.args:
@@ -242,28 +251,33 @@ def mask_conditionals(loop_body):
visit_node(loop_body, mask=True)
-def insert_vector_casts(ast_node, default_float_type='double'):
+def insert_vector_casts(ast_node, instruction_set, default_float_type='double'):
"""Inserts necessary casts from scalar values to vector values."""
- handled_functions = (sp.Add, sp.Mul, fast_division, fast_sqrt, fast_inv_sqrt, vec_any, vec_all)
-
- def visit_expr(expr, default_type='double'):
- if isinstance(expr, vector_memory_access):
- return vector_memory_access(*expr.args[0:4], visit_expr(expr.args[4], default_type), *expr.args[5:])
- elif isinstance(expr, cast_func):
- return expr
- elif expr.func is sp.Abs and 'abs' not in ast_node.instruction_set:
+ handled_functions = (sp.Add, sp.Mul, fast_division, fast_sqrt, fast_inv_sqrt, vec_any, vec_all, DivFunc,
+ sp.UnevaluatedExpr, sp.Abs)
+
+ def visit_expr(expr, default_type='double'): # TODO Vectorization Revamp: get rid of default_type
+ if isinstance(expr, VectorMemoryAccess):
+ return VectorMemoryAccess(*expr.args[0:4], visit_expr(expr.args[4], default_type), *expr.args[5:])
+ elif isinstance(expr, CastFunc):
+ cast_type = expr.args[1]
+ arg = visit_expr(expr.args[0])
+ assert cast_type in [BasicType('float32'), BasicType('float64')],\
+ f'Vectorization cannot vectorize type {cast_type}'
+ return expr.func(arg, VectorType(cast_type, instruction_set['width']))
+ elif expr.func is sp.Abs and 'abs' not in instruction_set:
new_arg = visit_expr(expr.args[0], default_type)
- base_type = get_type_of_expression(expr.args[0]).base_type if type(expr.args[0]) is vector_memory_access \
+ base_type = get_type_of_expression(expr.args[0]).base_type if type(expr.args[0]) is VectorMemoryAccess \
else get_type_of_expression(expr.args[0])
- pw = sp.Piecewise((-new_arg, new_arg < cast_func(0, base_type.numpy_dtype)),
+ pw = sp.Piecewise((-new_arg, new_arg < CastFunc(0, base_type.numpy_dtype)),
(new_arg, True))
return visit_expr(pw, default_type)
elif expr.func in handled_functions or isinstance(expr, sp.Rel) or isinstance(expr, BooleanFunction):
if expr.func is sp.Mul and expr.args[0] == -1:
# special treatment for the unary minus: make sure that the -1 has the same type as the argument
dtype = int
- for arg in expr.atoms(vector_memory_access):
+ for arg in expr.atoms(VectorMemoryAccess):
if arg.dtype.base_type.is_float():
dtype = arg.dtype.base_type.numpy_dtype.type
for arg in expr.atoms(TypedSymbol):
@@ -280,7 +294,7 @@ def insert_vector_casts(ast_node, default_float_type='double'):
else:
target_type = collate_types(arg_types)
casted_args = [
- cast_func(a, target_type) if t != target_type and not isinstance(a, vector_memory_access) else a
+ CastFunc(a, target_type) if t != target_type and not isinstance(a, VectorMemoryAccess) else a
for a, t in zip(new_args, arg_types)]
return expr.func(*casted_args)
elif expr.func is sp.Pow:
@@ -299,22 +313,28 @@ def insert_vector_casts(ast_node, default_float_type='double'):
if type(condition_target_type) is not VectorType and type(result_target_type) is VectorType:
condition_target_type = VectorType(condition_target_type, width=result_target_type.width)
- casted_results = [cast_func(a, result_target_type) if t != result_target_type else a
+ casted_results = [CastFunc(a, result_target_type) if t != result_target_type else a
for a, t in zip(new_results, types_of_results)]
- casted_conditions = [cast_func(a, condition_target_type)
+ casted_conditions = [CastFunc(a, condition_target_type)
if t != condition_target_type and a is not True else a
for a, t in zip(new_conditions, types_of_conditions)]
return sp.Piecewise(*[(r, c) for r, c in zip(casted_results, casted_conditions)])
- else:
+ elif isinstance(expr, (sp.Number, TypedSymbol, BooleanAtom)):
return expr
+ else:
+ raise NotImplementedError(f'Due to defensive programming we handle only specific expressions.\n'
+ f'The expression {expr} of type {type(expr)} is not known yet.')
def visit_node(node, substitution_dict, default_type='double'):
substitution_dict = substitution_dict.copy()
for arg in node.args:
if isinstance(arg, ast.SympyAssignment):
assignment = arg
+ # If there is a remainder loop we do not vectorise it, thus lhs will indicate this
+ # if isinstance(assignment.lhs, ast.ResolvedFieldAccess):
+ # continue
subs_expr = fast_subs(assignment.rhs, substitution_dict,
skip=lambda e: isinstance(e, ast.ResolvedFieldAccess))
assignment.rhs = visit_expr(subs_expr, default_type)
@@ -326,7 +346,7 @@ def insert_vector_casts(ast_node, default_float_type='double'):
new_lhs = TypedSymbol(assignment.lhs.name, new_lhs_type)
substitution_dict[assignment.lhs] = new_lhs
assignment.lhs = new_lhs
- elif isinstance(assignment.lhs, vector_memory_access):
+ elif isinstance(assignment.lhs, VectorMemoryAccess):
assignment.lhs = visit_expr(assignment.lhs, default_type)
elif isinstance(arg, ast.Conditional):
arg.condition_expr = fast_subs(arg.condition_expr, substitution_dict,
diff --git a/pystencils/data_types.py b/pystencils/data_types.py
deleted file mode 100644
index bd18e2993cabbfa30b0996e6ad96c4fb6535407b..0000000000000000000000000000000000000000
--- a/pystencils/data_types.py
+++ /dev/null
@@ -1,814 +0,0 @@
-import ctypes
-from collections import defaultdict
-from functools import partial
-from typing import Tuple
-
-import numpy as np
-import sympy as sp
-import sympy.codegen.ast
-from sympy.core.cache import cacheit
-from sympy.logic.boolalg import Boolean, BooleanFunction
-
-import pystencils
-from pystencils.cache import memorycache, memorycache_if_hashable
-from pystencils.utils import all_equal
-
-
-def typed_symbols(names, dtype, *args):
- symbols = sp.symbols(names, *args)
- if isinstance(symbols, Tuple):
- return tuple(TypedSymbol(str(s), dtype) for s in symbols)
- else:
- return TypedSymbol(str(symbols), dtype)
-
-
-def type_all_numbers(expr, dtype):
- substitutions = {a: cast_func(a, dtype) for a in expr.atoms(sp.Number)}
- return expr.subs(substitutions)
-
-
-def matrix_symbols(names, dtype, rows, cols):
- if isinstance(names, str):
- names = names.replace(' ', '').split(',')
-
- matrices = []
- for n in names:
- symbols = typed_symbols(f"{n}:{rows * cols}", dtype)
- matrices.append(sp.Matrix(rows, cols, lambda i, j: symbols[i * cols + j]))
-
- return tuple(matrices)
-
-
-def assumptions_from_dtype(dtype):
- """Derives SymPy assumptions from :class:`BasicType` or a Numpy dtype
-
- Args:
- dtype (BasicType, np.dtype): a Numpy data type
- Returns:
- A dict of SymPy assumptions
- """
- if hasattr(dtype, 'numpy_dtype'):
- dtype = dtype.numpy_dtype
-
- assumptions = dict()
-
- try:
- if np.issubdtype(dtype, np.integer):
- assumptions.update({'integer': True})
-
- if np.issubdtype(dtype, np.unsignedinteger):
- assumptions.update({'negative': False})
-
- if np.issubdtype(dtype, np.integer) or \
- np.issubdtype(dtype, np.floating):
- assumptions.update({'real': True})
- except Exception:
- pass
-
- return assumptions
-
-
-# noinspection PyPep8Naming
-class address_of(sp.Function):
- is_Atom = True
-
- def __new__(cls, arg):
- obj = sp.Function.__new__(cls, arg)
- return obj
-
- @property
- def canonical(self):
- if hasattr(self.args[0], 'canonical'):
- return self.args[0].canonical
- else:
- raise NotImplementedError()
-
- @property
- def is_commutative(self):
- return self.args[0].is_commutative
-
- @property
- def dtype(self):
- if hasattr(self.args[0], 'dtype'):
- return PointerType(self.args[0].dtype, restrict=True)
- else:
- return PointerType('void', restrict=True)
-
-
-# noinspection PyPep8Naming
-class cast_func(sp.Function):
- is_Atom = True
-
- def __new__(cls, *args, **kwargs):
- if len(args) != 2:
- pass
- expr, dtype, *other_args = args
- if not isinstance(dtype, Type):
- dtype = create_type(dtype)
- # to work in conditions of sp.Piecewise cast_func has to be of type Boolean as well
- # however, a cast_function should only be a boolean if its argument is a boolean, otherwise this leads
- # to problems when for example comparing cast_func's for equality
- #
- # lhs = bitwise_and(a, cast_func(1, 'int'))
- # rhs = cast_func(0, 'int')
- # print( sp.Ne(lhs, rhs) ) # would give true if all cast_funcs are booleans
- # -> thus a separate class boolean_cast_func is introduced
- if isinstance(expr, Boolean) and (not isinstance(expr, TypedSymbol) or expr.dtype == BasicType(bool)):
- cls = boolean_cast_func
-
- return sp.Function.__new__(cls, expr, dtype, *other_args, **kwargs)
-
- @property
- def canonical(self):
- if hasattr(self.args[0], 'canonical'):
- return self.args[0].canonical
- else:
- raise NotImplementedError()
-
- @property
- def is_commutative(self):
- return self.args[0].is_commutative
-
- def _eval_evalf(self, *args, **kwargs):
- return self.args[0].evalf()
-
- @property
- def dtype(self):
- return self.args[1]
-
- @property
- def is_integer(self):
- """
- Uses Numpy type hierarchy to determine :func:`sympy.Expr.is_integer` predicate
-
- For reference: Numpy type hierarchy https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.scalars.html
- """
- if hasattr(self.dtype, 'numpy_dtype'):
- return np.issubdtype(self.dtype.numpy_dtype, np.integer) or super().is_integer
- else:
- return super().is_integer
-
- @property
- def is_negative(self):
- """
- See :func:`.TypedSymbol.is_integer`
- """
- if hasattr(self.dtype, 'numpy_dtype'):
- if np.issubdtype(self.dtype.numpy_dtype, np.unsignedinteger):
- return False
-
- return super().is_negative
-
- @property
- def is_nonnegative(self):
- """
- See :func:`.TypedSymbol.is_integer`
- """
- if self.is_negative is False:
- return True
- else:
- return super().is_nonnegative
-
- @property
- def is_real(self):
- """
- See :func:`.TypedSymbol.is_integer`
- """
- if hasattr(self.dtype, 'numpy_dtype'):
- return np.issubdtype(self.dtype.numpy_dtype, np.integer) or \
- np.issubdtype(self.dtype.numpy_dtype, np.floating) or \
- super().is_real
- else:
- return super().is_real
-
-
-# noinspection PyPep8Naming
-class boolean_cast_func(cast_func, Boolean):
- pass
-
-
-# noinspection PyPep8Naming
-class vector_memory_access(cast_func):
- # Arguments are: read/write expression, type, aligned, nontemporal, mask (or none), stride
- nargs = (6,)
-
-
-# noinspection PyPep8Naming
-class reinterpret_cast_func(cast_func):
- pass
-
-
-# noinspection PyPep8Naming
-class pointer_arithmetic_func(sp.Function, Boolean):
- @property
- def canonical(self):
- if hasattr(self.args[0], 'canonical'):
- return self.args[0].canonical
- else:
- raise NotImplementedError()
-
-
-class TypedSymbol(sp.Symbol):
- def __new__(cls, *args, **kwds):
- obj = TypedSymbol.__xnew_cached_(cls, *args, **kwds)
- return obj
-
- def __new_stage2__(cls, name, dtype, **kwargs):
- assumptions = assumptions_from_dtype(dtype)
- assumptions.update(kwargs)
- obj = super(TypedSymbol, cls).__xnew__(cls, name, **assumptions)
- try:
- obj._dtype = create_type(dtype)
- except (TypeError, ValueError):
- # on error keep the string
- obj._dtype = dtype
- return obj
-
- __xnew__ = staticmethod(__new_stage2__)
- __xnew_cached_ = staticmethod(cacheit(__new_stage2__))
-
- @property
- def dtype(self):
- return self._dtype
-
- def _hashable_content(self):
- return super()._hashable_content(), hash(self._dtype)
-
- def __getnewargs__(self):
- return self.name, self.dtype
-
- def __getnewargs_ex__(self):
- return (self.name, self.dtype), self.assumptions0
-
- @property
- def canonical(self):
- return self
-
- @property
- def reversed(self):
- return self
-
- @property
- def headers(self):
- headers = []
- try:
- if np.issubdtype(self.dtype.numpy_dtype, np.complexfloating):
- headers.append('"cuda_complex.hpp"')
- except Exception:
- pass
- try:
- if np.issubdtype(self.dtype.base_type.numpy_dtype, np.complexfloating):
- headers.append('"cuda_complex.hpp"')
- except Exception:
- pass
-
- return headers
-
-
-def create_type(specification):
- """Creates a subclass of Type according to a string or an object of subclass Type.
-
- Args:
- specification: Type object, or a string
-
- Returns:
- Type object, or a new Type object parsed from the string
- """
- if isinstance(specification, Type):
- return specification
- else:
- numpy_dtype = np.dtype(specification)
- if numpy_dtype.fields is None:
- return BasicType(numpy_dtype, const=False)
- else:
- return StructType(numpy_dtype, const=False)
-
-
-@memorycache(maxsize=64)
-def create_composite_type_from_string(specification):
- """Creates a new Type object from a c-like string specification.
-
- Args:
- specification: Specification string
-
- Returns:
- Type object
- """
- specification = specification.lower().split()
- parts = []
- current = []
- for s in specification:
- if s == '*':
- parts.append(current)
- current = [s]
- else:
- current.append(s)
- if len(current) > 0:
- parts.append(current)
- # Parse native part
- base_part = parts.pop(0)
- const = False
- if 'const' in base_part:
- const = True
- base_part.remove('const')
- assert len(base_part) == 1
- if base_part[0][-1] == "*":
- base_part[0] = base_part[0][:-1]
- parts.append('*')
- current_type = BasicType(np.dtype(base_part[0]), const)
- # Parse pointer parts
- for part in parts:
- restrict = False
- const = False
- if 'restrict' in part:
- restrict = True
- part.remove('restrict')
- if 'const' in part:
- const = True
- part.remove("const")
- assert len(part) == 1 and part[0] == '*'
- current_type = PointerType(current_type, const, restrict)
- return current_type
-
-
-def get_base_type(data_type):
- while data_type.base_type is not None:
- data_type = data_type.base_type
- return data_type
-
-
-def to_ctypes(data_type):
- """
- Transforms a given Type into ctypes
- :param data_type: Subclass of Type
- :return: ctypes type object
- """
- if isinstance(data_type, PointerType):
- return ctypes.POINTER(to_ctypes(data_type.base_type))
- elif isinstance(data_type, StructType):
- return ctypes.POINTER(ctypes.c_uint8)
- else:
- return to_ctypes.map[data_type.numpy_dtype]
-
-
-to_ctypes.map = {
- np.dtype(np.int8): ctypes.c_int8,
- np.dtype(np.int16): ctypes.c_int16,
- np.dtype(np.int32): ctypes.c_int32,
- np.dtype(np.int64): ctypes.c_int64,
-
- np.dtype(np.uint8): ctypes.c_uint8,
- np.dtype(np.uint16): ctypes.c_uint16,
- np.dtype(np.uint32): ctypes.c_uint32,
- np.dtype(np.uint64): ctypes.c_uint64,
-
- np.dtype(np.float32): ctypes.c_float,
- np.dtype(np.float64): ctypes.c_double,
-}
-
-
-def peel_off_type(dtype, type_to_peel_off):
- while type(dtype) is type_to_peel_off:
- dtype = dtype.base_type
- return dtype
-
-
-def collate_types(types,
- forbid_collation_to_complex=False,
- forbid_collation_to_float=False,
- default_float_type='float64',
- default_int_type='int64'):
- """
- Takes a sequence of types and returns their "common type" e.g. (float, double, float) -> double
- Uses the collation rules from numpy.
- """
- if forbid_collation_to_complex:
- types = [t for t in types if not np.issubdtype(t.numpy_dtype, np.complexfloating)]
- if not types:
- return create_type(default_float_type)
-
- if forbid_collation_to_float:
- types = [t for t in types if not np.issubdtype(t.numpy_dtype, np.floating)]
- if not types:
- return create_type(default_int_type)
-
- # Pointer arithmetic case i.e. pointer + integer is allowed
- if any(type(t) is PointerType for t in types):
- pointer_type = None
- for t in types:
- if type(t) is PointerType:
- if pointer_type is not None:
- raise ValueError("Cannot collate the combination of two pointer types")
- pointer_type = t
- elif type(t) is BasicType:
- if not (t.is_int() or t.is_uint()):
- raise ValueError("Invalid pointer arithmetic")
- else:
- raise ValueError("Invalid pointer arithmetic")
- return pointer_type
-
- # peel of vector types, if at least one vector type occurred the result will also be the vector type
- vector_type = [t for t in types if type(t) is VectorType]
- if not all_equal(t.width for t in vector_type):
- raise ValueError("Collation failed because of vector types with different width")
- types = [peel_off_type(t, VectorType) for t in types]
-
- # now we should have a list of basic types - struct types are not yet supported
- assert all(type(t) is BasicType for t in types)
-
- if any(t.is_float() for t in types):
- types = tuple(t for t in types if t.is_float())
- # use numpy collation -> create type from numpy type -> and, put vector type around if necessary
- result_numpy_type = np.result_type(*(t.numpy_dtype for t in types))
- result = BasicType(result_numpy_type)
- if vector_type:
- result = VectorType(result, vector_type[0].width)
- return result
-
-
-@memorycache_if_hashable(maxsize=2048)
-def get_type_of_expression(expr,
- default_float_type='double',
- default_int_type='int',
- symbol_type_dict=None):
- from pystencils.astnodes import ResolvedFieldAccess
- from pystencils.cpu.vectorization import vec_all, vec_any
-
- if default_float_type == 'float':
- default_float_type = 'float32'
-
- if not symbol_type_dict:
- symbol_type_dict = defaultdict(lambda: create_type('double'))
-
- get_type = partial(get_type_of_expression,
- default_float_type=default_float_type,
- default_int_type=default_int_type,
- symbol_type_dict=symbol_type_dict)
-
- expr = sp.sympify(expr)
- if isinstance(expr, sp.Integer):
- return create_type(default_int_type)
- elif expr.is_real is False:
- return create_type((np.zeros((1,), default_float_type) * 1j).dtype)
- elif isinstance(expr, sp.Rational) or isinstance(expr, sp.Float):
- return create_type(default_float_type)
- elif isinstance(expr, ResolvedFieldAccess):
- return expr.field.dtype
- elif isinstance(expr, pystencils.field.Field.AbstractAccess):
- return expr.field.dtype
- elif isinstance(expr, TypedSymbol):
- return expr.dtype
- elif isinstance(expr, sp.Symbol):
- if symbol_type_dict:
- return symbol_type_dict[expr.name]
- else:
- raise ValueError("All symbols inside this expression have to be typed! ", str(expr))
- elif isinstance(expr, cast_func):
- return expr.args[1]
- elif isinstance(expr, (vec_any, vec_all)):
- return create_type("bool")
- elif hasattr(expr, 'func') and expr.func == sp.Piecewise:
- collated_result_type = collate_types(tuple(get_type(a[0]) for a in expr.args))
- collated_condition_type = collate_types(tuple(get_type(a[1]) for a in expr.args))
- if type(collated_condition_type) is VectorType and type(collated_result_type) is not VectorType:
- collated_result_type = VectorType(collated_result_type, width=collated_condition_type.width)
- return collated_result_type
- elif isinstance(expr, sp.Indexed):
- typed_symbol = expr.base.label
- return typed_symbol.dtype.base_type
- elif isinstance(expr, (Boolean, BooleanFunction)):
- # if any arg is of vector type return a vector boolean, else return a normal scalar boolean
- result = create_type("bool")
- vec_args = [get_type(a) for a in expr.args if isinstance(get_type(a), VectorType)]
- if vec_args:
- result = VectorType(result, width=vec_args[0].width)
- return result
- elif isinstance(expr, sp.Pow):
- base_type = get_type(expr.args[0])
- if expr.exp.is_integer:
- return base_type
- else:
- return collate_types([create_type(default_float_type), base_type])
- elif isinstance(expr, (sp.Sum, sp.Product)):
- return get_type(expr.args[0])
- elif isinstance(expr, sp.Expr):
- expr: sp.Expr
- if expr.args:
- types = tuple(get_type(a) for a in expr.args)
- # collate_types checks numpy_dtype in the special cases
- if any(not hasattr(t, 'numpy_dtype') for t in types):
- forbid_collation_to_complex = False
- forbid_collation_to_float = False
- else:
- forbid_collation_to_complex = expr.is_real is True
- forbid_collation_to_float = expr.is_integer is True
- return collate_types(
- types,
- forbid_collation_to_complex=forbid_collation_to_complex,
- forbid_collation_to_float=forbid_collation_to_float,
- default_float_type=default_float_type,
- default_int_type=default_int_type)
- else:
- if expr.is_integer:
- return create_type(default_int_type)
- else:
- return create_type(default_float_type)
-
- raise NotImplementedError("Could not determine type for", expr, type(expr))
-
-
-sympy_version = sp.__version__.split('.')
-if int(sympy_version[0]) * 100 + int(sympy_version[1]) >= 109:
- # __setstate__ would bypass the contructor, so we remove it
- sp.Number.__getstate__ = sp.Basic.__getstate__
- del sp.Basic.__getstate__
-
- class FunctorWithStoredKwargs:
- def __init__(self, func, **kwargs):
- self.func = func
- self.kwargs = kwargs
-
- def __call__(self, *args):
- return self.func(*args, **self.kwargs)
-
- # __reduce_ex__ would strip kwargs, so we override it
- def basic_reduce_ex(self, protocol):
- if hasattr(self, '__getnewargs_ex__'):
- args, kwargs = self.__getnewargs_ex__()
- else:
- args, kwargs = self.__getnewargs__(), {}
- if hasattr(self, '__getstate__'):
- state = self.__getstate__()
- else:
- state = None
- return FunctorWithStoredKwargs(type(self), **kwargs), args, state
- sp.Number.__reduce_ex__ = sp.Basic.__reduce_ex__
- sp.Basic.__reduce_ex__ = basic_reduce_ex
-
-
-class Type(sp.Atom):
- def __new__(cls, *args, **kwargs):
- return sp.Basic.__new__(cls)
-
- def _sympystr(self, *args, **kwargs):
- return str(self)
-
-
-class BasicType(Type):
- @staticmethod
- def numpy_name_to_c(name):
- if name == 'float64':
- return 'double'
- elif name == 'float32':
- return 'float'
- elif name == 'complex64':
- return 'ComplexFloat'
- elif name == 'complex128':
- return 'ComplexDouble'
- elif name.startswith('int'):
- width = int(name[len("int"):])
- return f"int{width}_t"
- elif name.startswith('uint'):
- width = int(name[len("uint"):])
- return f"uint{width}_t"
- elif name == 'bool':
- return 'bool'
- else:
- raise NotImplementedError(f"Can map numpy to C name for {name}")
-
- def __init__(self, dtype, const=False):
- self.const = const
- if isinstance(dtype, Type):
- self._dtype = dtype.numpy_dtype
- else:
- self._dtype = np.dtype(dtype)
- assert self._dtype.fields is None, "Tried to initialize NativeType with a structured type"
- assert self._dtype.hasobject is False
- assert self._dtype.subdtype is None
-
- def __getnewargs__(self):
- return self.numpy_dtype, self.const
-
- def __getnewargs_ex__(self):
- return (self.numpy_dtype, self.const), {}
-
- @property
- def base_type(self):
- return None
-
- @property
- def numpy_dtype(self):
- return self._dtype
-
- @property
- def sympy_dtype(self):
- return getattr(sympy.codegen.ast, str(self.numpy_dtype))
-
- @property
- def item_size(self):
- return 1
-
- def is_int(self):
- return self.numpy_dtype in np.sctypes['int'] or self.numpy_dtype in np.sctypes['uint']
-
- def is_float(self):
- return self.numpy_dtype in np.sctypes['float']
-
- def is_uint(self):
- return self.numpy_dtype in np.sctypes['uint']
-
- def is_complex(self):
- return self.numpy_dtype in np.sctypes['complex']
-
- def is_other(self):
- return self.numpy_dtype in np.sctypes['others']
-
- @property
- def base_name(self):
- return BasicType.numpy_name_to_c(str(self._dtype))
-
- def __str__(self):
- result = BasicType.numpy_name_to_c(str(self._dtype))
- if self.const:
- result += " const"
- return result
-
- def __repr__(self):
- return str(self)
-
- def __eq__(self, other):
- if not isinstance(other, BasicType):
- return False
- else:
- return (self.numpy_dtype, self.const) == (other.numpy_dtype, other.const)
-
- def __hash__(self):
- return hash(str(self))
-
-
-class VectorType(Type):
- instruction_set = None
-
- def __init__(self, base_type, width=4):
- self._base_type = base_type
- self.width = width
-
- @property
- def base_type(self):
- return self._base_type
-
- @property
- def item_size(self):
- return self.width * self.base_type.item_size
-
- def __eq__(self, other):
- if not isinstance(other, VectorType):
- return False
- else:
- return (self.base_type, self.width) == (other.base_type, other.width)
-
- def __str__(self):
- if self.instruction_set is None:
- return f"{self.base_type}[{self.width}]"
- else:
- if self.base_type == create_type("int64") or self.base_type == create_type("int32"):
- return self.instruction_set['int']
- elif self.base_type == create_type("float64"):
- return self.instruction_set['double']
- elif self.base_type == create_type("float32"):
- return self.instruction_set['float']
- elif self.base_type == create_type("bool"):
- return self.instruction_set['bool']
- else:
- raise NotImplementedError()
-
- def __hash__(self):
- return hash((self.base_type, self.width))
-
- def __getnewargs__(self):
- return self._base_type, self.width
-
- def __getnewargs_ex__(self):
- return (self._base_type, self.width), {}
-
-
-class PointerType(Type):
- def __init__(self, base_type, const=False, restrict=True):
- self._base_type = base_type
- self.const = const
- self.restrict = restrict
-
- def __getnewargs__(self):
- return self.base_type, self.const, self.restrict
-
- def __getnewargs_ex__(self):
- return (self.base_type, self.const, self.restrict), {}
-
- @property
- def alias(self):
- return not self.restrict
-
- @property
- def base_type(self):
- return self._base_type
-
- @property
- def item_size(self):
- return self.base_type.item_size
-
- def __eq__(self, other):
- if not isinstance(other, PointerType):
- return False
- else:
- return (self.base_type, self.const, self.restrict) == (other.base_type, other.const, other.restrict)
-
- def __str__(self):
- components = [str(self.base_type), '*']
- if self.restrict:
- components.append('RESTRICT')
- if self.const:
- components.append("const")
- return " ".join(components)
-
- def __repr__(self):
- return str(self)
-
- def __hash__(self):
- return hash((self._base_type, self.const, self.restrict))
-
-
-class StructType:
- def __init__(self, numpy_type, const=False):
- self.const = const
- self._dtype = np.dtype(numpy_type)
-
- def __getnewargs__(self):
- return self.numpy_dtype, self.const
-
- def __getnewargs_ex__(self):
- return (self.numpy_dtype, self.const), {}
-
- @property
- def base_type(self):
- return None
-
- @property
- def numpy_dtype(self):
- return self._dtype
-
- @property
- def item_size(self):
- return self.numpy_dtype.itemsize
-
- def get_element_offset(self, element_name):
- return self.numpy_dtype.fields[element_name][1]
-
- def get_element_type(self, element_name):
- np_element_type = self.numpy_dtype.fields[element_name][0]
- return BasicType(np_element_type, self.const)
-
- def has_element(self, element_name):
- return element_name in self.numpy_dtype.fields
-
- def __eq__(self, other):
- if not isinstance(other, StructType):
- return False
- else:
- return (self.numpy_dtype, self.const) == (other.numpy_dtype, other.const)
-
- def __str__(self):
- # structs are handled byte-wise
- result = "uint8_t"
- if self.const:
- result += " const"
- return result
-
- def __repr__(self):
- return str(self)
-
- def __hash__(self):
- return hash((self.numpy_dtype, self.const))
-
-
-class TypedImaginaryUnit(TypedSymbol):
- def __new__(cls, *args, **kwds):
- obj = TypedImaginaryUnit.__xnew_cached_(cls, *args, **kwds)
- return obj
-
- def __new_stage2__(cls, dtype):
- obj = super(TypedImaginaryUnit, cls).__xnew__(cls,
- "_i",
- dtype,
- imaginary=True)
- return obj
-
- headers = ['"cuda_complex.hpp"']
-
- __xnew__ = staticmethod(__new_stage2__)
- __xnew_cached_ = staticmethod(cacheit(__new_stage2__))
-
- def __getnewargs__(self):
- return (self.dtype,)
-
- def __getnewargs_ex__(self):
- return (self.dtype,), {}
diff --git a/pystencils/datahandling/parallel_datahandling.py b/pystencils/datahandling/parallel_datahandling.py
index 1fb8fe0bea37c60e764068f038fb37586b094278..9d1e898d7368c22faf6c1699a619587cb1c613a1 100644
--- a/pystencils/datahandling/parallel_datahandling.py
+++ b/pystencils/datahandling/parallel_datahandling.py
@@ -9,7 +9,7 @@ from pystencils.datahandling.blockiteration import block_iteration, sliced_block
from pystencils.datahandling.datahandling_interface import DataHandling
from pystencils.enums import Backend
from pystencils.field import Field, FieldType
-from pystencils.kernelparameters import FieldPointerSymbol
+from pystencils.typing.typed_sympy import FieldPointerSymbol
from pystencils.utils import DotDict
from pystencils import Target
diff --git a/pystencils/display_utils.py b/pystencils/display_utils.py
index 3250765c83bafb46de7b878669dbe485a64dd91e..f6c32ac88ae68d684e860b33c0e5185ccc030e4e 100644
--- a/pystencils/display_utils.py
+++ b/pystencils/display_utils.py
@@ -10,7 +10,12 @@ from pystencils.kernel_wrapper import KernelWrapper
def to_dot(expr: sp.Expr, graph_style: Optional[Dict[str, Any]] = None, short=True):
"""Show a sympy or pystencils AST as dot graph"""
from pystencils.astnodes import Node
- import graphviz
+ try:
+ import graphviz
+ except ImportError:
+ print("graphviz is not installed. Visualizing the AST is not available")
+ return
+
graph_style = {} if graph_style is None else graph_style
if isinstance(expr, Node):
diff --git a/pystencils/fast_approximation.py b/pystencils/fast_approximation.py
index 9eee41a96f96d05b9fc9be3443a7291359369857..ab0dc59740e9ec7fcd3e59eb826979cd5350aa3f 100644
--- a/pystencils/fast_approximation.py
+++ b/pystencils/fast_approximation.py
@@ -9,16 +9,25 @@ from pystencils.assignment import Assignment
# noinspection PyPep8Naming
class fast_division(sp.Function):
+ """
+ Produces special float instructions for CUDA kernels
+ """
nargs = (2,)
# noinspection PyPep8Naming
class fast_sqrt(sp.Function):
+ """
+ Produces special float instructions for CUDA kernels
+ """
nargs = (1, )
# noinspection PyPep8Naming
class fast_inv_sqrt(sp.Function):
+ """
+ Produces special float instructions for CUDA kernels
+ """
nargs = (1, )
diff --git a/pystencils/fd/spatial.py b/pystencils/fd/spatial.py
index 2355906a85a4a5c6ff43af89f6d414ef9da41f76..387a03bac6c0c6f88b92851bc18b7d752d64b036 100644
--- a/pystencils/fd/spatial.py
+++ b/pystencils/fd/spatial.py
@@ -1,9 +1,9 @@
+from functools import lru_cache
from typing import Tuple
import sympy as sp
from pystencils.astnodes import LoopOverCoordinate
-from pystencils.cache import memorycache
from pystencils.fd import Diff
from pystencils.field import Field
from pystencils.transformations import generic_visit
@@ -136,7 +136,7 @@ def discretize_spatial_staggered(expr, dx, stencil=fd_stencils_standard):
# -------------------------------------- special stencils --------------------------------------------------------------
-@memorycache(maxsize=1)
+@lru_cache(maxsize=1)
def forth_order_2d_derivation() -> Tuple[FiniteDifferenceStencilDerivation.Result, ...]:
# Symmetry, isotropy and 4th order conditions are not enough to fully specify the stencil
# one weight has to be specifically set to a somewhat arbitrary value
diff --git a/pystencils/field.py b/pystencils/field.py
index dcb33ca99c35884fe94692b2f46e3bc0c77a04ba..e92cac4046400061b19d851755af63db76dea110 100644
--- a/pystencils/field.py
+++ b/pystencils/field.py
@@ -13,13 +13,13 @@ from sympy.core.cache import cacheit
import pystencils
from pystencils.alignedarray import aligned_empty
-from pystencils.data_types import StructType, TypedSymbol, create_type
-from pystencils.kernelparameters import FieldShapeSymbol, FieldStrideSymbol
+from pystencils.typing import StructType, TypedSymbol, BasicType, create_type
+from pystencils.typing.typed_sympy import FieldShapeSymbol, FieldStrideSymbol
from pystencils.stencil import (
direction_string_to_offset, inverse_direction, offset_to_direction_string)
from pystencils.sympyextensions import is_integer_sequence
-__all__ = ['Field', 'fields', 'FieldType', 'AbstractField']
+__all__ = ['Field', 'fields', 'FieldType', 'Field']
class FieldType(Enum):
@@ -137,12 +137,7 @@ def fields(description=None, index_dimensions=0, layout=None, field_type=FieldTy
return result
-class AbstractField:
- class AbstractAccess:
- pass
-
-
-class Field(AbstractField):
+class Field:
"""
With fields one can formulate stencil-like update rules on structured grids.
This Field class knows about the dimension, memory layout (strides) and optionally about the size of an array.
@@ -472,27 +467,6 @@ class Field(AbstractField):
assert FieldType.is_custom(self)
return Field.Access(self, offset, index, is_absolute_access=True)
- def interpolated_access(self,
- offset: Tuple,
- interpolation_mode='linear',
- address_mode='BORDER',
- allow_textures=True):
- """Provides access to field values at non-integer positions
-
- ``interpolated_access`` is similar to :func:`Field.absolute_access` except that
- it allows non-integer offsets and automatic handling of out-of-bound accesses.
-
- :param offset: Tuple of spatial coordinates (can be floats)
- :param interpolation_mode: One of :class:`pystencils.interpolation_astnodes.InterpolationMode`
- :param address_mode: How boundaries are handled can be 'border', 'wrap', 'mirror', 'clamp'
- :param allow_textures: Allow implementation by texture accesses on GPUs
- """
- from pystencils.interpolation_astnodes import Interpolator
- return Interpolator(self,
- interpolation_mode,
- address_mode,
- allow_textures=allow_textures).at(offset)
-
def staggered_access(self, offset, index=None):
"""If this field is a staggered field, it can be accessed using half-integer offsets.
For example, an offset of ``(0, sp.Rational(1,2))`` or ``"E"`` corresponds to the staggered point to the east
@@ -645,7 +619,7 @@ class Field(AbstractField):
self.coordinate_origin = -sp.Matrix([i / 2 for i in self.spatial_shape])
# noinspection PyAttributeOutsideInit,PyUnresolvedReferences
- class Access(TypedSymbol, AbstractField.AbstractAccess):
+ class Access(TypedSymbol):
"""Class representing a relative access into a `Field`.
This class behaves like a normal sympy Symbol, it is actually derived from it. One can built up
@@ -699,7 +673,11 @@ class Field(AbstractField):
if superscript is not None:
symbol_name += "^" + superscript
- obj = super(Field.Access, self).__xnew__(self, symbol_name, field.dtype)
+ if dtype:
+ obj = super(Field.Access, self).__xnew__(self, symbol_name, dtype)
+ else:
+ obj = super(Field.Access, self).__xnew__(self, symbol_name, field.dtype)
+
obj._field = field
obj._offsets = []
for o in offsets:
@@ -742,7 +720,11 @@ class Field(AbstractField):
if len(idx) != self.field.index_dimensions:
raise ValueError(f"Wrong number of indices: Got {len(idx)}, expected {self.field.index_dimensions}")
- return Field.Access(self.field, self._offsets, idx, dtype=self.dtype)
+ if len(idx) == 1 and isinstance(idx[0], str):
+ dtype = BasicType(self.field.dtype.numpy_dtype[idx[0]])
+ return Field.Access(self.field, self._offsets, idx, dtype=dtype)
+ else:
+ return Field.Access(self.field, self._offsets, idx, dtype=self.dtype)
def __getitem__(self, *idx):
return self.__call__(*idx)
diff --git a/pystencils/functions.py b/pystencils/functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..722c2c5d410dd81968cc593871c6714bbbba1645
--- /dev/null
+++ b/pystencils/functions.py
@@ -0,0 +1,57 @@
+import sympy as sp
+from pystencils.typing import PointerType
+
+
+class DivFunc(sp.Function):
+ """
+ DivFunc represents a division operation, since sympy represents divisions with ^-1
+ """
+ is_Atom = True
+ is_real = True
+
+ def __new__(cls, *args, **kwargs):
+ if len(args) != 2:
+ raise ValueError(f'{cls} takes only 2 arguments, instead {len(args)} received!')
+ divisor, dividend, *other_args = args
+
+ return sp.Function.__new__(cls, divisor, dividend, *other_args, **kwargs)
+
+ def _eval_evalf(self, *args, **kwargs):
+ return self.divisor.evalf() / self.dividend.evalf()
+
+ @property
+ def divisor(self):
+ return self.args[0]
+
+ @property
+ def dividend(self):
+ return self.args[1]
+
+
+class AddressOf(sp.Function):
+ """
+ AddressOf is the '&' operation in C. It gets the address of a lvalue.
+ """
+ is_Atom = True
+
+ def __new__(cls, arg):
+ obj = sp.Function.__new__(cls, arg)
+ return obj
+
+ @property
+ def canonical(self):
+ if hasattr(self.args[0], 'canonical'):
+ return self.args[0].canonical
+ else:
+ raise NotImplementedError()
+
+ @property
+ def is_commutative(self):
+ return self.args[0].is_commutative
+
+ @property
+ def dtype(self):
+ if hasattr(self.args[0], 'dtype'):
+ return PointerType(self.args[0].dtype, restrict=True)
+ else:
+ raise ValueError(f'pystencils supports only non void pointers. Current address_of type: {self.args[0]}')
diff --git a/pystencils/gpucuda/cudajit.py b/pystencils/gpucuda/cudajit.py
index 67adac65723d57c6f961517507bd140289ed5d90..b6fb901750895b341d44fde26040ff3b91d0e9e9 100644
--- a/pystencils/gpucuda/cudajit.py
+++ b/pystencils/gpucuda/cudajit.py
@@ -2,11 +2,11 @@ import numpy as np
from pystencils.backends.cbackend import get_headers
from pystencils.backends.cuda_backend import generate_cuda
-from pystencils.data_types import StructType
+from pystencils.typing import StructType
from pystencils.field import FieldType
from pystencils.include import get_pycuda_include_path, get_pystencils_include_path
from pystencils.kernel_wrapper import KernelWrapper
-from pystencils.kernelparameters import FieldPointerSymbol
+from pystencils.typing.typed_sympy import FieldPointerSymbol
USE_FAST_MATH = True
diff --git a/pystencils/gpucuda/indexing.py b/pystencils/gpucuda/indexing.py
index ae5db1b984d1ddc6f33ec0437b3f9fdc44ea48a4..6f30b0a1c10d00e2c83f7944e7c80aaa385f3f4c 100644
--- a/pystencils/gpucuda/indexing.py
+++ b/pystencils/gpucuda/indexing.py
@@ -5,7 +5,7 @@ import sympy as sp
from sympy.core.cache import cacheit
from pystencils.astnodes import Block, Conditional
-from pystencils.data_types import TypedSymbol, create_type
+from pystencils.typing import TypedSymbol, create_type
from pystencils.integer_functions import div_ceil, div_floor
from pystencils.slicing import normalize_slice
from pystencils.sympyextensions import is_integer_sequence, prod
diff --git a/pystencils/gpucuda/kernelcreation.py b/pystencils/gpucuda/kernelcreation.py
index 39808eab0e5434c56754fe6edb677add5cc50f95..a50953b64e720a50f41b83fea0f6b376834fd257 100644
--- a/pystencils/gpucuda/kernelcreation.py
+++ b/pystencils/gpucuda/kernelcreation.py
@@ -1,25 +1,36 @@
+from typing import Union
+
import numpy as np
from pystencils.astnodes import Block, KernelFunction, LoopOverCoordinate, SympyAssignment
-from pystencils.data_types import StructType, TypedSymbol
+from pystencils.config import CreateKernelConfig
+from pystencils.typing import StructType, TypedSymbol
+from pystencils.typing.transformations import add_types
from pystencils.field import Field, FieldType
from pystencils.enums import Target, Backend
from pystencils.gpucuda.cudajit import make_python_function
-from pystencils.gpucuda.indexing import BlockIndexing
+from pystencils.node_collection import NodeCollection
+from pystencils.gpucuda.indexing import indexing_creator_from_params
+from pystencils.simp.assignment_collection import AssignmentCollection
from pystencils.transformations import (
- add_types, get_base_buffer_index, get_common_shape, parse_base_pointer_info,
+ get_base_buffer_index, get_common_shape, parse_base_pointer_info,
resolve_buffer_accesses, resolve_field_accesses, unify_shape_symbols)
-def create_cuda_kernel(assignments,
- function_name="kernel",
- type_info=None,
- indexing_creator=BlockIndexing,
- iteration_slice=None,
- ghost_layers=None,
- skip_independence_check=False):
- assert assignments, "Assignments must not be empty!"
- fields_read, fields_written, assignments = add_types(assignments, type_info, not skip_independence_check)
+def create_cuda_kernel(assignments: Union[AssignmentCollection, NodeCollection],
+ config: CreateKernelConfig):
+
+ function_name = config.function_name
+ indexing_creator = indexing_creator_from_params(config.gpu_indexing, config.gpu_indexing_params)
+ iteration_slice = config.iteration_slice
+ ghost_layers = config.ghost_layers
+
+ fields_written = assignments.bound_fields
+ fields_read = assignments.rhs_fields
+ assignments = assignments.all_assignments
+
+ assignments = add_types(assignments, config)
+
all_fields = fields_read.union(fields_written)
read_only_fields = set([f.name for f in fields_read - fields_written])
@@ -102,13 +113,20 @@ def create_cuda_kernel(assignments,
return ast
-def created_indexed_cuda_kernel(assignments,
- index_fields,
- function_name="kernel",
- type_info=None,
- coordinate_names=('x', 'y', 'z'),
- indexing_creator=BlockIndexing):
- fields_read, fields_written, assignments = add_types(assignments, type_info, check_independence_condition=False)
+def created_indexed_cuda_kernel(assignments: Union[AssignmentCollection, NodeCollection],
+ config: CreateKernelConfig):
+
+ index_fields = config.index_fields
+ function_name = config.function_name
+ coordinate_names = config.coordinate_names
+ indexing_creator = indexing_creator_from_params(config.gpu_indexing, config.gpu_indexing_params)
+
+ fields_written = assignments.bound_fields
+ fields_read = assignments.rhs_fields
+ assignments = assignments.all_assignments
+
+ assignments = add_types(assignments, config)
+
all_fields = fields_read.union(fields_written)
read_only_fields = set([f.name for f in fields_read - fields_written])
diff --git a/pystencils/gpucuda/periodicity.py b/pystencils/gpucuda/periodicity.py
index cb9cd7ad1b8ae61e97cde6d496910ce6a2f7b960..7cad51654de75c2462d7d846baccc916aa102d4d 100644
--- a/pystencils/gpucuda/periodicity.py
+++ b/pystencils/gpucuda/periodicity.py
@@ -1,9 +1,9 @@
import numpy as np
from itertools import product
+from pystencils import CreateKernelConfig, create_kernel
import pystencils.gpucuda
from pystencils import Assignment, Field
-from pystencils.gpucuda.kernelcreation import create_cuda_kernel
from pystencils.enums import Target
from pystencils.slicing import get_periodic_boundary_src_dst_slices, normalize_slice
@@ -26,12 +26,14 @@ def create_copy_kernel(domain_size, from_slice, to_slice, index_dimensions=0, in
eq = Assignment(f(*i), f[tuple(offset)](*i))
update_eqs.append(eq)
- ast = create_cuda_kernel(update_eqs, iteration_slice=to_slice, skip_independence_check=True)
+ config = CreateKernelConfig(target=Target.GPU, iteration_slice=to_slice, skip_independence_check=True)
+
+ ast = create_kernel(update_eqs, config=config)
return ast
def get_periodic_boundary_functor(stencil, domain_size, index_dimensions=0, index_dim_shape=1, ghost_layers=1,
- thickness=None, dtype=float, target=Target.GPU):
+ thickness=None, dtype=np.float64, target=Target.GPU):
assert target in {Target.GPU}
src_dst_slice_tuples = get_periodic_boundary_src_dst_slices(stencil, ghost_layers, thickness)
kernels = []
diff --git a/pystencils/integer_functions.py b/pystencils/integer_functions.py
index efdaaaecf5ebc572e2fb4b16edb5c0050b5a9c2e..cd0e6f231edc754bcf4c5d7e991e6048c623a45c 100644
--- a/pystencils/integer_functions.py
+++ b/pystencils/integer_functions.py
@@ -1,7 +1,8 @@
+# TODO #47 move to a module functions
import numpy as np
import sympy as sp
-from pystencils.data_types import cast_func, collate_types, create_type, get_type_of_expression
+from pystencils.typing import CastFunc, collate_types, create_type, get_type_of_expression
from pystencils.sympyextensions import is_integer_sequence
@@ -12,9 +13,9 @@ class IntegerFunctionTwoArgsMixIn(sp.Function):
args = []
for a in (arg1, arg2):
if isinstance(a, sp.Number) or isinstance(a, int):
- args.append(cast_func(a, create_type("int")))
+ args.append(CastFunc(a, create_type("int")))
elif isinstance(a, np.generic):
- args.append(cast_func(a, a.dtype))
+ args.append(CastFunc(a, a.dtype))
else:
args.append(a)
diff --git a/pystencils/integer_set_analysis.py b/pystencils/integer_set_analysis.py
index 82af791caf805877089ba957afcff517669f4b6b..00fc1cb960c6fc1fd718bdc669df607285af8749 100644
--- a/pystencils/integer_set_analysis.py
+++ b/pystencils/integer_set_analysis.py
@@ -4,7 +4,8 @@ import islpy as isl
import sympy as sp
import pystencils.astnodes as ast
-from pystencils.transformations import parents_of_type
+from pystencils.typing import parents_of_type
+from pystencils.backends.cbackend import CustomSympyPrinter
def remove_brackets(s):
@@ -51,11 +52,13 @@ def simplify_loop_counter_dependent_conditional(conditional):
dofs_in_loops, iteration_set = isl_iteration_set(conditional)
if dofs_in_condition.issubset(dofs_in_loops):
symbol_names = ','.join(dofs_in_loops)
- condition_str = remove_brackets(str(conditional.condition_expr))
+ condition_str = CustomSympyPrinter().doprint(conditional.condition_expr)
+ condition_str = remove_brackets(condition_str)
condition_set = isl.BasicSet(f"{{ [{symbol_names}] : {condition_str} }}")
if condition_set.is_empty():
conditional.replace_by_false_block()
+ return
intersection = iteration_set.intersect(condition_set)
if intersection.is_empty():
diff --git a/pystencils/kernel_contrains_check.py b/pystencils/kernel_contrains_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1fa4b8a141400c0880672f4fdbcd356b59d4ccd
--- /dev/null
+++ b/pystencils/kernel_contrains_check.py
@@ -0,0 +1,132 @@
+from collections import namedtuple, defaultdict
+from typing import Union
+
+import sympy as sp
+from sympy.codegen import Assignment
+
+from pystencils.simp import AssignmentCollection
+from pystencils import astnodes as ast, TypedSymbol
+from pystencils.field import Field
+from pystencils.node_collection import NodeCollection
+from pystencils.transformations import NestedScopes
+
+# TODO use this in Constraint Checker
+accepted_functions = [
+ sp.Pow,
+ sp.sqrt,
+ sp.log,
+ # TODO trigonometric functions (and whatever tests will fail)
+]
+
+
+class KernelConstraintsCheck:
+ # TODO: proper specification
+ # TODO: More checks :)
+ """Checks if the input to create_kernel is valid.
+
+ Test the following conditions:
+
+ - SSA Form for pure symbols:
+ - Every pure symbol may occur only once as left-hand-side of an assignment
+ - Every pure symbol that is read, may not be written to later
+ - Independence / Parallelization condition:
+ - a field that is written may only be read at exact the same spatial position
+
+ (Pure symbols are symbols that are not Field.Accesses)
+ """
+ FieldAndIndex = namedtuple('FieldAndIndex', ['field', 'index'])
+
+ def __init__(self, check_independence_condition=True, check_double_write_condition=True):
+ self.scopes = NestedScopes()
+ self.field_writes = defaultdict(set)
+ self.fields_read = set()
+ self.check_independence_condition = check_independence_condition
+ self.check_double_write_condition = check_double_write_condition
+
+ def visit(self, obj):
+ if isinstance(obj, (AssignmentCollection, NodeCollection)):
+ [self.visit(e) for e in obj.all_assignments]
+ elif isinstance(obj, list) or isinstance(obj, tuple):
+ [self.visit(e) for e in obj]
+ elif isinstance(obj, (sp.Eq, ast.SympyAssignment, Assignment)):
+ self.process_assignment(obj)
+ elif isinstance(obj, ast.Conditional):
+ self.scopes.push()
+ # Disable double write check inside conditionals
+ # would be triggered by e.g. in-kernel boundaries
+ old_double_write = self.check_double_write_condition
+ old_independence_condition = self.check_independence_condition
+ self.check_double_write_condition = False
+ self.check_independence_condition = False
+ if obj.false_block:
+ self.visit(obj.false_block)
+ self.process_expression(obj.condition_expr)
+ self.process_expression(obj.true_block)
+ self.check_double_write_condition = old_double_write
+ self.check_independence_condition = old_independence_condition
+ self.scopes.pop()
+ elif isinstance(obj, ast.Block):
+ self.scopes.push()
+ [self.visit(e) for e in obj.args]
+ self.scopes.pop()
+ elif isinstance(obj, ast.Node) and not isinstance(obj, ast.LoopOverCoordinate):
+ pass
+ else:
+ raise ValueError(f'Invalid object in kernel {type(obj)}')
+
+ def process_assignment(self, assignment: Union[sp.Eq, ast.SympyAssignment, Assignment]):
+ # for checks it is crucial to process rhs before lhs to catch e.g. a = a + 1
+ self.process_expression(assignment.rhs)
+ self.process_lhs(assignment.lhs)
+
+ def process_expression(self, rhs):
+ # TODO constraint for accepted functions, see TODO above
+ self.update_accesses_rhs(rhs)
+ if isinstance(rhs, Field.Access):
+ self.fields_read.add(rhs.field)
+ self.fields_read.update(rhs.indirect_addressing_fields)
+ else:
+ for arg in rhs.args:
+ self.process_expression(arg)
+
+ @property
+ def fields_written(self):
+ """
+ Return all rhs fields
+ """
+ return set(k.field for k, v in self.field_writes.items() if len(v))
+
+ def process_lhs(self, lhs: Union[Field.Access, TypedSymbol, sp.Symbol]):
+ assert isinstance(lhs, sp.Symbol)
+ self.update_accesses_lhs(lhs)
+
+ def update_accesses_lhs(self, lhs):
+ if isinstance(lhs, Field.Access):
+ fai = self.FieldAndIndex(lhs.field, lhs.index)
+ if self.check_double_write_condition and lhs.offsets in self.field_writes[fai]:
+ raise ValueError(f"Field {lhs.field.name} is written twice at the same location")
+
+ self.field_writes[fai].add(lhs.offsets)
+
+ if self.check_double_write_condition and len(self.field_writes[fai]) > 1:
+ raise ValueError(
+ f"Field {lhs.field.name} is written at two different locations")
+ elif isinstance(lhs, sp.Symbol):
+ if self.scopes.is_defined_locally(lhs):
+ raise ValueError(f"Assignments not in SSA form, multiple assignments to {lhs.name}")
+ if lhs in self.scopes.free_parameters:
+ raise ValueError(f"Symbol {lhs.name} is written, after it has been read")
+ self.scopes.define_symbol(lhs)
+
+ def update_accesses_rhs(self, rhs):
+ if isinstance(rhs, Field.Access) and self.check_independence_condition:
+ writes = self.field_writes[self.FieldAndIndex(
+ rhs.field, rhs.index)]
+ for write_offset in writes:
+ assert len(writes) == 1
+ if write_offset != rhs.offsets:
+ raise ValueError(f"Violation of loop independence condition. Field "
+ f"{rhs.field} is read at {rhs.offsets} and written at {write_offset}")
+ self.fields_read.add(rhs.field)
+ elif isinstance(rhs, sp.Symbol):
+ self.scopes.access_symbol(rhs)
diff --git a/pystencils/kernel_decorator.py b/pystencils/kernel_decorator.py
index 19938e1507fd82970f15a2c2926a01775d18519d..ad5d625929058ef383402e2a1d97c0dfadbf5fda 100644
--- a/pystencils/kernel_decorator.py
+++ b/pystencils/kernel_decorator.py
@@ -7,7 +7,7 @@ import sympy as sp
from pystencils.assignment import Assignment
from pystencils.sympyextensions import SymbolCreator
-from pystencils.kernelcreation import CreateKernelConfig
+from pystencils.config import CreateKernelConfig
__all__ = ['kernel', 'kernel_config']
@@ -77,10 +77,10 @@ def kernel_config(config: CreateKernelConfig, **kwargs) -> Callable[..., Dict]:
and updates the function name accordingly.
Changes the meaning of the '@=' operator. Each line containing this operator gives a symbolic assignment
- in the result list. Furthermore the meaning of the ternary inline 'if-else' changes meaning to denote a
+ in the result list. Furthermore, the meaning of the ternary inline 'if-else' changes meaning to denote a
sympy Piecewise.
- The decorated function may not receive any arguments, with exception of an argument called 's' that specifies
+ The decorated function may not receive any arguments, with exception to an argument called 's' that specifies
a SymbolCreator()
Args:
config: Specify whether to return the list with assignments, or a dictionary containing additional settings
@@ -90,14 +90,14 @@ def kernel_config(config: CreateKernelConfig, **kwargs) -> Callable[..., Dict]:
Examples:
>>> import pystencils as ps
- >>> config = ps.CreateKernelConfig()
- >>> @kernel_config(config)
+ >>> kernel_configuration = ps.CreateKernelConfig()
+ >>> @kernel_config(kernel_configuration)
... def my_kernel(s):
- ... f, g = ps.fields('f, g: [2D]')
- ... s.neighbors @= f[0,1] + f[1,0]
- ... g[0,0] @= s.neighbors + f[0,0] if f[0,0] > 0 else 0
- >>> f, g = ps.fields('f, g: [2D]')
- >>> assert my_kernel['assignments'][0].rhs == f[0,1] + f[1,0]
+ ... src, dst = ps.fields('src, dst: [2D]')
+ ... s.neighbors @= src[0, 1] + src[1, 0]
+ ... dst[0, 0] @= s.neighbors + src[0, 0] if src[0, 0] > 0 else 0
+ >>> f, g = ps.fields('src, dst: [2D]')
+ >>> assert my_kernel['assignments'][0].rhs == f[0, 1] + f[1, 0]
"""
def decorator(func: Callable[..., None]) -> Union[List[Assignment], Dict]:
"""
diff --git a/pystencils/kernelcreation.py b/pystencils/kernelcreation.py
index 673eb54c01da503772a482b6b6e1b04bf35654d8..4b02ca13dfd66731747d80978770d976993f61cc 100644
--- a/pystencils/kernelcreation.py
+++ b/pystencils/kernelcreation.py
@@ -1,137 +1,25 @@
import itertools
import warnings
-from dataclasses import dataclass, field
-from types import MappingProxyType
-from typing import Callable, Union, List, Dict, Tuple, Any
+from typing import Union, List
import sympy as sp
+from pystencils.config import CreateKernelConfig
from pystencils.assignment import Assignment
-from pystencils.astnodes import Block, Conditional, LoopOverCoordinate, SympyAssignment
+from pystencils.astnodes import Node, Block, Conditional, LoopOverCoordinate, SympyAssignment
from pystencils.cpu.vectorization import vectorize
from pystencils.enums import Target, Backend
from pystencils.field import Field, FieldType
-from pystencils.gpucuda.indexing import indexing_creator_from_params
+from pystencils.node_collection import NodeCollection
from pystencils.simp.assignment_collection import AssignmentCollection
-from pystencils.simp.simplifications import apply_sympy_optimisations
+from pystencils.kernel_contrains_check import KernelConstraintsCheck
from pystencils.simplificationfactory import create_simplification_strategy
from pystencils.stencil import direction_string_to_offset, inverse_direction_string
from pystencils.transformations import (
loop_blocking, move_constants_before_loop, remove_conditionals_in_staggered_kernel)
-@dataclass
-class CreateKernelConfig:
- """
- **Below all parameters for the CreateKernelConfig are explained**
- """
- target: Target = Target.CPU
- """
- All targets are defined in :class:`pystencils.enums.Target`
- """
- backend: Backend = None
- """
- All backends are defined in :class:`pystencils.enums.Backend`
- """
- function_name: str = 'kernel'
- """
- Name of the generated function - only important if generated code is written out
- """
- data_type: Union[str, dict] = 'double'
- """
- Data type used for all untyped symbols (i.e. non-fields), can also be a dict from symbol name to type
- """
- iteration_slice: Tuple = None
- """
- Rectangular subset to iterate over, if not specified the complete non-ghost layer part of the field is iterated over
- """
- ghost_layers: Union[bool, int, List[Tuple[int]]] = None
- """
- A single integer specifies the ghost layer count at all borders, can also be a sequence of
- pairs ``[(x_lower_gl, x_upper_gl), .... ]``. These layers are excluded from the iteration.
- If left to default, the number of ghost layers is determined automatically from the assignments.
- """
- skip_independence_check: bool = False
- """
- Don't check that loop iterations are independent. This is needed e.g. for
- periodicity kernel, that access the field outside the iteration bounds. Use with care!
- """
- cpu_openmp: Union[bool, int] = False
- """
- `True` or number of threads for OpenMP parallelization, `False` for no OpenMP. If set to `True`, the maximum number
- of available threads will be chosen.
- """
- cpu_vectorize_info: Dict = None
- """
- A dictionary with keys, 'vector_instruction_set', 'assume_aligned' and 'nontemporal'
- for documentation of these parameters see vectorize function. Example:
- '{'instruction_set': 'avx512', 'assume_aligned': True, 'nontemporal':True}'
- """
- cpu_blocking: Tuple[int] = None
- """
- A tuple of block sizes or `None` if no blocking should be applied
- """
- omp_single_loop: bool = True
- """
- If OpenMP is active: whether multiple outer loops are permitted
- """
- gpu_indexing: str = 'block'
- """
- Either 'block' or 'line' , or custom indexing class, see `AbstractIndexing`
- """
- gpu_indexing_params: MappingProxyType = field(default=MappingProxyType({}))
- """
- Dict with indexing parameters (constructor parameters of indexing class)
- e.g. for 'block' one can specify '{'block_size': (20, 20, 10) }'.
- """
- default_assignment_simplifications: bool = False
- """
- If `True` default simplifications are first performed on the Assignments. If problems occur during the
- simplification a warning will be thrown.
- Furthermore, it is essential to know that this is a two-stage process. The first stage of the process acts
- on the level of the `AssignmentCollection`. In this part, `create_simplification_strategy`
- from pystencils.simplificationfactory will be used to apply optimisations like insertion of constants to
- remove pressure from the registers. Thus the first part of the optimisations can only be executed if
- an `AssignmentCollection` is passed. The second part of the optimisation acts on the level of each Assignment
- individually. In this stage, all optimisations from `sympy.codegen.rewriting.optims_c99` are applied
- to each Assignment. Thus this stage can also be applied if a list of Assignments is passed.
- """
- cpu_prepend_optimizations: List[Callable] = field(default_factory=list)
- """
- List of extra optimizations to perform first on the AST.
- """
- use_auto_for_assignments: bool = False
- """
- If set to `True`, auto can be used in the generated code for data types. This makes the type system more robust.
- """
- index_fields: List[Field] = None
- """
- List of index fields, i.e. 1D fields with struct data type. If not `None`, `create_index_kernel`
- instead of `create_domain_kernel` is used.
- """
- coordinate_names: Tuple[str, Any] = ('x', 'y', 'z')
- """
- Name of the coordinate fields in the struct data type.
- """
-
- def __post_init__(self):
- # ---- Legacy parameters
- if isinstance(self.target, str):
- new_target = Target[self.target.upper()]
- warnings.warn(f'Target "{self.target}" as str is deprecated. Use {new_target} instead',
- category=DeprecationWarning)
- self.target = new_target
- # ---- Auto Backend
- if not self.backend:
- if self.target == Target.CPU:
- self.backend = Backend.C
- elif self.target == Target.GPU:
- self.backend = Backend.CUDA
- else:
- raise NotImplementedError(f'Target {self.target} has no default backend')
-
-
-def create_kernel(assignments: Union[Assignment, List[Assignment], AssignmentCollection, List[Conditional]], *,
+def create_kernel(assignments: Union[Assignment, List[Assignment], AssignmentCollection, List[Node], NodeCollection], *,
config: CreateKernelConfig = None, **kwargs):
"""
Creates abstract syntax tree (AST) of kernel, using a list of update equations.
@@ -174,6 +62,21 @@ def create_kernel(assignments: Union[Assignment, List[Assignment], AssignmentCol
if isinstance(assignments, Assignment):
assignments = [assignments]
assert assignments, "Assignments must not be empty!"
+ if isinstance(assignments, list):
+ assignments = NodeCollection(assignments)
+ elif isinstance(assignments, AssignmentCollection):
+ # TODO Markus check and doku
+ # --- applying first default simplifications
+ try:
+ if config.default_assignment_simplifications:
+ simplification = create_simplification_strategy()
+ assignments = simplification(assignments)
+ except Exception as e:
+ warnings.warn(f"It was not possible to apply the default pystencils optimisations to the "
+ f"AssignmentCollection due to the following problem :{e}")
+ simplification_hints = assignments.simplification_hints
+ assignments = NodeCollection.from_assignment_collection(assignments)
+ assignments.simplification_hints = simplification_hints
if config.index_fields:
return create_indexed_kernel(assignments, config=config)
@@ -181,10 +84,13 @@ def create_kernel(assignments: Union[Assignment, List[Assignment], AssignmentCol
return create_domain_kernel(assignments, config=config)
-def create_domain_kernel(assignments: List[Assignment], *, config: CreateKernelConfig):
+def create_domain_kernel(assignments: NodeCollection, *, config: CreateKernelConfig):
"""
Creates abstract syntax tree (AST) of kernel, using a list of update equations.
+ Note that `create_domain_kernel` is a lower level function which shoul be accessed by not providing `index_fields`
+ to create_kernel
+
Args:
assignments: can be a single assignment, sequence of assignments or an `AssignmentCollection`
config: CreateKernelConfig which includes the needed configuration
@@ -196,10 +102,12 @@ def create_domain_kernel(assignments: List[Assignment], *, config: CreateKernelC
Example:
>>> import pystencils as ps
>>> import numpy as np
+ >>> from pystencils.kernelcreation import create_domain_kernel
+ >>> from pystencils.node_collection import NodeCollection
>>> s, d = ps.fields('s, d: [2D]')
>>> assignment = ps.Assignment(d[0,0], s[0, 1] + s[0, -1] + s[1, 0] + s[-1, 0])
>>> kernel_config = ps.CreateKernelConfig(cpu_openmp=True)
- >>> kernel_ast = ps.kernelcreation.create_domain_kernel([assignment], config=kernel_config)
+ >>> kernel_ast = create_domain_kernel(NodeCollection([assignment]), config=kernel_config)
>>> kernel = kernel_ast.compile()
>>> d_arr = np.zeros([5, 5])
>>> kernel(d=d_arr, s=np.ones([5, 5]))
@@ -210,38 +118,24 @@ def create_domain_kernel(assignments: List[Assignment], *, config: CreateKernelC
[0., 4., 4., 4., 0.],
[0., 0., 0., 0., 0.]])
"""
- # --- applying first default simplifications
- try:
- if config.default_assignment_simplifications and isinstance(assignments, AssignmentCollection):
- simplification = create_simplification_strategy()
- assignments = simplification(assignments)
- except Exception as e:
- warnings.warn(f"It was not possible to apply the default pystencils optimisations to the "
- f"AssignmentCollection due to the following problem :{e}")
+ # --- eval
+ assignments.evaluate_terms()
- # ---- Normalizing parameters
- split_groups = ()
- if isinstance(assignments, AssignmentCollection):
- if 'split_groups' in assignments.simplification_hints:
- split_groups = assignments.simplification_hints['split_groups']
- assignments = assignments.all_assignments
+ # FUTURE WORK from here we shouldn't NEED sympy
+ # --- check constrains
+ check = KernelConstraintsCheck(check_independence_condition=not config.skip_independence_check,
+ check_double_write_condition=not config.allow_double_writes)
+ check.visit(assignments)
- try:
- if config.default_assignment_simplifications:
- assignments = apply_sympy_optimisations(assignments)
- except Exception as e:
- warnings.warn(f"It was not possible to apply the default SymPy optimisations to the "
- f"Assignments due to the following problem :{e}")
+ assignments.bound_fields = check.fields_written
+ assignments.rhs_fields = check.fields_read
# ---- Creating ast
ast = None
if config.target == Target.CPU:
if config.backend == Backend.C:
from pystencils.cpu import add_openmp, create_kernel
- ast = create_kernel(assignments, function_name=config.function_name, type_info=config.data_type,
- split_groups=split_groups,
- iteration_slice=config.iteration_slice, ghost_layers=config.ghost_layers,
- skip_independence_check=config.skip_independence_check)
+ ast = create_kernel(assignments, config=config)
for optimization in config.cpu_prepend_optimizations:
optimization(ast)
omp_collapse = None
@@ -266,11 +160,7 @@ def create_domain_kernel(assignments: List[Assignment], *, config: CreateKernelC
elif config.target == Target.GPU:
if config.backend == Backend.CUDA:
from pystencils.gpucuda import create_cuda_kernel
- ast = create_cuda_kernel(assignments, function_name=config.function_name, type_info=config.data_type,
- indexing_creator=indexing_creator_from_params(config.gpu_indexing,
- config.gpu_indexing_params),
- iteration_slice=config.iteration_slice, ghost_layers=config.ghost_layers,
- skip_independence_check=config.skip_independence_check)
+ ast = create_cuda_kernel(assignments, config=config)
if not ast:
raise NotImplementedError(
@@ -283,7 +173,7 @@ def create_domain_kernel(assignments: List[Assignment], *, config: CreateKernelC
return ast
-def create_indexed_kernel(assignments: List[Assignment], *, config: CreateKernelConfig):
+def create_indexed_kernel(assignments: NodeCollection, *, config: CreateKernelConfig):
"""
Similar to :func:`create_kernel`, but here not all cells of a field are updated but only cells with
coordinates which are stored in an index field. This traversal method can e.g. be used for boundary handling.
@@ -293,6 +183,9 @@ def create_indexed_kernel(assignments: List[Assignment], *, config: CreateKernel
'coordinate_names' parameter. The struct can have also other fields that can be read and written in the kernel, for
example boundary parameters.
+ Note that `create_indexed_kernel` is a lower level function which shoul be accessed by providing `index_fields`
+ to create_kernel
+
Args:
assignments: can be a single assignment, sequence of assignments or an `AssignmentCollection`
config: CreateKernelConfig which includes the needed configuration
@@ -303,7 +196,9 @@ def create_indexed_kernel(assignments: List[Assignment], *, config: CreateKernel
Example:
>>> import pystencils as ps
+ >>> from pystencils.node_collection import NodeCollection
>>> import numpy as np
+ >>> from pystencils.kernelcreation import create_indexed_kernel
>>>
>>> # Index field stores the indices of the cell to visit together with optional values
>>> index_arr_dtype = np.dtype([('x', np.int32), ('y', np.int32), ('val', np.double)])
@@ -314,7 +209,7 @@ def create_indexed_kernel(assignments: List[Assignment], *, config: CreateKernel
>>> s, d = ps.fields('s, d: [2D]')
>>> assignment = ps.Assignment(d[0, 0], 2 * s[0, 1] + 2 * s[1, 0] + idx_field('val'))
>>> kernel_config = ps.CreateKernelConfig(index_fields=[idx_field], coordinate_names=('x', 'y'))
- >>> kernel_ast = ps.create_indexed_kernel([assignment], config=kernel_config)
+ >>> kernel_ast = create_indexed_kernel(NodeCollection([assignment]), config=kernel_config)
>>> kernel = kernel_ast.compile()
>>> d_arr = np.zeros([5, 5])
>>> kernel(s=np.ones([5, 5]), d=d_arr, idx=index_arr)
@@ -324,23 +219,30 @@ def create_indexed_kernel(assignments: List[Assignment], *, config: CreateKernel
[0. , 0. , 4.2, 0. , 0. ],
[0. , 0. , 0. , 4.3, 0. ],
[0. , 0. , 0. , 0. , 0. ]])
+
"""
+ # --- eval
+ assignments.evaluate_terms()
+
+ # FUTURE WORK from here we shouldn't NEED sympy
+ # --- check constrains
+ check = KernelConstraintsCheck(check_independence_condition=not config.skip_independence_check,
+ check_double_write_condition=not config.allow_double_writes)
+ check.visit(assignments)
+
+ assignments.bound_fields = check.fields_written
+ assignments.rhs_fields = check.fields_read
+
ast = None
if config.target == Target.CPU and config.backend == Backend.C:
from pystencils.cpu import add_openmp, create_indexed_kernel
- ast = create_indexed_kernel(assignments, index_fields=config.index_fields, type_info=config.data_type,
- coordinate_names=config.coordinate_names)
+ ast = create_indexed_kernel(assignments, config=config)
if config.cpu_openmp:
add_openmp(ast, num_threads=config.cpu_openmp)
elif config.target == Target.GPU:
if config.backend == Backend.CUDA:
from pystencils.gpucuda import created_indexed_cuda_kernel
- idx_creator = indexing_creator_from_params(config.gpu_indexing, config.gpu_indexing_params)
- ast = created_indexed_cuda_kernel(assignments,
- config.index_fields,
- type_info=config.data_type,
- coordinate_names=config.coordinate_names,
- indexing_creator=idx_creator)
+ ast = created_indexed_cuda_kernel(assignments, config=config)
if not ast:
raise NotImplementedError(f'Indexed kernels are not yet supported for {config.target} with {config.backend}')
@@ -369,6 +271,7 @@ def create_staggered_kernel(assignments, target: Target = Target.CPU, gpu_exclus
Returns:
AST, see `create_kernel`
"""
+ # TODO: Add doku like in the other kernels
if 'ghost_layers' in kwargs:
assert kwargs['ghost_layers'] is None
del kwargs['ghost_layers']
@@ -462,11 +365,8 @@ def create_staggered_kernel(assignments, target: Target = Target.CPU, gpu_exclus
[SympyAssignment(s.lhs, s.rhs) for s in subexpressions if hasattr(s, 'lhs')] + \
[last_conditional]
- if target == Target.CPU:
- from pystencils.cpu import create_kernel as create_kernel_cpu
- ast = create_kernel_cpu(final_assignments, ghost_layers=ghost_layers, omp_single_loop=False, **kwargs)
- else:
- ast = create_kernel(final_assignments, ghost_layers=ghost_layers, target=target, **kwargs)
+ config = CreateKernelConfig(target=target, ghost_layers=ghost_layers, omp_single_loop=False, **kwargs)
+ ast = create_kernel(final_assignments, config=config)
return ast
for assignment in assignments:
@@ -483,6 +383,8 @@ def create_staggered_kernel(assignments, target: Target = Target.CPU, gpu_exclus
if 'cpu_prepend_optimizations' in kwargs:
prepend_optimizations += kwargs['cpu_prepend_optimizations']
del kwargs['cpu_prepend_optimizations']
- ast = create_kernel(final_assignments, ghost_layers=ghost_layers, target=target, omp_single_loop=False,
- cpu_prepend_optimizations=prepend_optimizations, **kwargs)
+
+ config = CreateKernelConfig(ghost_layers=ghost_layers, target=target, omp_single_loop=False,
+ cpu_prepend_optimizations=prepend_optimizations, **kwargs)
+ ast = create_kernel(final_assignments, config=config)
return ast
diff --git a/pystencils/node_collection.py b/pystencils/node_collection.py
new file mode 100644
index 0000000000000000000000000000000000000000..804a74e207bc79756a286feab2d14c59ff123713
--- /dev/null
+++ b/pystencils/node_collection.py
@@ -0,0 +1,72 @@
+from typing import List, Union
+
+import sympy
+import sympy as sp
+from sympy.codegen import Assignment
+from sympy.codegen.rewriting import ReplaceOptim, optimize
+
+from pystencils.astnodes import Block, Node, SympyAssignment
+from pystencils.backends.cbackend import CustomCodeNode
+from pystencils.functions import DivFunc
+from pystencils.simp import AssignmentCollection
+
+
+class NodeCollection:
+ def __init__(self, assignments: List[Union[Node, Assignment]]):
+ self.all_assignments = assignments
+
+ if all((isinstance(a, Assignment) for a in assignments)):
+ self.is_Nodes = False
+ self.is_Assignments = True
+ elif all((isinstance(n, Node) for n in assignments)):
+ self.is_Nodes = True
+ self.is_Assignments = False
+ else:
+ raise ValueError(f'The list "{assignments}" is mixed. Pass either a list of "pystencils.Assignments" '
+ f'or a list of "pystencils.astnodes.Node')
+
+ self.simplification_hints = {}
+
+ @staticmethod
+ def from_assignment_collection(assignment_collection: AssignmentCollection):
+ nodes = list()
+ for assignemt in assignment_collection.all_assignments:
+ if isinstance(assignemt, Assignment):
+ nodes.append(SympyAssignment(assignemt.lhs, assignemt.rhs))
+ elif isinstance(assignemt, Node):
+ nodes.append(assignemt)
+ else:
+ raise ValueError(f"Unknown node in the AssignmentCollection: {assignemt}")
+
+ return NodeCollection(nodes)
+
+ def evaluate_terms(self):
+ evaluate_constant_terms = ReplaceOptim(
+ lambda e: hasattr(e, 'is_constant') and e.is_constant and not e.is_integer,
+ lambda p: p.evalf())
+
+ evaluate_pow = ReplaceOptim(
+ lambda e: e.is_Pow and e.exp.is_Integer and abs(e.exp) <= 8,
+ lambda p: (
+ sp.UnevaluatedExpr(sp.Mul(*([p.base] * +p.exp), evaluate=False)) if p.exp > 0 else
+ DivFunc(sp.Integer(1), sp.Mul(*([p.base] * -p.exp), evaluate=False))
+ ))
+ sympy_optimisations = [evaluate_constant_terms, evaluate_pow]
+
+ if self.is_Nodes:
+ def visitor(node):
+ if isinstance(node, CustomCodeNode):
+ return node
+ elif isinstance(node, Block):
+ return node.func([visitor(child) for child in node.args])
+ elif isinstance(node, Node):
+ return node.func(*[visitor(child) for child in node.args])
+ elif isinstance(node, sympy.Basic):
+ return optimize(node, sympy_optimisations)
+ else:
+ raise NotImplementedError(f'{node} {type(node)} has no valid visitor')
+ self.all_assignments = [visitor(assignment) for assignment in self.all_assignments]
+ else:
+ self.all_assignments = [Assignment(a.lhs, optimize(a.rhs, sympy_optimisations))
+ if hasattr(a, 'lhs')
+ else a for a in self.all_assignments]
diff --git a/pystencils/rng.py b/pystencils/rng.py
index 7c4f894f9871e350fd9a5f531708d123dcb7be2b..c75c3f9727720d2d313adee3cda3eead520334c7 100644
--- a/pystencils/rng.py
+++ b/pystencils/rng.py
@@ -2,7 +2,7 @@ import copy
import numpy as np
import sympy as sp
-from pystencils.data_types import TypedSymbol, cast_func
+from pystencils.typing import TypedSymbol, CastFunc
from pystencils.astnodes import LoopOverCoordinate
from pystencils.backends.cbackend import CustomCodeNode
from pystencils.sympyextensions import fast_subs
@@ -47,11 +47,11 @@ class RNGBase(CustomCodeNode):
def get_code(self, dialect, vector_instruction_set, print_arg):
code = "\n"
for r in self.result_symbols:
- if vector_instruction_set and not self.args[1].atoms(cast_func):
+ if vector_instruction_set and not self.args[1].atoms(CastFunc):
# this vector RNG has become scalar through substitution
code += f"{r.dtype} {r.name};\n"
else:
- code += f"{vector_instruction_set[r.dtype.base_name] if vector_instruction_set else r.dtype} " + \
+ code += f"{vector_instruction_set[r.dtype.c_name] if vector_instruction_set else r.dtype} " + \
f"{r.name};\n"
args = [print_arg(a) for a in self.args] + ['' + r.name for r in self.result_symbols]
code += (self._name + "(" + ", ".join(args) + ");\n")
diff --git a/pystencils/simp/assignment_collection.py b/pystencils/simp/assignment_collection.py
index 07d29f3dcb467285c601689819325016bc18be06..49fc06e2ddf6217f3425dacd33f1b913f6cd8c18 100644
--- a/pystencils/simp/assignment_collection.py
+++ b/pystencils/simp/assignment_collection.py
@@ -107,16 +107,21 @@ class AssignmentCollection:
return self.subexpressions + self.main_assignments
@property
- def free_symbols(self) -> Set[sp.Symbol]:
- """All symbols used in the assignment collection, which do not occur as left hand sides in any assignment."""
- free_symbols = set()
+ def rhs_symbols(self) -> Set[sp.Symbol]:
+ """All symbols used in the assignment collection, which occur on the rhs of any assignment."""
+ rhs_symbols = set()
for eq in self.all_assignments:
if isinstance(eq, Assignment):
- free_symbols.update(eq.rhs.atoms(sp.Symbol))
+ rhs_symbols.update(eq.rhs.atoms(sp.Symbol))
elif isinstance(eq, pystencils.astnodes.Node):
- free_symbols.update(eq.undefined_symbols)
+ rhs_symbols.update(eq.undefined_symbols)
+
+ return rhs_symbols
- return free_symbols - self.bound_symbols
+ @property
+ def free_symbols(self) -> Set[sp.Symbol]:
+ """All symbols used in the assignment collection, which do not occur as left hand sides in any assignment."""
+ return self.rhs_symbols - self.bound_symbols
@property
def bound_symbols(self) -> Set[sp.Symbol]:
@@ -131,11 +136,15 @@ class AssignmentCollection:
bound_symbols_set = bound_symbols_set.union(*[
assignment.symbols_defined for assignment in self.all_assignments
if isinstance(assignment, pystencils.astnodes.Node)
- ]
- )
+ ])
return bound_symbols_set
+ @property
+ def rhs_fields(self):
+ """All fields accessed in the assignment collection, which do not occur as left hand sides in any assignment."""
+ return {s.field for s in self.rhs_symbols if hasattr(s, 'field')}
+
@property
def free_fields(self):
"""All fields accessed in the assignment collection, which do not occur as left hand sides in any assignment."""
@@ -149,11 +158,9 @@ class AssignmentCollection:
@property
def defined_symbols(self) -> Set[sp.Symbol]:
"""All symbols which occur as left-hand-sides of one of the main equations"""
- return (set(
- [assignment.lhs for assignment in self.main_assignments if isinstance(assignment, Assignment)]
- ).union(*[assignment.symbols_defined for assignment in self.main_assignments if isinstance(
- assignment, pystencils.astnodes.Node)]
- ))
+ lhs_set = set([assignment.lhs for assignment in self.main_assignments if isinstance(assignment, Assignment)])
+ return (lhs_set.union(*[assignment.symbols_defined for assignment in self.main_assignments
+ if isinstance(assignment, pystencils.astnodes.Node)]))
@property
def operation_count(self):
@@ -214,6 +221,7 @@ class AssignmentCollection:
return {s: func(*args, **kwargs) for s, func in lambdas.items()}
return f
+
# ---------------------------- Creating new modified collections ---------------------------------------------------
def copy(self,
@@ -328,8 +336,10 @@ class AssignmentCollection:
new_eqs = [Assignment(eq.lhs, fast_subs(eq.rhs, subs_dict)) for eq in self.main_assignments]
return self.copy(new_eqs, new_subexpressions)
- def new_without_subexpressions(self, subexpressions_to_keep: Set[sp.Symbol] = set()) -> 'AssignmentCollection':
+ def new_without_subexpressions(self, subexpressions_to_keep=None) -> 'AssignmentCollection':
"""Returns a new collection where all subexpressions have been inserted."""
+ if subexpressions_to_keep is None:
+ subexpressions_to_keep = set()
if len(self.subexpressions) == 0:
return self.copy()
@@ -357,6 +367,7 @@ class AssignmentCollection:
def _repr_html_(self):
"""Interface to Jupyter notebook, to display as a nicely formatted HTML table"""
+
def make_html_equation_table(equations):
no_border = 'style="border:none"'
html_table = '<table style="border:none; width: 100%; ">'
diff --git a/pystencils/simp/simplifications.py b/pystencils/simp/simplifications.py
index 720abb52ad0f66e030fa7b5f922b8ab0771124bd..5ed8c4ea9ee62fca33933eead71be6fff5571704 100644
--- a/pystencils/simp/simplifications.py
+++ b/pystencils/simp/simplifications.py
@@ -3,12 +3,10 @@ from typing import Callable, List, Sequence, Union
from collections import defaultdict
import sympy as sp
-from sympy.codegen.rewriting import optims_c99, optimize
-from sympy.codegen.rewriting import ReplaceOptim
from pystencils.assignment import Assignment
-from pystencils.astnodes import Node, SympyAssignment
-from pystencils.field import AbstractField, Field
+from pystencils.astnodes import Node
+from pystencils.field import Field
from pystencils.sympyextensions import subs_additive, is_constant, recursive_collect
@@ -164,7 +162,7 @@ def add_subexpressions_for_sums(ac):
for eq in ac.all_assignments:
search_addends(eq.rhs)
- addends = [a for a in addends if not isinstance(a, sp.Symbol) or isinstance(a, AbstractField.AbstractAccess)]
+ addends = [a for a in addends if not isinstance(a, sp.Symbol) or isinstance(a, Field.Access)]
new_symbol_gen = ac.subexpression_symbol_generator
substitutions = {addend: new_symbol for new_symbol, addend in zip(new_symbol_gen, addends)}
return ac.new_with_substitutions(substitutions, True, substitute_on_lhs=False)
@@ -226,23 +224,30 @@ def apply_on_all_subexpressions(operation: Callable[[sp.Expr], sp.Expr]):
f.__name__ = operation.__name__
return f
-
-def apply_sympy_optimisations(assignments):
- """ Evaluates constant expressions (e.g. :math:`\\sqrt{3}` will be replaced by its floating point representation)
- and applies the default sympy optimisations. See sympy.codegen.rewriting
- """
-
- # Evaluates all constant terms
- evaluate_constant_terms = ReplaceOptim(lambda e: hasattr(e, 'is_constant') and e.is_constant and not e.is_integer,
- lambda p: p.evalf(17))
-
- sympy_optimisations = [evaluate_constant_terms] + list(optims_c99)
-
- assignments = [Assignment(a.lhs, optimize(a.rhs, sympy_optimisations))
- if hasattr(a, 'lhs')
- else a for a in assignments]
- assignments_nodes = [a.atoms(SympyAssignment) for a in assignments]
- for a in chain.from_iterable(assignments_nodes):
- a.optimize(sympy_optimisations)
-
- return assignments
+# TODO Markus
+# make this really work for Assignmentcollections
+# this function should ONLY evaluate
+# do the optims_c99 elsewhere optionally
+
+# def apply_sympy_optimisations(ac: AssignmentCollection):
+# """ Evaluates constant expressions (e.g. :math:`\\sqrt{3}` will be replaced by its floating point representation)
+# and applies the default sympy optimisations. See sympy.codegen.rewriting
+# """
+#
+# # Evaluates all constant terms
+#
+# assignments = ac.all_assignments
+#
+# evaluate_constant_terms = ReplaceOptim(lambda e: hasattr(e, 'is_constant') and e.is_constant and not e.is_integer,
+# lambda p: p.evalf())
+#
+# sympy_optimisations = [evaluate_constant_terms] + list(optims_c99)
+#
+# assignments = [Assignment(a.lhs, optimize(a.rhs, sympy_optimisations))
+# if hasattr(a, 'lhs')
+# else a for a in assignments]
+# assignments_nodes = [a.atoms(SympyAssignment) for a in assignments]
+# for a in chain.from_iterable(assignments_nodes):
+# a.optimize(sympy_optimisations)
+#
+# return AssignmentCollection(assignments)
diff --git a/pystencils/sympyextensions.py b/pystencils/sympyextensions.py
index b2c960396aecc87b106afd79eabdde767be33ba5..b07707edc8dafc7da5313bb6acef65f2e4381ad5 100644
--- a/pystencils/sympyextensions.py
+++ b/pystencils/sympyextensions.py
@@ -10,8 +10,8 @@ from sympy.functions import Abs
from sympy.core.numbers import Zero
from pystencils.assignment import Assignment
-from pystencils.data_types import cast_func, get_type_of_expression, PointerType, VectorType
-from pystencils.kernelparameters import FieldPointerSymbol
+from pystencils.typing import CastFunc, get_type_of_expression, PointerType, VectorType
+from pystencils.typing.typed_sympy import FieldPointerSymbol
T = TypeVar('T')
@@ -588,7 +588,7 @@ def count_operations(term: Union[sp.Expr, List[sp.Expr], List[Assignment]],
visit_children = False
elif t.is_integer:
pass
- elif isinstance(t, cast_func):
+ elif isinstance(t, CastFunc):
visit_children = False
visit(t.args[0])
elif t.func is fast_sqrt:
diff --git a/pystencils/transformations.py b/pystencils/transformations.py
index c2b6cf54b118340f7cf8d1280ddd17a08e56be94..c022e728db0c7df47368be26941842e9664b2c76 100644
--- a/pystencils/transformations.py
+++ b/pystencils/transformations.py
@@ -1,27 +1,21 @@
import hashlib
import pickle
import warnings
-from collections import OrderedDict, defaultdict, namedtuple
+from collections import OrderedDict
from copy import deepcopy
from types import MappingProxyType
-import numpy as np
import sympy as sp
-from sympy.core.numbers import ImaginaryUnit
-from sympy.logic.boolalg import Boolean, BooleanFunction
import pystencils.astnodes as ast
-import pystencils.integer_functions
from pystencils.assignment import Assignment
-from pystencils.data_types import (
- PointerType, StructType, TypedImaginaryUnit, TypedSymbol, cast_func, collate_types, create_type,
- get_base_type, get_type_of_expression, pointer_arithmetic_func, reinterpret_cast_func)
-from pystencils.field import AbstractField, Field, FieldType
-from pystencils.kernelparameters import FieldPointerSymbol
+from pystencils.typing import (
+ PointerType, StructType, TypedSymbol, get_base_type, ReinterpretCastFunc, get_next_parent_of_type, parents_of_type)
+from pystencils.field import Field, FieldType
+from pystencils.typing import FieldPointerSymbol
from pystencils.simp.assignment_collection import AssignmentCollection
from pystencils.slicing import normalize_slice
from pystencils.integer_functions import int_div
-from pystencils.bit_masks import flag_cond
class NestedScopes:
@@ -166,7 +160,7 @@ def make_loop_over_domain(body, iteration_slice=None, ghost_layers=None, loop_or
tuple of loop-node, ghost_layer_info
"""
# find correct ordering by inspecting participating FieldAccesses
- field_accesses = body.atoms(AbstractField.AbstractAccess)
+ field_accesses = body.atoms(Field.Access)
field_accesses = {e for e in field_accesses if not e.is_absolute_access}
# exclude accesses to buffers from field_list, because buffers are treated separately
@@ -359,13 +353,17 @@ def get_base_buffer_index(ast_node, loop_counters=None, loop_iterations=None):
assert len(loops) == len(parents_of_innermost_loop)
assert all(l1 is l2 for l1, l2 in zip(loops, parents_of_innermost_loop))
- actual_sizes = [int_div((l.stop - l.start), l.step) for l in loops]
- actual_steps = [int_div((l.loop_counter_symbol - l.start), l.step) for l in loops]
+ actual_sizes = [int_div((loop.stop - loop.start), loop.step)
+ if loop.step != 1 else loop.stop - loop.start for loop in loops]
+
+ actual_steps = [int_div((loop.loop_counter_symbol - loop.start), loop.step)
+ if loop.step != 1 else loop.loop_counter_symbol - loop.start for loop in loops]
+
else:
actual_sizes = loop_iterations
actual_steps = loop_counters
- field_accesses = ast_node.atoms(AbstractField.AbstractAccess)
+ field_accesses = ast_node.atoms(Field.Access)
buffer_accesses = {fa for fa in field_accesses if FieldType.is_buffer(fa.field)}
buffer_index_size = len(buffer_accesses)
@@ -378,10 +376,13 @@ def get_base_buffer_index(ast_node, loop_counters=None, loop_iterations=None):
return base_buffer_index * buffer_index_size
-def resolve_buffer_accesses(ast_node, base_buffer_index, read_only_field_names=set()):
+def resolve_buffer_accesses(ast_node, base_buffer_index, read_only_field_names=None):
+
+ if read_only_field_names is None:
+ read_only_field_names = set()
def visit_sympy_expr(expr, enclosing_block, sympy_assignment):
- if isinstance(expr, AbstractField.AbstractAccess):
+ if isinstance(expr, Field.Access):
field_access = expr
# Do not apply transformation if field is not a buffer
@@ -424,7 +425,7 @@ def resolve_buffer_accesses(ast_node, base_buffer_index, read_only_field_names=s
return visit_node(ast_node)
-def resolve_field_accesses(ast_node, read_only_field_names=set(),
+def resolve_field_accesses(ast_node, read_only_field_names=None,
field_to_base_pointer_info=MappingProxyType({}),
field_to_fixed_coordinates=MappingProxyType({})):
"""
@@ -441,11 +442,13 @@ def resolve_field_accesses(ast_node, read_only_field_names=set(),
Returns
transformed AST
"""
+ if read_only_field_names is None:
+ read_only_field_names = set()
field_to_base_pointer_info = OrderedDict(sorted(field_to_base_pointer_info.items(), key=lambda pair: pair[0]))
field_to_fixed_coordinates = OrderedDict(sorted(field_to_fixed_coordinates.items(), key=lambda pair: pair[0]))
def visit_sympy_expr(expr, enclosing_block, sympy_assignment):
- if isinstance(expr, AbstractField.AbstractAccess):
+ if isinstance(expr, Field.Access):
field_access = expr
field = field_access.field
@@ -461,10 +464,7 @@ def resolve_field_accesses(ast_node, read_only_field_names=set(),
if field.name in field_to_base_pointer_info:
base_pointer_info = field_to_base_pointer_info[field.name]
else:
- base_pointer_info = [
- list(
- range(field.index_dimensions + field.spatial_dimensions))
- ]
+ base_pointer_info = [list(range(field.index_dimensions + field.spatial_dimensions))]
field_ptr = FieldPointerSymbol(
field.name,
@@ -519,7 +519,7 @@ def resolve_field_accesses(ast_node, read_only_field_names=set(),
if isinstance(accessed_field_name, sp.Symbol):
accessed_field_name = accessed_field_name.name
new_type = field_access.field.dtype.get_element_type(accessed_field_name)
- result = reinterpret_cast_func(result, new_type)
+ result = ReinterpretCastFunc(result, new_type)
return visit_sympy_expr(result, enclosing_block, sympy_assignment)
else:
@@ -687,13 +687,13 @@ def split_inner_loop(ast_node: ast.Node, symbol_groups):
if s in assignment_map: # if there is no assignment inside the loop body it is independent already
for new_symbol in assignment_map[s].rhs.atoms(sp.Symbol):
- if not isinstance(new_symbol, AbstractField.AbstractAccess) and \
+ if not isinstance(new_symbol, Field.Access) and \
new_symbol not in symbols_with_temporary_array:
symbols_to_process.append(new_symbol)
symbols_resolved.add(s)
for symbol in symbol_group:
- if not isinstance(symbol, AbstractField.AbstractAccess):
+ if not isinstance(symbol, Field.Access):
assert type(symbol) is TypedSymbol
new_ts = TypedSymbol(symbol.name, PointerType(symbol.dtype))
symbols_with_temporary_array[symbol] = sp.IndexedBase(
@@ -704,7 +704,7 @@ def split_inner_loop(ast_node: ast.Node, symbol_groups):
if assignment.lhs in symbols_resolved:
new_rhs = assignment.rhs.subs(
symbols_with_temporary_array.items())
- if not isinstance(assignment.lhs, AbstractField.AbstractAccess) and assignment.lhs in symbol_group:
+ if not isinstance(assignment.lhs, Field.Access) and assignment.lhs in symbol_group:
assert type(assignment.lhs) is TypedSymbol
new_ts = TypedSymbol(assignment.lhs.name, PointerType(assignment.lhs.dtype))
new_lhs = sp.IndexedBase(new_ts, shape=(1, ))[inner_loop.loop_counter_symbol]
@@ -772,7 +772,8 @@ def simplify_conditionals(node: ast.Node, loop_counter_simplification: bool = Fa
default.
"""
for conditional in node.atoms(ast.Conditional):
- conditional.condition_expr = sp.simplify(conditional.condition_expr)
+ # TODO simplify conditional before the type system! Casts make it very hard here
+ # conditional.condition_expr = sp.simplify(conditional.condition_expr)
if conditional.condition_expr == sp.true:
conditional.parent.replace(conditional, [conditional.true_block])
elif conditional.condition_expr == sp.false:
@@ -801,292 +802,6 @@ def cleanup_blocks(node: ast.Node) -> None:
cleanup_blocks(a)
-class KernelConstraintsCheck:
- """Checks if the input to create_kernel is valid.
-
- Test the following conditions:
-
- - SSA Form for pure symbols:
- - Every pure symbol may occur only once as left-hand-side of an assignment
- - Every pure symbol that is read, may not be written to later
- - Independence / Parallelization condition:
- - a field that is written may only be read at exact the same spatial position
-
- (Pure symbols are symbols that are not Field.Accesses)
- """
- FieldAndIndex = namedtuple('FieldAndIndex', ['field', 'index'])
-
- def __init__(self, type_for_symbol, check_independence_condition, check_double_write_condition=True):
- self._type_for_symbol = type_for_symbol
-
- self.scopes = NestedScopes()
- self._field_writes = defaultdict(set)
- self.fields_read = set()
- self.check_independence_condition = check_independence_condition
- self.check_double_write_condition = check_double_write_condition
-
- def process_assignment(self, assignment):
- # for checks it is crucial to process rhs before lhs to catch e.g. a = a + 1
- new_rhs = self.process_expression(assignment.rhs)
- new_lhs = self._process_lhs(assignment.lhs)
- return ast.SympyAssignment(new_lhs, new_rhs)
-
- def process_expression(self, rhs, type_constants=True):
-
- self._update_accesses_rhs(rhs)
- if isinstance(rhs, AbstractField.AbstractAccess):
- self.fields_read.add(rhs.field)
- self.fields_read.update(rhs.indirect_addressing_fields)
- return rhs
- elif isinstance(rhs, ImaginaryUnit):
- return TypedImaginaryUnit(create_type(self._type_for_symbol['_complex_type']))
- elif isinstance(rhs, TypedSymbol):
- return rhs
- elif isinstance(rhs, sp.Symbol):
- return TypedSymbol(rhs.name, self._type_for_symbol[rhs.name])
- elif type_constants and isinstance(rhs, np.generic):
- return cast_func(rhs, create_type(rhs.dtype))
- elif type_constants and isinstance(rhs, sp.Number):
- return cast_func(rhs, create_type(self._type_for_symbol['_constant']))
- # Very important that this clause comes before BooleanFunction
- elif isinstance(rhs, sp.Equality):
- if isinstance(rhs.args[1], sp.Number):
- return sp.Equality(
- self.process_expression(rhs.args[0], type_constants),
- rhs.args[1])
- else:
- return sp.Equality(
- self.process_expression(rhs.args[0], type_constants),
- self.process_expression(rhs.args[1], type_constants))
- elif isinstance(rhs, cast_func):
- return cast_func(
- self.process_expression(rhs.args[0], type_constants=False),
- rhs.dtype)
- elif isinstance(rhs, BooleanFunction) or \
- type(rhs) in pystencils.integer_functions.__dict__.values():
- new_args = [self.process_expression(a, type_constants) for a in rhs.args]
- types_of_expressions = [get_type_of_expression(a) for a in new_args]
- arg_type = collate_types(types_of_expressions, forbid_collation_to_float=True)
- new_args = [a if not hasattr(a, 'dtype') or a.dtype == arg_type
- else cast_func(a, arg_type)
- for a in new_args]
- return rhs.func(*new_args)
- elif isinstance(rhs, flag_cond):
- # do not process the arguments to the bit shift - they must remain integers
- processed_args = (self.process_expression(a) for a in rhs.args[2:])
- return flag_cond(rhs.args[0], rhs.args[1], *processed_args)
- elif isinstance(rhs, sp.Mul):
- new_args = [
- self.process_expression(arg, type_constants)
- if arg not in (-1, 1) else arg for arg in rhs.args
- ]
- return rhs.func(*new_args) if new_args else rhs
- elif isinstance(rhs, sp.Indexed):
- return rhs
- else:
- if isinstance(rhs, sp.Pow):
- # don't process exponents -> they should remain integers
- return sp.Pow(
- self.process_expression(rhs.args[0], type_constants),
- rhs.args[1])
- else:
- new_args = [
- self.process_expression(arg, type_constants)
- for arg in rhs.args
- ]
- return rhs.func(*new_args) if new_args else rhs
-
- @property
- def fields_written(self):
- return set(k.field for k, v in self._field_writes.items() if len(v))
-
- def _process_lhs(self, lhs):
- assert isinstance(lhs, sp.Symbol)
- self._update_accesses_lhs(lhs)
- if not isinstance(lhs, (AbstractField.AbstractAccess, TypedSymbol)):
- return TypedSymbol(lhs.name, self._type_for_symbol[lhs.name])
- else:
- return lhs
-
- def _update_accesses_lhs(self, lhs):
- if isinstance(lhs, AbstractField.AbstractAccess):
- fai = self.FieldAndIndex(lhs.field, lhs.index)
- self._field_writes[fai].add(lhs.offsets)
- if self.check_double_write_condition and len(self._field_writes[fai]) > 1:
- raise ValueError(
- f"Field {lhs.field.name} is written at two different locations")
- elif isinstance(lhs, sp.Symbol):
- if self.scopes.is_defined_locally(lhs):
- raise ValueError(f"Assignments not in SSA form, multiple assignments to {lhs.name}")
- if lhs in self.scopes.free_parameters:
- raise ValueError(f"Symbol {lhs.name} is written, after it has been read")
- self.scopes.define_symbol(lhs)
-
- def _update_accesses_rhs(self, rhs):
- if isinstance(rhs, AbstractField.AbstractAccess) and self.check_independence_condition:
- writes = self._field_writes[self.FieldAndIndex(
- rhs.field, rhs.index)]
- for write_offset in writes:
- assert len(writes) == 1
- if write_offset != rhs.offsets:
- raise ValueError("Violation of loop independence condition. Field "
- "{} is read at {} and written at {}".format(rhs.field, rhs.offsets, write_offset))
- self.fields_read.add(rhs.field)
- elif isinstance(rhs, sp.Symbol):
- self.scopes.access_symbol(rhs)
-
-
-def add_types(eqs, type_for_symbol, check_independence_condition, check_double_write_condition=True):
- """Traverses AST and replaces every :class:`sympy.Symbol` by a :class:`pystencils.typedsymbol.TypedSymbol`.
-
- Additionally returns sets of all fields which are read/written
-
- Args:
- eqs: list of equations
- type_for_symbol: dict mapping symbol names to types. Types are strings of C types like 'int' or 'double'
- check_independence_condition: check that loop iterations are independent - this has to be skipped for indexed
- kernels
-
- Returns:
- ``fields_read, fields_written, typed_equations`` set of read fields, set of written fields,
- list of equations where symbols have been replaced by typed symbols
- """
- if isinstance(type_for_symbol, (str, type)) or not hasattr(type_for_symbol, '__getitem__'):
- type_for_symbol = typing_from_sympy_inspection(eqs, type_for_symbol)
-
- type_for_symbol = adjust_c_single_precision_type(type_for_symbol)
-
- check = KernelConstraintsCheck(type_for_symbol, check_independence_condition,
- check_double_write_condition=check_double_write_condition)
-
- def visit(obj):
- if isinstance(obj, (list, tuple)):
- return [visit(e) for e in obj]
- if isinstance(obj, (sp.Eq, ast.SympyAssignment, Assignment)):
- return check.process_assignment(obj)
- elif isinstance(obj, ast.Conditional):
- check.scopes.push()
- # Disable double write check inside conditionals
- # would be triggered by e.g. in-kernel boundaries
- old_double_write = check.check_double_write_condition
- check.check_double_write_condition = False
- false_block = None if obj.false_block is None else visit(
- obj.false_block)
- result = ast.Conditional(check.process_expression(
- obj.condition_expr, type_constants=False),
- true_block=visit(obj.true_block),
- false_block=false_block)
- check.check_double_write_condition = old_double_write
- check.scopes.pop()
- return result
- elif isinstance(obj, ast.Block):
- check.scopes.push()
- result = ast.Block([visit(e) for e in obj.args])
- check.scopes.pop()
- return result
- elif isinstance(obj, ast.Node) and not isinstance(obj, ast.LoopOverCoordinate):
- return obj
- else:
- raise ValueError("Invalid object in kernel " + str(type(obj)))
-
- typed_equations = visit(eqs)
-
- return check.fields_read, check.fields_written, typed_equations
-
-
-def insert_casts(node):
- """Checks the types and inserts casts and pointer arithmetic where necessary.
-
- Args:
- node: the head node of the ast
-
- Returns:
- modified AST
- """
- def cast(zipped_args_types, target_dtype):
- """
- Adds casts to the arguments if their type differs from the target type
- :param zipped_args_types: a zipped list of args and types
- :param target_dtype: The target data type
- :return: args with possible casts
- """
- casted_args = []
- for argument, data_type in zipped_args_types:
- if data_type.numpy_dtype != target_dtype.numpy_dtype: # ignoring const
- casted_args.append(cast_func(argument, target_dtype))
- else:
- casted_args.append(argument)
- return casted_args
-
- def pointer_arithmetic(expr_args):
- """
- Creates a valid pointer arithmetic function
- :param expr_args: Arguments of the add expression
- :return: pointer_arithmetic_func
- """
- pointer = None
- new_args = []
- for arg, data_type in expr_args:
- if data_type.func is PointerType:
- assert pointer is None
- pointer = arg
- for arg, data_type in expr_args:
- if arg != pointer:
- assert data_type.is_int() or data_type.is_uint()
- new_args.append(arg)
- new_args = sp.Add(*new_args) if len(new_args) > 0 else new_args
- return pointer_arithmetic_func(pointer, new_args)
-
- if isinstance(node, sp.AtomicExpr) or isinstance(node, cast_func):
- return node
- args = []
- for arg in node.args:
- args.append(insert_casts(arg))
- # TODO indexed, LoopOverCoordinate
- if node.func in (sp.Add, sp.Mul, sp.Or, sp.And, sp.Pow, sp.Eq, sp.Ne, sp.Lt, sp.Le, sp.Gt, sp.Ge):
- # TODO optimize pow, don't cast integer on double
- types = [get_type_of_expression(arg) for arg in args]
- assert len(types) > 0
- # Never ever, ever collate to float type for boolean functions!
- target = collate_types(types, forbid_collation_to_float=isinstance(node.func, BooleanFunction))
- zipped = list(zip(args, types))
- if target.func is PointerType:
- assert node.func is sp.Add
- return pointer_arithmetic(zipped)
- else:
- return node.func(*cast(zipped, target))
- elif node.func is ast.SympyAssignment:
- lhs = args[0]
- rhs = args[1]
- target = get_type_of_expression(lhs)
- if target.func is PointerType:
- return node.func(*args) # TODO fix, not complete
- else:
- return node.func(lhs, *cast([(rhs, get_type_of_expression(rhs))], target))
- elif node.func is ast.ResolvedFieldAccess:
- return node
- elif node.func is ast.Block:
- for old_arg, new_arg in zip(node.args, args):
- node.replace(old_arg, new_arg)
- return node
- elif node.func is ast.LoopOverCoordinate:
- for old_arg, new_arg in zip(node.args, args):
- node.replace(old_arg, new_arg)
- return node
- elif node.func is sp.Piecewise:
- expressions = [expr for (expr, _) in args]
- types = [get_type_of_expression(expr) for expr in expressions]
- target = collate_types(types)
- zipped = list(zip(expressions, types))
- casted_expressions = cast(zipped, target)
- args = [
- arg.func(*[expr, arg.cond])
- for (arg, expr) in zip(args, casted_expressions)
- ]
-
- return node.func(*args)
-
-
def remove_conditionals_in_staggered_kernel(function_node: ast.KernelFunction, include_first=True) -> None:
"""Removes conditionals of a kernel that iterates over staggered positions by splitting the loops at last or
first and last element"""
@@ -1109,73 +824,6 @@ def remove_conditionals_in_staggered_kernel(function_node: ast.KernelFunction, i
# --------------------------------------- Helper Functions -------------------------------------------------------------
-
-
-def typing_from_sympy_inspection(eqs, default_type="double", default_int_type='int64'):
- """
- Creates a default symbol name to type mapping.
- If a sympy Boolean is assigned to a symbol it is assumed to be 'bool' otherwise the default type, usually ('double')
-
- Args:
- eqs: list of equations
- default_type: the type for non-boolean symbols
- Returns:
- dictionary, mapping symbol name to type
- """
- result = defaultdict(lambda: default_type)
- if hasattr(default_type, 'numpy_dtype'):
- result['_complex_type'] = (np.zeros((1,), default_type.numpy_dtype) * 1j).dtype
- else:
- result['_complex_type'] = (np.zeros((1,), default_type) * 1j).dtype
- for eq in eqs:
- if isinstance(eq, ast.Conditional):
- result.update(typing_from_sympy_inspection(eq.true_block.args))
- if eq.false_block:
- result.update(typing_from_sympy_inspection(
- eq.false_block.args))
- elif isinstance(eq, ast.Node) and not isinstance(eq, ast.SympyAssignment):
- continue
- else:
- from pystencils.cpu.vectorization import vec_all, vec_any
- if isinstance(eq.rhs, (vec_all, vec_any)):
- result[eq.lhs.name] = "bool"
- # problematic case here is when rhs is a symbol: then it is impossible to decide here without
- # further information what type the left hand side is - default fallback is the dict value then
- if isinstance(eq.rhs, Boolean) and not isinstance(eq.rhs, sp.Symbol):
- result[eq.lhs.name] = "bool"
- try:
- result[eq.lhs.name] = get_type_of_expression(eq.rhs,
- default_float_type=default_type,
- default_int_type=default_int_type,
- symbol_type_dict=result)
- except Exception:
- pass # gracefully fail in case get_type_of_expression cannot determine type
- return result
-
-
-def get_next_parent_of_type(node, parent_type):
- """Returns the next parent node of given type or None, if root is reached.
-
- Traverses the AST nodes parents until a parent of given type was found.
- If no such parent is found, None is returned
- """
- parent = node.parent
- while parent is not None:
- if isinstance(parent, parent_type):
- return parent
- parent = parent.parent
- return None
-
-
-def parents_of_type(node, parent_type, include_current=False):
- """Generator for all parent nodes of given type"""
- parent = node if include_current else node.parent
- while parent is not None:
- if isinstance(parent, parent_type):
- yield parent
- parent = parent.parent
-
-
def get_optimal_loop_ordering(fields):
"""
Determines the optimal loop order for a given set of fields.
@@ -1331,16 +979,3 @@ def loop_blocking(ast_node: ast.KernelFunction, block_size) -> int:
inner_loop.start = block_ctr
inner_loop.stop = stop
return coordinates_taken_into_account
-
-
-def adjust_c_single_precision_type(type_for_symbol):
- """Replaces every occurrence of 'float' with 'single' to enforce the numpy single precision type."""
- def single_factory():
- return "single"
-
- for symbol in type_for_symbol:
- if type_for_symbol[symbol] == "float":
- type_for_symbol[symbol] = single_factory()
- if hasattr(type_for_symbol, "default_factory") and type_for_symbol.default_factory() == "float":
- type_for_symbol.default_factory = single_factory
- return type_for_symbol
diff --git a/pystencils/typing/__init__.py b/pystencils/typing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae4483da44f9ddfb43e365d0f16e6ea2d9dc97c2
--- /dev/null
+++ b/pystencils/typing/__init__.py
@@ -0,0 +1,16 @@
+from pystencils.typing.cast_functions import (CastFunc, BooleanCastFunc, VectorMemoryAccess, ReinterpretCastFunc,
+ PointerArithmeticFunc)
+from pystencils.typing.types import (is_supported_type, numpy_name_to_c, AbstractType, BasicType, VectorType,
+ PointerType, StructType, create_type)
+from pystencils.typing.typed_sympy import (assumptions_from_dtype, TypedSymbol, FieldStrideSymbol, FieldShapeSymbol,
+ FieldPointerSymbol)
+from pystencils.typing.utilities import (typed_symbols, get_base_type, result_type, collate_types,
+ get_type_of_expression, get_next_parent_of_type, parents_of_type)
+
+
+__all__ = ['CastFunc', 'BooleanCastFunc', 'VectorMemoryAccess', 'ReinterpretCastFunc', 'PointerArithmeticFunc',
+ 'is_supported_type', 'numpy_name_to_c', 'AbstractType', 'BasicType',
+ 'VectorType', 'PointerType', 'StructType', 'create_type',
+ 'assumptions_from_dtype', 'TypedSymbol', 'FieldStrideSymbol', 'FieldShapeSymbol', 'FieldPointerSymbol',
+ 'typed_symbols', 'get_base_type', 'result_type', 'collate_types',
+ 'get_type_of_expression', 'get_next_parent_of_type', 'parents_of_type']
diff --git a/pystencils/typing/cast_functions.py b/pystencils/typing/cast_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b83d223cbff2ce08c1fc0516d2ce53dc2ec350a
--- /dev/null
+++ b/pystencils/typing/cast_functions.py
@@ -0,0 +1,131 @@
+import numpy as np
+import sympy as sp
+from sympy.logic.boolalg import Boolean
+
+from pystencils.typing.types import AbstractType, BasicType
+from pystencils.typing.typed_sympy import TypedSymbol
+
+
+class CastFunc(sp.Function):
+ """
+ CastFunc is used in order to introduce static casts. They are especially useful as a way to signal what type
+ a certain node should have, if it is impossible to add a type to a node, e.g. a sp.Number.
+ """
+ is_Atom = True
+
+ def __new__(cls, *args, **kwargs):
+ if len(args) != 2:
+ pass
+ expr, dtype, *other_args = args
+
+ # If we have two consecutive casts, throw the inner one away.
+ # This optimisation is only available for simple casts. Thus the == is intended here!
+ if expr.__class__ == CastFunc:
+ expr = expr.args[0]
+ if not isinstance(dtype, AbstractType):
+ dtype = BasicType(dtype)
+ # to work in conditions of sp.Piecewise cast_func has to be of type Boolean as well
+ # however, a cast_function should only be a boolean if its argument is a boolean, otherwise this leads
+ # to problems when for example comparing cast_func's for equality
+ #
+ # lhs = bitwise_and(a, cast_func(1, 'int'))
+ # rhs = cast_func(0, 'int')
+ # print( sp.Ne(lhs, rhs) ) # would give true if all cast_funcs are booleans
+ # -> thus a separate class boolean_cast_func is introduced
+ if isinstance(expr, Boolean) and (not isinstance(expr, TypedSymbol) or expr.dtype == BasicType('bool')):
+ cls = BooleanCastFunc
+
+ return sp.Function.__new__(cls, expr, dtype, *other_args, **kwargs)
+
+ @property
+ def canonical(self):
+ if hasattr(self.args[0], 'canonical'):
+ return self.args[0].canonical
+ else:
+ raise NotImplementedError()
+
+ @property
+ def is_commutative(self):
+ return self.args[0].is_commutative
+
+ @property
+ def dtype(self):
+ return self.args[1]
+
+ @property
+ def expr(self):
+ return self.args[0]
+
+ @property
+ def is_integer(self):
+ """
+ Uses Numpy type hierarchy to determine :func:`sympy.Expr.is_integer` predicate
+
+ For reference: Numpy type hierarchy https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.scalars.html
+ """
+ if hasattr(self.dtype, 'numpy_dtype'):
+ return np.issubdtype(self.dtype.numpy_dtype, np.integer) or super().is_integer
+ else:
+ return super().is_integer
+
+ @property
+ def is_negative(self):
+ """
+ See :func:`.TypedSymbol.is_integer`
+ """
+ if hasattr(self.dtype, 'numpy_dtype'):
+ if np.issubdtype(self.dtype.numpy_dtype, np.unsignedinteger):
+ return False
+
+ return super().is_negative
+
+ @property
+ def is_nonnegative(self):
+ """
+ See :func:`.TypedSymbol.is_integer`
+ """
+ if self.is_negative is False:
+ return True
+ else:
+ return super().is_nonnegative
+
+ @property
+ def is_real(self):
+ """
+ See :func:`.TypedSymbol.is_integer`
+ """
+ if hasattr(self.dtype, 'numpy_dtype'):
+ return np.issubdtype(self.dtype.numpy_dtype, np.integer) or np.issubdtype(self.dtype.numpy_dtype,
+ np.floating) or super().is_real
+ else:
+ return super().is_real
+
+
+class BooleanCastFunc(CastFunc, Boolean):
+ # TODO: documentation
+ pass
+
+
+class VectorMemoryAccess(CastFunc):
+ """
+ Special memory access for vectorized kernel.
+ Arguments: read/write expression, type, aligned, non-temporal, mask (or none), stride
+ """
+ nargs = (6,)
+
+
+class ReinterpretCastFunc(CastFunc):
+ """
+ Reinterpret cast is necessary for the StructType
+ """
+ pass
+
+
+class PointerArithmeticFunc(sp.Function, Boolean):
+ # TODO: documentation, or deprecate!
+ @property
+ def canonical(self):
+ if hasattr(self.args[0], 'canonical'):
+ return self.args[0].canonical
+ else:
+ raise NotImplementedError()
diff --git a/pystencils/typing/leaf_typing.py b/pystencils/typing/leaf_typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddffd61ced02b3603e7a21a784860d49127e1b5f
--- /dev/null
+++ b/pystencils/typing/leaf_typing.py
@@ -0,0 +1,241 @@
+from collections import namedtuple
+from typing import Union, Tuple, Any, DefaultDict
+import logging
+
+import numpy as np
+
+import sympy as sp
+from sympy import Piecewise
+from sympy.core.relational import Relational
+from sympy.functions.elementary.piecewise import ExprCondPair
+from sympy.functions.elementary.trigonometric import TrigonometricFunction, InverseTrigonometricFunction
+from sympy.functions.elementary.hyperbolic import HyperbolicFunction
+from sympy.codegen import Assignment
+from sympy.logic.boolalg import BooleanFunction
+from sympy.logic.boolalg import BooleanAtom
+
+from pystencils import astnodes as ast
+from pystencils.functions import DivFunc, AddressOf
+from pystencils.cpu.vectorization import vec_all, vec_any
+from pystencils.field import Field
+from pystencils.typing.types import BasicType, PointerType
+from pystencils.typing.utilities import collate_types
+from pystencils.typing.cast_functions import CastFunc, BooleanCastFunc
+from pystencils.typing.typed_sympy import TypedSymbol
+from pystencils.fast_approximation import fast_sqrt, fast_division, fast_inv_sqrt
+from pystencils.utils import ContextVar
+
+
+class TypeAdder:
+ # TODO: specification -> jupyter notebook
+ """Checks if the input to create_kernel is valid.
+
+ Test the following conditions:
+
+ - SSA Form for pure symbols:
+ - Every pure symbol may occur only once as left-hand-side of an assignment
+ - Every pure symbol that is read, may not be written to later
+ - Independence / Parallelization condition:
+ - a field that is written may only be read at exact the same spatial position
+
+ (Pure symbols are symbols that are not Field.Accesses)
+ """
+ FieldAndIndex = namedtuple('FieldAndIndex', ['field', 'index'])
+
+ def __init__(self, type_for_symbol: DefaultDict[str, BasicType], default_number_float: BasicType,
+ default_number_int: BasicType):
+ self.type_for_symbol = type_for_symbol
+ self.default_number_float = ContextVar(default_number_float)
+ self.default_number_int = ContextVar(default_number_int)
+
+ def visit(self, obj):
+ if isinstance(obj, (list, tuple)):
+ return [self.visit(e) for e in obj]
+ if isinstance(obj, (sp.Eq, ast.SympyAssignment, Assignment)):
+ return self.process_assignment(obj)
+ elif isinstance(obj, ast.Conditional):
+ condition, condition_type = self.figure_out_type(obj.condition_expr)
+ assert condition_type == BasicType('bool')
+ true_block = self.visit(obj.true_block)
+ false_block = None if obj.false_block is None else self.visit(
+ obj.false_block)
+ return ast.Conditional(condition, true_block=true_block, false_block=false_block)
+ elif isinstance(obj, ast.Block):
+ return ast.Block([self.visit(e) for e in obj.args])
+ elif isinstance(obj, ast.Node) and not isinstance(obj, ast.LoopOverCoordinate):
+ return obj
+ else:
+ raise ValueError("Invalid object in kernel " + str(type(obj)))
+
+ def process_assignment(self, assignment: Union[sp.Eq, ast.SympyAssignment, Assignment]) -> ast.SympyAssignment:
+ # for checks it is crucial to process rhs before lhs to catch e.g. a = a + 1
+ new_rhs, rhs_type = self.figure_out_type(assignment.rhs)
+
+ lhs = assignment.lhs
+ if not isinstance(lhs, (Field.Access, TypedSymbol)):
+ if isinstance(lhs, sp.Symbol):
+ self.type_for_symbol[lhs.name] = rhs_type
+ else:
+ raise ValueError(f'Lhs: `{lhs}` is not a subtype of sp.Symbol')
+ new_lhs, lhs_type = self.figure_out_type(lhs)
+ assert isinstance(new_lhs, (Field.Access, TypedSymbol))
+
+ if lhs_type != rhs_type:
+ logging.warning(f'Lhs"{new_lhs} of type "{lhs_type}" is assigned with a different datatype '
+ f'rhs: "{new_rhs}" of type "{rhs_type}".')
+ return ast.SympyAssignment(new_lhs, CastFunc(new_rhs, lhs_type))
+ else:
+ return ast.SympyAssignment(new_lhs, new_rhs)
+
+ # Type System Specification
+ # - Defined Types: TypedSymbol, Field, Field.Access, ...?
+ # - Indexed: always unsigned_integer64
+ # - Undefined Types: Symbol
+ # - Is specified in Config in the dict or as 'default_type' or behaves like `auto` in the case of lhs.
+ # - Constants/Numbers: Are either integer or floating. The precision and sign is specified via config
+ # - Example: 1.4 config:float32 -> float32
+ # - Expressions deduce types from arguments
+ # - Functions deduce types from arguments
+ # - default_type and default_float and default_int can be given for a list of assignment, or
+ # individually as a list for assignment
+
+ # Possible Problems - Do we need to support this?
+ # - Mixture in expression with int and float
+ # - Mixture in expression with uint64 and sint64
+ # TODO Logging: Lowest log level should log all casts ----> cast factory, make cast should contain logging
+ def figure_out_type(self, expr) -> Tuple[Any, Union[BasicType, PointerType]]:
+ # Trivial cases
+ from pystencils.field import Field
+ import pystencils.integer_functions
+ from pystencils.bit_masks import flag_cond
+ bool_type = BasicType('bool')
+
+ # TOOO: check the access
+ if isinstance(expr, Field.Access):
+ return expr, expr.dtype
+ elif isinstance(expr, TypedSymbol):
+ return expr, expr.dtype
+ elif isinstance(expr, sp.Symbol):
+ t = TypedSymbol(expr.name, self.type_for_symbol[expr.name])
+ return t, t.dtype
+ elif isinstance(expr, np.generic):
+ assert False, f'Why do we have a np.generic in rhs???? {expr}'
+ elif isinstance(expr, (sp.core.numbers.Infinity, sp.core.numbers.NegativeInfinity)):
+ return expr, BasicType('float32') # see https://en.cppreference.com/w/cpp/numeric/math/INFINITY
+ elif isinstance(expr, sp.Number):
+ if expr.is_Integer:
+ data_type = self.default_number_int.get()
+ elif expr.is_Float or expr.is_Rational:
+ data_type = self.default_number_float.get()
+ else:
+ assert False, f'{sp.Number} is neither Float nor Integer'
+ return CastFunc(expr, data_type), data_type
+ elif isinstance(expr, AddressOf):
+ of = expr.args[0]
+ # TODO Basically this should do address_of already
+ assert isinstance(of, (Field.Access, TypedSymbol, Field))
+ return expr, PointerType(of.dtype)
+ elif isinstance(expr, BooleanAtom):
+ return expr, bool_type
+ elif isinstance(expr, Relational):
+ # TODO Jan: Code duplication with general case
+ args_types = [self.figure_out_type(arg) for arg in expr.args]
+ collated_type = collate_types([t for _, t in args_types])
+ if isinstance(expr, sp.Equality) and collated_type.is_float():
+ logging.warning(f"Using floating point numbers in equality comparison: {expr}")
+ new_args = [a if t.dtype_eq(collated_type) else CastFunc(a, collated_type) for a, t in args_types]
+ new_eq = expr.func(*new_args)
+ return new_eq, bool_type
+ elif isinstance(expr, CastFunc):
+ new_expr, _ = self.figure_out_type(expr.expr)
+ return expr.func(*[new_expr, expr.dtype]), expr.dtype
+ elif isinstance(expr, ast.ConditionalFieldAccess):
+ access, access_type = self.figure_out_type(expr.access)
+ value, value_type = self.figure_out_type(expr.outofbounds_value)
+ condition, condition_type = self.figure_out_type(expr.outofbounds_condition)
+ assert condition_type == bool_type
+ collated_type = collate_types([access_type, value_type])
+ if collated_type == access_type:
+ new_access = access
+ else:
+ logging.warning(f"In {expr} the Field Access had to be casted to {collated_type}. This is "
+ f"probably due to a type missmatch of the Field and the value of "
+ f"ConditionalFieldAccess")
+ new_access = CastFunc(access, collated_type)
+
+ new_value = value if value_type == collated_type else CastFunc(value, collated_type)
+ return expr.func(new_access, condition, new_value), collated_type
+ elif isinstance(expr, (vec_any, vec_all)):
+ return expr, bool_type
+ elif isinstance(expr, BooleanFunction):
+ args_types = [self.figure_out_type(a) for a in expr.args]
+ new_args = [a if t.dtype_eq(bool_type) else BooleanCastFunc(a, bool_type) for a, t in args_types]
+ return expr.func(*new_args), bool_type
+ elif type(expr, ) in pystencils.integer_functions.__dict__.values():
+ args_types = [self.figure_out_type(a) for a in expr.args]
+ collated_type = collate_types([t for _, t in args_types])
+ # TODO: should we downcast to integer? If yes then which integer type?
+ if not collated_type.is_int():
+ raise ValueError(f"Integer functions need to be used with integer types but {collated_type} was given")
+
+ return expr, collated_type
+ elif isinstance(expr, flag_cond):
+ # do not process the arguments to the bit shift - they must remain integers
+ args_types = [self.figure_out_type(a) for a in (expr.args[i] for i in range(2, len(expr.args)))]
+ collated_type = collate_types([t for _, t in args_types])
+ new_expressions = [a if t.dtype_eq(collated_type) else CastFunc(a, collated_type) for a, t in args_types]
+ return expr.func(expr.args[0], expr.args[1], *new_expressions), collated_type
+ # elif isinstance(expr, sp.Mul):
+ # raise NotImplementedError('sp.Mul')
+ # # TODO can we ignore this and move it to general expr handling, i.e. removing Mul? (See todo in backend)
+ # # args_types = [self.figure_out_type(arg) for arg in expr.args if arg not in (-1, 1)]
+ elif isinstance(expr, sp.Indexed):
+ typed_symbol = expr.base.label
+ return expr, typed_symbol.dtype
+ elif isinstance(expr, ExprCondPair):
+ expr_expr, expr_type = self.figure_out_type(expr.expr)
+ condition, condition_type = self.figure_out_type(expr.cond)
+ if condition_type != bool_type:
+ logging.warning(f'Condition "{condition}" is of type "{condition_type}" and not "bool"')
+ return expr.func(expr_expr, condition), expr_type
+ elif isinstance(expr, Piecewise):
+ args_types = [self.figure_out_type(arg) for arg in expr.args]
+ collated_type = collate_types([t for _, t in args_types])
+ new_args = []
+ for a, t in args_types:
+ if t != collated_type:
+ if isinstance(a, ExprCondPair):
+ new_args.append(a.func(CastFunc(a.expr, collated_type), a.cond))
+ else:
+ new_args.append(CastFunc(a, collated_type))
+ else:
+ new_args.append(a)
+ return expr.func(*new_args) if new_args else expr, collated_type
+ elif isinstance(expr, (sp.Pow, sp.exp, InverseTrigonometricFunction, TrigonometricFunction,
+ HyperbolicFunction, sp.log)):
+ args_types = [self.figure_out_type(arg) for arg in expr.args]
+ collated_type = collate_types([t for _, t in args_types])
+ new_args = [a if t.dtype_eq(collated_type) else CastFunc(a, collated_type) for a, t in args_types]
+ new_func = expr.func(*new_args) if new_args else expr
+ if collated_type == BasicType('float64'):
+ return new_func, collated_type
+ else:
+ return CastFunc(new_func, collated_type), collated_type
+ elif isinstance(expr, (fast_sqrt, fast_division, fast_inv_sqrt)):
+ args_types = [self.figure_out_type(arg) for arg in expr.args]
+ collated_type = BasicType('float32')
+ new_args = [a if t.dtype_eq(collated_type) else CastFunc(a, collated_type) for a, t in args_types]
+ new_func = expr.func(*new_args) if new_args else expr
+ return CastFunc(new_func, collated_type), collated_type
+ elif isinstance(expr, (sp.Add, sp.Mul, sp.Abs, sp.Min, sp.Max, DivFunc, sp.UnevaluatedExpr)):
+ args_types = [self.figure_out_type(arg) for arg in expr.args]
+ collated_type = collate_types([t for _, t in args_types])
+ if isinstance(collated_type, PointerType):
+ if isinstance(expr, sp.Add):
+ return expr.func(*[a for a, _ in args_types]), collated_type
+ else:
+ raise NotImplementedError(f'Pointer Arithmetic is implemented only for Add, not {expr}')
+ new_args = [a if t.dtype_eq(collated_type) else CastFunc(a, collated_type) for a, t in args_types]
+ return expr.func(*new_args) if new_args else expr, collated_type
+ else:
+ raise NotImplementedError(f'expr {type(expr)}: {expr} unknown to typing')
diff --git a/pystencils/typing/transformations.py b/pystencils/typing/transformations.py
new file mode 100644
index 0000000000000000000000000000000000000000..74ecf19f19607c120e2aa7642911cb2c01586960
--- /dev/null
+++ b/pystencils/typing/transformations.py
@@ -0,0 +1,25 @@
+from typing import List
+
+from pystencils.config import CreateKernelConfig
+from pystencils.typing.leaf_typing import TypeAdder
+from sympy.codegen import Assignment
+
+
+def add_types(eqs: List[Assignment], config: CreateKernelConfig):
+ """Traverses AST and replaces every :class:`sympy.Symbol` by a :class:`pystencils.typedsymbol.TypedSymbol`.
+
+ Additionally returns sets of all fields which are read/written
+
+ Args:
+ eqs: list of equations
+ config: CreateKernelConfig
+
+ Returns:
+ ``typed_equations`` list of equations where symbols have been replaced by typed symbols
+ """
+
+ check = TypeAdder(type_for_symbol=config.data_type,
+ default_number_float=config.default_number_float,
+ default_number_int=config.default_number_int)
+
+ return check.visit(eqs)
diff --git a/pystencils/kernelparameters.py b/pystencils/typing/typed_sympy.py
similarity index 52%
rename from pystencils/kernelparameters.py
rename to pystencils/typing/typed_sympy.py
index 934c305cc21e3a5bcad2e9f6076230dd69ec1d40..302c2f9987b2db1a907710678ddbb7234668cfc6 100644
--- a/pystencils/kernelparameters.py
+++ b/pystencils/typing/typed_sympy.py
@@ -1,25 +1,101 @@
-"""Special symbols representing kernel parameters related to fields/arrays.
-
-A `KernelFunction` node determines parameters that have to be passed to the function by searching for all undefined
-symbols. Some symbols are not directly defined by the user, but are related to the `Field`s used in the kernel:
-For each field a `FieldPointerSymbol` needs to be passed in, which is the pointer to the memory region where
-the field is stored. This pointer is represented by the `FieldPointerSymbol` class that additionally stores the
-name of the corresponding field. For fields where the size is not known at compile time, additionally shape and stride
-information has to be passed in at runtime. These values are represented by `FieldShapeSymbol`
-and `FieldPointerSymbol`.
-
-The special symbols in this module store only the field name instead of a field reference. Storing a field reference
-directly leads to problems with copying and pickling behaviour due to the circular dependency of `Field` and
-e.g. `FieldShapeSymbol`, since a Field contains `FieldShapeSymbol`s in its shape, and a `FieldShapeSymbol`
-would reference back to the field.
-"""
+from typing import Union
+
+import numpy as np
+import sympy as sp
from sympy.core.cache import cacheit
-from pystencils.data_types import (
- PointerType, TypedSymbol, create_composite_type_from_string, get_base_type)
+from pystencils.typing.types import BasicType, create_type, PointerType
+
+
+def assumptions_from_dtype(dtype: Union[BasicType, np.dtype]):
+ """Derives SymPy assumptions from :class:`BasicType` or a Numpy dtype
+
+ Args:
+ dtype (BasicType, np.dtype): a Numpy data type
+ Returns:
+ A dict of SymPy assumptions
+ """
+ if hasattr(dtype, 'numpy_dtype'):
+ dtype = dtype.numpy_dtype
+
+ assumptions = dict()
+
+ try:
+ if np.issubdtype(dtype, np.integer):
+ assumptions.update({'integer': True})
+
+ if np.issubdtype(dtype, np.unsignedinteger):
+ assumptions.update({'negative': False})
+
+ if np.issubdtype(dtype, np.integer) or \
+ np.issubdtype(dtype, np.floating):
+ assumptions.update({'real': True})
+ except Exception: # TODO this is dirty
+ pass
-SHAPE_DTYPE = create_composite_type_from_string("const int64")
-STRIDE_DTYPE = create_composite_type_from_string("const int64")
+ return assumptions
+
+
+class TypedSymbol(sp.Symbol):
+ def __new__(cls, *args, **kwds):
+ obj = TypedSymbol.__xnew_cached_(cls, *args, **kwds)
+ return obj
+
+ def __new_stage2__(cls, name, dtype, **kwargs): # TODO does not match signature of sp.Symbol???
+ # TODO: also Symbol should be allowed ---> see sympy Variable
+ assumptions = assumptions_from_dtype(dtype)
+ assumptions.update(kwargs)
+ obj = super(TypedSymbol, cls).__xnew__(cls, name, **assumptions)
+ try:
+ obj.numpy_dtype = create_type(dtype)
+ except (TypeError, ValueError):
+ # on error keep the string
+ obj.numpy_dtype = dtype
+ return obj
+
+ __xnew__ = staticmethod(__new_stage2__)
+ __xnew_cached_ = staticmethod(cacheit(__new_stage2__))
+
+ @property
+ def dtype(self):
+ return self.numpy_dtype
+
+ def _hashable_content(self):
+ return super()._hashable_content(), hash(self.numpy_dtype)
+
+ def __getnewargs__(self):
+ return self.name, self.dtype
+
+ def __getnewargs_ex__(self):
+ return (self.name, self.dtype), self.assumptions0
+
+ @property
+ def canonical(self):
+ return self
+
+ @property
+ def reversed(self):
+ return self
+
+ @property
+ def headers(self):
+ headers = []
+ try:
+ if np.issubdtype(self.dtype.numpy_dtype, np.complexfloating):
+ headers.append('"cuda_complex.hpp"')
+ except Exception:
+ pass
+ try:
+ if np.issubdtype(self.dtype.base_type.numpy_dtype, np.complexfloating):
+ headers.append('"cuda_complex.hpp"')
+ except Exception:
+ pass
+
+ return headers
+
+
+SHAPE_DTYPE = BasicType('int64', const=True)
+STRIDE_DTYPE = BasicType('int64', const=True)
class FieldStrideSymbol(TypedSymbol):
@@ -83,6 +159,8 @@ class FieldPointerSymbol(TypedSymbol):
return obj
def __new_stage2__(cls, field_name, field_dtype, const):
+ from pystencils.typing.utilities import get_base_type
+
name = f"_data_{field_name}"
dtype = PointerType(get_base_type(field_dtype), const=const, restrict=True)
obj = super(FieldPointerSymbol, cls).__xnew__(cls, name, dtype)
diff --git a/pystencils/typing/types.py b/pystencils/typing/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..06a2888ac0e1fd3e94710bc12dee96c76bd24733
--- /dev/null
+++ b/pystencils/typing/types.py
@@ -0,0 +1,297 @@
+from abc import abstractmethod
+from typing import Union
+
+import numpy as np
+import sympy as sp
+
+
+def is_supported_type(dtype: np.dtype):
+ scalar = dtype.type
+ c = np.issctype(dtype)
+ subclass = issubclass(scalar, np.floating) or issubclass(scalar, np.integer) or issubclass(scalar, np.bool_)
+ additional_checks = dtype.fields is None and dtype.hasobject is False and dtype.subdtype is None
+ return c and subclass and additional_checks
+
+
+def numpy_name_to_c(name: str) -> str:
+ """
+ Converts a np.dtype.name into a C type
+ Args:
+ name: np.dtype.name string
+ Returns:
+ type as a C string
+ """
+ if name == 'float64':
+ return 'double'
+ elif name == 'float32':
+ return 'float'
+ elif name.startswith('int'):
+ width = int(name[len("int"):])
+ return f"int{width}_t"
+ elif name.startswith('uint'):
+ width = int(name[len("uint"):])
+ return f"uint{width}_t"
+ elif name == 'bool':
+ return 'bool'
+ else:
+ raise NotImplementedError(f"Can't map numpy to C name for {name}")
+
+
+class AbstractType(sp.Atom):
+ # TODO: Is it necessary to ineherit from sp.Atom?
+ def __new__(cls, *args, **kwargs):
+ return sp.Basic.__new__(cls)
+
+ def _sympystr(self, *args, **kwargs):
+ return str(self)
+
+ @property
+ @abstractmethod
+ def base_type(self) -> Union[None, 'BasicType']:
+ """
+ Returns: Returns BasicType of a Vector or Pointer type, None otherwise
+ """
+ pass
+
+ @property
+ @abstractmethod
+ def item_size(self) -> int:
+ """
+ Returns: Number of items.
+ E.g. width * item_size(basic_type) in vector's case, or simple numpy itemsize in Struct's case.
+ """
+ pass
+
+
+class BasicType(AbstractType):
+ """
+ BasicType is defined with a const qualifier and a np.dtype.
+ """
+
+ def __init__(self, dtype: Union[np.dtype, 'BasicType', str], const: bool = False):
+ if isinstance(dtype, BasicType):
+ self.numpy_dtype = dtype.numpy_dtype
+ self.const = dtype.const
+ else:
+ self.numpy_dtype = np.dtype(dtype)
+ self.const = const
+ assert is_supported_type(self.numpy_dtype), f'Type {self.numpy_dtype} is currently not supported!'
+
+ def __getnewargs__(self):
+ return self.numpy_dtype, self.const
+
+ def __getnewargs_ex__(self):
+ return (self.numpy_dtype, self.const), {}
+
+ @property
+ def base_type(self):
+ return None
+
+ @property
+ def item_size(self): # TODO: Do we want self.numpy_type.itemsize????
+ return 1
+
+ def is_float(self):
+ return issubclass(self.numpy_dtype.type, np.floating)
+
+ def is_int(self):
+ return issubclass(self.numpy_dtype.type, np.integer)
+
+ def is_uint(self):
+ return issubclass(self.numpy_dtype.type, np.unsignedinteger)
+
+ def is_sint(self):
+ return issubclass(self.numpy_dtype.type, np.signedinteger)
+
+ def is_bool(self):
+ return issubclass(self.numpy_dtype.type, np.bool_)
+
+ def dtype_eq(self, other):
+ if not isinstance(other, BasicType):
+ return False
+ else:
+ return self.numpy_dtype == other.numpy_dtype
+
+ @property
+ def c_name(self) -> str:
+ return numpy_name_to_c(self.numpy_dtype.name)
+
+ def __str__(self):
+ return f'{self.c_name}{" const" if self.const else ""}'
+
+ def __repr__(self):
+ return str(self)
+
+ def __eq__(self, other):
+ return self.dtype_eq(other) and self.const == other.const
+
+ def __hash__(self):
+ return hash(str(self))
+
+
+class VectorType(AbstractType):
+ """
+ VectorType consists of a BasicType and a width.
+ """
+ instruction_set = None
+
+ def __init__(self, base_type: BasicType, width: int):
+ self._base_type = base_type
+ self.width = width
+
+ @property
+ def base_type(self):
+ return self._base_type
+
+ @property
+ def item_size(self):
+ return self.width * self.base_type.item_size
+
+ def __eq__(self, other):
+ if not isinstance(other, VectorType):
+ return False
+ else:
+ return (self.base_type, self.width) == (other.base_type, other.width)
+
+ def __str__(self):
+ if self.instruction_set is None:
+ return f"{self.base_type}[{self.width}]"
+ else:
+ # TODO VectorizationRevamp: this seems super weird. the instruction_set should know how to print a type out!
+ # TODO VectorizationRevamp: this is error prone. base_type could be cons=True. Use dtype instead
+ if self.base_type == create_type("int64") or self.base_type == create_type("int32"):
+ return self.instruction_set['int']
+ elif self.base_type == create_type("float64"):
+ return self.instruction_set['double']
+ elif self.base_type == create_type("float32"):
+ return self.instruction_set['float']
+ elif self.base_type == create_type("bool"):
+ return self.instruction_set['bool']
+ else:
+ raise NotImplementedError()
+
+ def __hash__(self):
+ return hash((self.base_type, self.width))
+
+ def __getnewargs__(self):
+ return self._base_type, self.width
+
+ def __getnewargs_ex__(self):
+ return (self._base_type, self.width), {}
+
+
+class PointerType(AbstractType):
+ def __init__(self, base_type: BasicType, const: bool = False, restrict: bool = True):
+ self._base_type = base_type
+ self.const = const
+ self.restrict = restrict
+
+ def __getnewargs__(self):
+ return self.base_type, self.const, self.restrict
+
+ def __getnewargs_ex__(self):
+ return (self.base_type, self.const, self.restrict), {}
+
+ @property
+ def alias(self):
+ return not self.restrict
+
+ @property
+ def base_type(self):
+ return self._base_type
+
+ @property
+ def item_size(self):
+ return self.base_type.item_size
+
+ def __eq__(self, other):
+ if not isinstance(other, PointerType):
+ return False
+ else:
+ return (self.base_type, self.const, self.restrict) == (other.base_type, other.const, other.restrict)
+
+ def __str__(self):
+ return f'{str(self.base_type)} * {"RESTRICT " if self.restrict else "" }{"const" if self.const else ""}'
+
+ def __repr__(self):
+ return str(self)
+
+ def __hash__(self):
+ return hash((self._base_type, self.const, self.restrict))
+
+
+class StructType(AbstractType):
+ """
+ A list of types (with C offsets).
+ It is implemented with uint8_t and casts to the correct datatype.
+ """
+ def __init__(self, numpy_type, const=False):
+ self.const = const
+ self._dtype = np.dtype(numpy_type)
+
+ def __getnewargs__(self):
+ return self.numpy_dtype, self.const
+
+ def __getnewargs_ex__(self):
+ return (self.numpy_dtype, self.const), {}
+
+ @property
+ def base_type(self):
+ return None
+
+ @property
+ def numpy_dtype(self):
+ return self._dtype
+
+ @property
+ def item_size(self):
+ return self.numpy_dtype.itemsize
+
+ def get_element_offset(self, element_name):
+ return self.numpy_dtype.fields[element_name][1]
+
+ def get_element_type(self, element_name):
+ np_element_type = self.numpy_dtype.fields[element_name][0]
+ return BasicType(np_element_type, self.const)
+
+ def has_element(self, element_name):
+ return element_name in self.numpy_dtype.fields
+
+ def __eq__(self, other):
+ if not isinstance(other, StructType):
+ return False
+ else:
+ return (self.numpy_dtype, self.const) == (other.numpy_dtype, other.const)
+
+ def __str__(self):
+ # structs are handled byte-wise
+ result = "uint8_t"
+ if self.const:
+ result += " const"
+ return result
+
+ def __repr__(self):
+ return str(self)
+
+ def __hash__(self):
+ return hash((self.numpy_dtype, self.const))
+
+
+def create_type(specification: Union[np.dtype, AbstractType, str]) -> AbstractType:
+ # TODO: Deprecated Use the constructor of BasicType or StructType instead
+ """Creates a subclass of Type according to a string or an object of subclass Type.
+
+ Args:
+ specification: Type object, or a string
+
+ Returns:
+ Type object, or a new Type object parsed from the string
+ """
+ if isinstance(specification, AbstractType):
+ return specification
+ else:
+ numpy_dtype = np.dtype(specification)
+ if numpy_dtype.fields is None:
+ return BasicType(numpy_dtype, const=False)
+ else:
+ return StructType(numpy_dtype, const=False)
diff --git a/pystencils/typing/utilities.py b/pystencils/typing/utilities.py
new file mode 100644
index 0000000000000000000000000000000000000000..da40c510ef91c7ca7fee0e6a0259b3eef50f0ab8
--- /dev/null
+++ b/pystencils/typing/utilities.py
@@ -0,0 +1,239 @@
+from collections import defaultdict
+from functools import partial
+from typing import Tuple, Union, Sequence
+
+import numpy as np
+import sympy as sp
+from sympy.logic.boolalg import Boolean, BooleanFunction
+
+import pystencils
+from pystencils.cache import memorycache_if_hashable
+from pystencils.typing.types import BasicType, VectorType, PointerType, create_type
+from pystencils.typing.cast_functions import CastFunc
+from pystencils.typing.typed_sympy import TypedSymbol
+from pystencils.utils import all_equal
+
+
+def typed_symbols(names, dtype, **kwargs):
+ """
+ Creates TypedSymbols with the same functionality as sympy.symbols
+ Args:
+ names: See sympy.symbols
+ dtype: The data type all symbols will have
+ **kwargs: Key value arguments passed to sympy.symbols
+
+ Returns:
+ TypedSymbols
+ """
+ symbols = sp.symbols(names, **kwargs)
+ if isinstance(symbols, Tuple):
+ return tuple(TypedSymbol(str(s), dtype) for s in symbols)
+ else:
+ return TypedSymbol(str(symbols), dtype)
+
+
+def get_base_type(data_type):
+ """
+ Returns the BasicType of a Pointer or a Vector
+ """
+ while data_type.base_type is not None:
+ data_type = data_type.base_type
+ return data_type
+
+
+def result_type(*args: np.dtype):
+ """Returns the type of the result if the np.dtype arguments would be collated.
+ We can't use numpy functionality, because numpy casts don't behave exactly like C casts"""
+ s = sorted(args, key=lambda x: x.itemsize)
+
+ def kind_to_value(kind: str) -> int:
+ if kind == 'f':
+ return 3
+ elif kind == 'i':
+ return 2
+ elif kind == 'u':
+ return 1
+ elif kind == 'b':
+ return 0
+ else:
+ raise NotImplementedError(f'{kind=} is not a supported kind of a type. See "numpy.dtype.kind" for options')
+ s = sorted(s, key=lambda x: kind_to_value(x.kind))
+ return s[-1]
+
+
+def collate_types(types: Sequence[Union[BasicType, VectorType]]):
+ """
+ Takes a sequence of types and returns their "common type" e.g. (float, double, float) -> double
+ Uses the collation rules from numpy.
+ """
+ # Pointer arithmetic case i.e. pointer + [int, uint] is allowed
+ if any(isinstance(t, PointerType) for t in types):
+ pointer_type = None
+ for t in types:
+ if isinstance(t, PointerType):
+ if pointer_type is not None:
+ raise ValueError(f'Cannot collate the combination of two pointer types "{pointer_type}" and "{t}"')
+ pointer_type = t
+ elif isinstance(t, BasicType):
+ if not (t.is_int() or t.is_uint()):
+ raise ValueError("Invalid pointer arithmetic")
+ else:
+ raise ValueError("Invalid pointer arithmetic")
+ return pointer_type
+
+ # # peel of vector types, if at least one vector type occurred the result will also be the vector type
+ vector_type = [t for t in types if isinstance(t, VectorType)]
+ if not all_equal(t.width for t in vector_type):
+ raise ValueError("Collation failed because of vector types with different width")
+
+ # TODO: check if this is needed
+ # def peel_off_type(dtype, type_to_peel_off):
+ # while type(dtype) is type_to_peel_off:
+ # dtype = dtype.base_type
+ # return dtype
+ # types = [peel_off_type(t, VectorType) for t in types]
+
+ types = [t.base_type if isinstance(t, VectorType) else t for t in types]
+
+ # now we should have a list of basic types - struct types are not yet supported
+ assert all(type(t) is BasicType for t in types)
+
+ result_numpy_type = result_type(*(t.numpy_dtype for t in types))
+ result = BasicType(result_numpy_type)
+ if vector_type:
+ result = VectorType(result, vector_type[0].width)
+ return result
+
+
+# TODO get_type_of_expression should be used after leaf_typing. So no defaults should be necessary
+@memorycache_if_hashable(maxsize=2048)
+def get_type_of_expression(expr,
+ default_float_type='double',
+ default_int_type='int',
+ symbol_type_dict=None):
+ from pystencils.astnodes import ResolvedFieldAccess
+ from pystencils.cpu.vectorization import vec_all, vec_any
+
+ if default_float_type == 'float':
+ default_float_type = 'float32'
+
+ if not symbol_type_dict:
+ symbol_type_dict = defaultdict(lambda: create_type('double'))
+
+ # TODO this line is quite hard to understand, if possible simpl
+ get_type = partial(get_type_of_expression,
+ default_float_type=default_float_type,
+ default_int_type=default_int_type,
+ symbol_type_dict=symbol_type_dict)
+
+ expr = sp.sympify(expr)
+ if isinstance(expr, sp.Integer):
+ return create_type(default_int_type)
+ elif isinstance(expr, sp.Rational) or isinstance(expr, sp.Float):
+ return create_type(default_float_type)
+ elif isinstance(expr, ResolvedFieldAccess):
+ return expr.field.dtype
+ elif isinstance(expr, pystencils.field.Field.Access):
+ return expr.field.dtype
+ elif isinstance(expr, TypedSymbol):
+ return expr.dtype
+ elif isinstance(expr, sp.Symbol):
+ # TODO delete if case
+ if symbol_type_dict:
+ return symbol_type_dict[expr.name]
+ else:
+ raise ValueError("All symbols inside this expression have to be typed! ", str(expr))
+ elif isinstance(expr, CastFunc):
+ return expr.args[1]
+ elif isinstance(expr, (vec_any, vec_all)):
+ return create_type("bool")
+ elif hasattr(expr, 'func') and expr.func == sp.Piecewise:
+ collated_result_type = collate_types(tuple(get_type(a[0]) for a in expr.args))
+ collated_condition_type = collate_types(tuple(get_type(a[1]) for a in expr.args))
+ if type(collated_condition_type) is VectorType and type(collated_result_type) is not VectorType:
+ collated_result_type = VectorType(collated_result_type, width=collated_condition_type.width)
+ return collated_result_type
+ elif isinstance(expr, sp.Indexed):
+ typed_symbol = expr.base.label
+ return typed_symbol.dtype.base_type
+ elif isinstance(expr, (Boolean, BooleanFunction)):
+ # if any arg is of vector type return a vector boolean, else return a normal scalar boolean
+ result = create_type("bool")
+ vec_args = [get_type(a) for a in expr.args if isinstance(get_type(a), VectorType)]
+ if vec_args:
+ result = VectorType(result, width=vec_args[0].width)
+ return result
+ elif isinstance(expr, sp.Pow):
+ base_type = get_type(expr.args[0])
+ if expr.exp.is_integer:
+ return base_type
+ else:
+ return collate_types([create_type(default_float_type), base_type])
+ elif isinstance(expr, (sp.Sum, sp.Product)):
+ return get_type(expr.args[0])
+ elif isinstance(expr, sp.Expr):
+ expr: sp.Expr
+ if expr.args:
+ types = tuple(get_type(a) for a in expr.args)
+ return collate_types(types)
+ else:
+ if expr.is_integer:
+ return create_type(default_int_type)
+ else:
+ return create_type(default_float_type)
+
+ raise NotImplementedError("Could not determine type for", expr, type(expr))
+
+
+# Fix for sympy versions from 1.9
+sympy_version = sp.__version__.split('.')
+if int(sympy_version[0]) * 100 + int(sympy_version[1]) >= 109:
+ # __setstate__ would bypass the contructor, so we remove it
+ sp.Number.__getstate__ = sp.Basic.__getstate__
+ del sp.Basic.__getstate__
+
+ class FunctorWithStoredKwargs:
+ def __init__(self, func, **kwargs):
+ self.func = func
+ self.kwargs = kwargs
+
+ def __call__(self, *args):
+ return self.func(*args, **self.kwargs)
+
+ # __reduce_ex__ would strip kwargs, so we override it
+ def basic_reduce_ex(self, protocol):
+ if hasattr(self, '__getnewargs_ex__'):
+ args, kwargs = self.__getnewargs_ex__()
+ else:
+ args, kwargs = self.__getnewargs__(), {}
+ if hasattr(self, '__getstate__'):
+ state = self.__getstate__()
+ else:
+ state = None
+ return FunctorWithStoredKwargs(type(self), **kwargs), args, state
+
+ sp.Number.__reduce_ex__ = sp.Basic.__reduce_ex__
+ sp.Basic.__reduce_ex__ = basic_reduce_ex
+
+
+def get_next_parent_of_type(node, parent_type):
+ """Returns the next parent node of given type or None, if root is reached.
+
+ Traverses the AST nodes parents until a parent of given type was found.
+ If no such parent is found, None is returned
+ """
+ parent = node.parent
+ while parent is not None:
+ if isinstance(parent, parent_type):
+ return parent
+ parent = parent.parent
+ return None
+
+
+def parents_of_type(node, parent_type, include_current=False):
+ """Generator for all parent nodes of given type"""
+ parent = node if include_current else node.parent
+ while parent is not None:
+ if isinstance(parent, parent_type):
+ yield parent
+ parent = parent.parent
diff --git a/pystencils/utils.py b/pystencils/utils.py
index 3afdbc582ef7dece1933dbaf5b00be149f9cbd30..22d61d0bac6c402e10a7f48a07a55264ec4ddf27 100644
--- a/pystencils/utils.py
+++ b/pystencils/utils.py
@@ -1,5 +1,6 @@
import os
import itertools
+from itertools import groupby
from collections import Counter
from contextlib import contextmanager
from tempfile import NamedTemporaryFile
@@ -23,13 +24,13 @@ class DotDict(dict):
self[key] = value
-def all_equal(iterator):
- iterator = iter(iterator)
- try:
- first = next(iterator)
- except StopIteration:
- return True
- return all(first == rest for rest in iterator)
+def all_equal(iterable):
+ """
+ Returns ``True`` if all the elements are equal to each other.
+ Copied from: more-itertools 8.12.0
+ """
+ g = groupby(iterable)
+ return next(g, True) and not next(g, False)
def recursive_dict_update(d, u):
@@ -220,3 +221,17 @@ class LinearEquationSystem:
break
result -= 1
self.next_zero_row = result
+
+
+class ContextVar:
+ def __init__(self, value):
+ self.stack = [value]
+
+ @contextmanager
+ def __call__(self, new_value):
+ self.stack.append(new_value)
+ yield self
+ self.stack.pop()
+
+ def get(self):
+ return self.stack[-1]
diff --git a/pystencils_tests/test_Min_Max.py b/pystencils_tests/test_Min_Max.py
index c227fbf149bac148ff4a497f77e28a9e33d5aada..7fb48b18d1e75f39bef8f069ba1bc5d7cbac782a 100644
--- a/pystencils_tests/test_Min_Max.py
+++ b/pystencils_tests/test_Min_Max.py
@@ -6,31 +6,37 @@ import pystencils
from pystencils.datahandling import create_data_handling
+@pytest.mark.parametrize('dtype', ["float64", "float32"])
@pytest.mark.parametrize('sympy_function', [sp.Min, sp.Max])
-def test_max(sympy_function):
+def test_max(dtype, sympy_function):
dh = create_data_handling(domain_size=(10, 10), periodicity=True)
- x = dh.add_array('x', values_per_cell=1)
+ x = dh.add_array('x', values_per_cell=1, dtype=dtype)
dh.fill("x", 0.0, ghost_layers=True)
- y = dh.add_array('y', values_per_cell=1)
+ y = dh.add_array('y', values_per_cell=1, dtype=dtype)
dh.fill("y", 1.0, ghost_layers=True)
- z = dh.add_array('z', values_per_cell=1)
+ z = dh.add_array('z', values_per_cell=1, dtype=dtype)
dh.fill("z", 2.0, ghost_layers=True)
+ config = pystencils.CreateKernelConfig(default_number_float=dtype)
+
# test sp.Max with one argument
assignment_1 = pystencils.Assignment(x.center, sympy_function(y.center + 3.3))
- ast_1 = pystencils.create_kernel(assignment_1)
+ ast_1 = pystencils.create_kernel(assignment_1, config=config)
kernel_1 = ast_1.compile()
+ # pystencils.show_code(ast_1)
# test sp.Max with two arguments
assignment_2 = pystencils.Assignment(x.center, sympy_function(0.5, y.center - 1.5))
- ast_2 = pystencils.create_kernel(assignment_2)
+ ast_2 = pystencils.create_kernel(assignment_2, config=config)
kernel_2 = ast_2.compile()
+ # pystencils.show_code(ast_2)
# test sp.Max with many arguments
assignment_3 = pystencils.Assignment(x.center, sympy_function(z.center, 4.5, y.center - 1.5, y.center + z.center))
- ast_3 = pystencils.create_kernel(assignment_3)
+ ast_3 = pystencils.create_kernel(assignment_3, config=config)
kernel_3 = ast_3.compile()
+ # pystencils.show_code(ast_3)
if sympy_function is sp.Max:
results = [4.3, 0.5, 4.5]
@@ -43,3 +49,48 @@ def test_max(sympy_function):
assert numpy.all(dh.gather_array('x') == results[1])
dh.run_kernel(kernel_3)
assert numpy.all(dh.gather_array('x') == results[2])
+
+
+@pytest.mark.parametrize('dtype', ["int64", 'int32'])
+@pytest.mark.parametrize('sympy_function', [sp.Min, sp.Max])
+def test_max_integer(dtype, sympy_function):
+ dh = create_data_handling(domain_size=(10, 10), periodicity=True)
+
+ x = dh.add_array('x', values_per_cell=1, dtype=dtype)
+ dh.fill("x", 0, ghost_layers=True)
+ y = dh.add_array('y', values_per_cell=1, dtype=dtype)
+ dh.fill("y", 1, ghost_layers=True)
+ z = dh.add_array('z', values_per_cell=1, dtype=dtype)
+ dh.fill("z", 2, ghost_layers=True)
+
+ config = pystencils.CreateKernelConfig(default_number_int=dtype)
+
+ # test sp.Max with one argument
+ assignment_1 = pystencils.Assignment(x.center, sympy_function(y.center + 3))
+ ast_1 = pystencils.create_kernel(assignment_1, config=config)
+ kernel_1 = ast_1.compile()
+ # pystencils.show_code(ast_1)
+
+ # test sp.Max with two arguments
+ assignment_2 = pystencils.Assignment(x.center, sympy_function(1, y.center - 1))
+ ast_2 = pystencils.create_kernel(assignment_2, config=config)
+ kernel_2 = ast_2.compile()
+ # pystencils.show_code(ast_2)
+
+ # test sp.Max with many arguments
+ assignment_3 = pystencils.Assignment(x.center, sympy_function(z.center, 4, y.center - 1, y.center + z.center))
+ ast_3 = pystencils.create_kernel(assignment_3, config=config)
+ kernel_3 = ast_3.compile()
+ # pystencils.show_code(ast_3)
+
+ if sympy_function is sp.Max:
+ results = [4, 1, 4]
+ else:
+ results = [4, 0, 0]
+
+ dh.run_kernel(kernel_1)
+ assert numpy.all(dh.gather_array('x') == results[0])
+ dh.run_kernel(kernel_2)
+ assert numpy.all(dh.gather_array('x') == results[1])
+ dh.run_kernel(kernel_3)
+ assert numpy.all(dh.gather_array('x') == results[2])
diff --git a/pystencils_tests/test_abs.py b/pystencils_tests/test_abs.py
index cf71bc04c7f5fb502a3f1e93b72ca8304fcfaadf..277cf4f5c4a39598aafbded82a267e6619c15bee 100644
--- a/pystencils_tests/test_abs.py
+++ b/pystencils_tests/test_abs.py
@@ -1,19 +1,21 @@
+import pytest
+
+import pystencils.config
import sympy
import pystencils as ps
-from pystencils.data_types import cast_func, create_type
+from pystencils.typing import CastFunc, create_type
-def test_abs():
+@pytest.mark.parametrize('target', (ps.Target.CPU, ps.Target.GPU))
+def test_abs(target):
x, y, z = ps.fields('x, y, z: float64[2d]')
default_int_type = create_type('int64')
- assignments = ps.AssignmentCollection({
- x[0, 0]: sympy.Abs(cast_func(y[0, 0], default_int_type))
- })
+ assignments = ps.AssignmentCollection({x[0, 0]: sympy.Abs(CastFunc(y[0, 0], default_int_type))})
- config = ps.CreateKernelConfig(target=ps.Target.GPU)
+ config = pystencils.config.CreateKernelConfig(target=target)
ast = ps.create_kernel(assignments, config=config)
code = ps.get_code_str(ast)
print(code)
diff --git a/pystencils_tests/test_address_of.py b/pystencils_tests/test_address_of.py
index 659f5d92fe86250a45728227e7a4b8a359b1aa82..c0a75e540237aa2e0ff46af37859c19cc069ce59 100644
--- a/pystencils_tests/test_address_of.py
+++ b/pystencils_tests/test_address_of.py
@@ -1,48 +1,50 @@
"""
Test of pystencils.data_types.address_of
"""
-import sympy as sp
+import pytest
import pystencils
-from pystencils.data_types import PointerType, address_of, cast_func, create_type
+from pystencils.typing import PointerType, CastFunc, BasicType
+from pystencils.functions import AddressOf
from pystencils.simp.simplifications import sympy_cse
+import sympy as sp
+
def test_address_of():
- x, y = pystencils.fields('x,y: int64[2d]')
- s = pystencils.TypedSymbol('s', PointerType(create_type('int64')))
+ x, y = pystencils.fields('x, y: int64[2d]')
+ s = pystencils.TypedSymbol('s', PointerType(BasicType('int64')))
- assert address_of(x[0, 0]).canonical() == x[0, 0]
- assert address_of(x[0, 0]).dtype == PointerType(x[0, 0].dtype, restrict=True)
- assert address_of(sp.Symbol("a")).dtype == PointerType('void', restrict=True)
+ assert AddressOf(x[0, 0]).canonical() == x[0, 0]
+ assert AddressOf(x[0, 0]).dtype == PointerType(x[0, 0].dtype, restrict=True)
+ with pytest.raises(ValueError):
+ assert AddressOf(sp.Symbol("a")).dtype
assignments = pystencils.AssignmentCollection({
- s: address_of(x[0, 0]),
- y[0, 0]: cast_func(s, create_type('int64'))
- }, {})
+ s: AddressOf(x[0, 0]),
+ y[0, 0]: CastFunc(s, BasicType('int64'))
+ })
- ast = pystencils.create_kernel(assignments)
- pystencils.show_code(ast)
+ kernel = pystencils.create_kernel(assignments).compile()
+ # pystencils.show_code(kernel.ast)
assignments = pystencils.AssignmentCollection({
- y[0, 0]: cast_func(address_of(x[0, 0]), create_type('int64'))
- }, {})
+ y[0, 0]: CastFunc(AddressOf(x[0, 0]), BasicType('int64'))
+ })
- ast = pystencils.create_kernel(assignments)
- pystencils.show_code(ast)
+ kernel = pystencils.create_kernel(assignments).compile()
+ # pystencils.show_code(kernel.ast)
def test_address_of_with_cse():
- x, y = pystencils.fields('x,y: int64[2d]')
- s = pystencils.TypedSymbol('s', PointerType(create_type('int64')))
+ x, y = pystencils.fields('x, y: int64[2d]')
assignments = pystencils.AssignmentCollection({
- y[0, 0]: cast_func(address_of(x[0, 0]), create_type('int64')) + s,
- x[0, 0]: cast_func(address_of(x[0, 0]), create_type('int64')) + 1
- }, {})
+ x[0, 0]: CastFunc(AddressOf(x[0, 0]), BasicType('int64')) + 1
+ })
- ast = pystencils.create_kernel(assignments)
- pystencils.show_code(ast)
+ kernel = pystencils.create_kernel(assignments).compile()
+ # pystencils.show_code(kernel.ast)
assignments_cse = sympy_cse(assignments)
- ast = pystencils.create_kernel(assignments_cse)
- pystencils.show_code(ast)
+ kernel = pystencils.create_kernel(assignments_cse).compile()
+ # pystencils.show_code(kernel.ast)
diff --git a/pystencils_tests/test_astnodes.py b/pystencils_tests/test_astnodes.py
index 688f63ed95e3d7f40feb428f8ca778e0e38d9288..91c11d8ecb1c76bfe19a0594663dfaa2e5b7ca4e 100644
--- a/pystencils_tests/test_astnodes.py
+++ b/pystencils_tests/test_astnodes.py
@@ -1,5 +1,7 @@
import pytest
import sys
+
+import pystencils.config
import sympy as sp
import pystencils as ps
@@ -84,27 +86,3 @@ def test_loop_over_coordinate():
assert loop.stop == 20
assert loop.step == 2
-
-@pytest.mark.parametrize('default_assignment_simplifications', [False, True])
-@pytest.mark.skipif(python_version == '3.8.2', reason="For this python version a strange bug in mpmath occurs")
-def test_sympy_assignment(default_assignment_simplifications):
- assignment = SympyAssignment(dst[0, 0](0), sp.log(x + 3) / sp.log(2) + sp.log(x ** 2 + 1))
-
- config = ps.CreateKernelConfig(default_assignment_simplifications=default_assignment_simplifications)
- ast = ps.create_kernel([assignment], config=config)
- code = ps.get_code_str(ast)
-
- if default_assignment_simplifications:
- assert 'log1p' in code
- # constant term is directly evaluated
- assert 'log2' not in code
- else:
- # no optimisations will be applied so the optimised version of log will not be in the code
- assert 'log1p' not in code
- assert 'log2' not in code
-
- assignment.replace(assignment.lhs, dst[0, 0](1))
- assignment.replace(assignment.rhs, sp.log(2))
-
- assert assignment.lhs == dst[0, 0](1)
- assert assignment.rhs == sp.log(2)
diff --git a/pystencils_tests/test_bit_masks.py b/pystencils_tests/test_bit_masks.py
index 57371976f416abdf52274852666860c3c92dcdf2..423fc13cc63569d3b6277983ca1e9210a3bbe9c9 100644
--- a/pystencils_tests/test_bit_masks.py
+++ b/pystencils_tests/test_bit_masks.py
@@ -1,11 +1,15 @@
+import pytest
import numpy as np
+
+import pystencils as ps
from pystencils import Field, Assignment, create_kernel
from pystencils.bit_masks import flag_cond
-def test_flag_condition():
+@pytest.mark.parametrize('mask_type', [np.uint8, np.uint16, np.uint32, np.uint64])
+def test_flag_condition(mask_type):
f_arr = np.zeros((2, 2, 2), dtype=np.float64)
- mask_arr = np.zeros((2, 2), dtype=np.uint64)
+ mask_arr = np.zeros((2, 2), dtype=mask_type)
mask_arr[0, 1] = (1 << 3)
mask_arr[1, 0] = (1 << 5)
@@ -16,7 +20,7 @@ def test_flag_condition():
v1 = 42.3
v2 = 39.7
- v3 = 119.87
+ v3 = 119
assignments = [
Assignment(f(0), flag_cond(3, mask(0), v1)),
@@ -25,6 +29,8 @@ def test_flag_condition():
kernel = create_kernel(assignments).compile()
kernel(f=f_arr, mask=mask_arr)
+ code = ps.get_code_str(kernel)
+ assert '119.0' in code
reference = np.zeros((2, 2, 2), dtype=np.float64)
reference[0, 1, 0] = v1
diff --git a/pystencils_tests/test_blocking.py b/pystencils_tests/test_blocking.py
index 3d6436a74e45f82f299bde4bf3a911f8811cb222..5ab66cd4e3a69c23d90d6c1d62005d7ca3d9da1f 100644
--- a/pystencils_tests/test_blocking.py
+++ b/pystencils_tests/test_blocking.py
@@ -77,4 +77,4 @@ def test_jacobi3d_fixed_field_size():
print("Fixed Field Size: Smaller than block sizes")
arr = np.empty([3, 5, 6])
- check_equivalence(jacobi(dst, src), arr)
\ No newline at end of file
+ check_equivalence(jacobi(dst, src), arr)
diff --git a/pystencils_tests/test_blocking_staggered.py b/pystencils_tests/test_blocking_staggered.py
index a79efe7c4445faa9baeb8323383b382a42f2cf33..722c2a35871c27a008123f053b8bd7a446d937a0 100644
--- a/pystencils_tests/test_blocking_staggered.py
+++ b/pystencils_tests/test_blocking_staggered.py
@@ -12,8 +12,10 @@ def test_blocking_staggered():
f[0, 0, 0] - f[0, 0, -1],
]
assignments = [ps.Assignment(stag.staggered_access(d), terms[i]) for i, d in enumerate(stag.staggered_stencil)]
+ reference_kernel = ps.create_staggered_kernel(assignments)
+ print(ps.show_code(reference_kernel))
+ reference_kernel = reference_kernel.compile()
kernel = ps.create_staggered_kernel(assignments, cpu_blocking=(3, 16, 8)).compile()
- reference_kernel = ps.create_staggered_kernel(assignments).compile()
print(ps.show_code(kernel.ast))
f_arr = np.random.rand(80, 33, 19)
diff --git a/pystencils_tests/test_buffer.py b/pystencils_tests/test_buffer.py
index 935ef8edcc7a0d9da2c706cce1f771f834b01640..b8af6f53f0b25075d55a182a2a93231fc03b60f7 100644
--- a/pystencils_tests/test_buffer.py
+++ b/pystencils_tests/test_buffer.py
@@ -2,7 +2,8 @@
import numpy as np
-from pystencils import Assignment, Field, FieldType, create_kernel, make_slice
+import pystencils as ps
+from pystencils import Assignment, Field, FieldType, create_kernel
from pystencils.field import create_numpy_array_with_layout, layout_string_to_tuple
from pystencils.slicing import (
add_ghost_layers, get_ghost_region_slice, get_slice_before_ghost_layer)
@@ -41,6 +42,8 @@ def test_full_scalar_field():
pack_eqs = [Assignment(buffer.center(), src_field.center())]
pack_code = create_kernel(pack_eqs, data_type={'src_field': src_arr.dtype, 'buffer': buffer.dtype})
+ code = ps.get_code_str(pack_code)
+ ps.show_code(pack_code)
pack_kernel = pack_code.compile()
pack_kernel(buffer=buffer_arr, src_field=src_arr)
diff --git a/pystencils_tests/test_buffer_gpu.py b/pystencils_tests/test_buffer_gpu.py
index 2b3f55df59a7b0b1218c41f7cf464e37bb36efbb..39750301a43288ada788f94cc469e055cb55f749 100644
--- a/pystencils_tests/test_buffer_gpu.py
+++ b/pystencils_tests/test_buffer_gpu.py
@@ -3,9 +3,9 @@
import numpy as np
import pytest
-from pystencils import Assignment, Field, FieldType
+import pystencils
+from pystencils import Assignment, Field, FieldType, CreateKernelConfig, create_kernel
from pystencils.field import create_numpy_array_with_layout, layout_string_to_tuple
-from pystencils.gpucuda import create_cuda_kernel, make_python_function
from pystencils.slicing import (
add_ghost_layers, get_ghost_region_slice, get_slice_before_ghost_layer)
from pystencils.stencil import direction_string_to_offset
@@ -57,16 +57,20 @@ def test_full_scalar_field():
pack_eqs = [Assignment(buffer.center(), src_field.center())]
pack_types = {'src_field': gpu_src_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- pack_code = create_cuda_kernel(pack_eqs, type_info=pack_types)
- pack_kernel = make_python_function(pack_code)
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=pack_types)
+ pack_ast = create_kernel(pack_eqs, config=config)
+
+ pack_kernel = pack_ast.compile()
pack_kernel(buffer=gpu_buffer_arr, src_field=gpu_src_arr)
unpack_eqs = [Assignment(dst_field.center(), buffer.center())]
unpack_types = {'dst_field': gpu_dst_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- unpack_code = create_cuda_kernel(unpack_eqs, type_info=unpack_types)
- unpack_kernel = make_python_function(unpack_code)
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=unpack_types)
+ unpack_ast = create_kernel(unpack_eqs, config=config)
+
+ unpack_kernel = unpack_ast.compile()
unpack_kernel(dst_field=gpu_dst_arr, buffer=gpu_buffer_arr)
dst_arr = gpu_dst_arr.get()
@@ -91,17 +95,21 @@ def test_field_slice():
pack_eqs = [Assignment(buffer.center(), src_field.center())]
pack_types = {'src_field': gpu_src_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- pack_code = create_cuda_kernel(pack_eqs, type_info=pack_types)
- pack_kernel = make_python_function(pack_code)
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=pack_types)
+ pack_ast = create_kernel(pack_eqs, config=config)
+
+ pack_kernel = pack_ast.compile()
pack_kernel(buffer=gpu_buffer_arr, src_field=gpu_src_arr[pack_slice])
# Unpack into ghost layer of dst_field in N direction
unpack_eqs = [Assignment(dst_field.center(), buffer.center())]
unpack_types = {'dst_field': gpu_dst_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- unpack_code = create_cuda_kernel(unpack_eqs, type_info=unpack_types)
- unpack_kernel = make_python_function(unpack_code)
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=unpack_types)
+ unpack_ast = create_kernel(unpack_eqs, config=config)
+
+ unpack_kernel = unpack_ast.compile()
unpack_kernel(buffer=gpu_buffer_arr, dst_field=gpu_dst_arr[unpack_slice])
dst_arr = gpu_dst_arr.get()
@@ -127,8 +135,11 @@ def test_all_cell_values():
pack_eqs.append(eq)
pack_types = {'src_field': gpu_src_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- pack_code = create_cuda_kernel(pack_eqs, type_info=pack_types)
- pack_kernel = make_python_function(pack_code)
+
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=pack_types)
+ pack_code = create_kernel(pack_eqs, config=config)
+ pack_kernel = pack_code.compile()
+
pack_kernel(buffer=gpu_buffer_arr, src_field=gpu_src_arr)
unpack_eqs = []
@@ -138,8 +149,10 @@ def test_all_cell_values():
unpack_eqs.append(eq)
unpack_types = {'dst_field': gpu_dst_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- unpack_code = create_cuda_kernel(unpack_eqs, type_info=unpack_types)
- unpack_kernel = make_python_function(unpack_code)
+
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=unpack_types)
+ unpack_ast = create_kernel(unpack_eqs, config=config)
+ unpack_kernel = unpack_ast.compile()
unpack_kernel(buffer=gpu_buffer_arr, dst_field=gpu_dst_arr)
dst_arr = gpu_dst_arr.get()
@@ -167,8 +180,9 @@ def test_subset_cell_values():
pack_eqs.append(eq)
pack_types = {'src_field': gpu_src_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- pack_code = create_cuda_kernel(pack_eqs, type_info=pack_types)
- pack_kernel = make_python_function(pack_code)
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=pack_types)
+ pack_ast = create_kernel(pack_eqs, config=config)
+ pack_kernel = pack_ast.compile()
pack_kernel(buffer=gpu_buffer_arr, src_field=gpu_src_arr)
unpack_eqs = []
@@ -178,8 +192,10 @@ def test_subset_cell_values():
unpack_eqs.append(eq)
unpack_types = {'dst_field': gpu_dst_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- unpack_code = create_cuda_kernel(unpack_eqs, type_info=unpack_types)
- unpack_kernel = make_python_function(unpack_code)
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=unpack_types)
+ unpack_ast = create_kernel(unpack_eqs, config=config)
+ unpack_kernel = unpack_ast.compile()
+
unpack_kernel(buffer=gpu_buffer_arr, dst_field=gpu_dst_arr)
dst_arr = gpu_dst_arr.get()
@@ -206,8 +222,10 @@ def test_field_layouts():
pack_eqs.append(eq)
pack_types = {'src_field': gpu_src_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- pack_code = create_cuda_kernel(pack_eqs, type_info=pack_types)
- pack_kernel = make_python_function(pack_code)
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=pack_types)
+ pack_ast = create_kernel(pack_eqs, config=config)
+ pack_kernel = pack_ast.compile()
+
pack_kernel(buffer=gpu_buffer_arr, src_field=gpu_src_arr)
unpack_eqs = []
@@ -217,6 +235,8 @@ def test_field_layouts():
unpack_eqs.append(eq)
unpack_types = {'dst_field': gpu_dst_arr.dtype, 'buffer': gpu_buffer_arr.dtype}
- unpack_code = create_cuda_kernel(unpack_eqs, type_info=unpack_types)
- unpack_kernel = make_python_function(unpack_code)
+ config = CreateKernelConfig(target=pystencils.Target.GPU, data_type=unpack_types)
+ unpack_ast = create_kernel(unpack_eqs, config=config)
+ unpack_kernel = unpack_ast.compile()
+
unpack_kernel(buffer=gpu_buffer_arr, dst_field=gpu_dst_arr)
diff --git a/pystencils_tests/test_complex_numbers.py b/pystencils_tests/test_complex_numbers.py
deleted file mode 100644
index 9d9f719527deca49e277e86da70bf732384849c7..0000000000000000000000000000000000000000
--- a/pystencils_tests/test_complex_numbers.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright © 2019 Stephan Seitz <stephan.seitz@fau.de>
-#
-# Distributed under terms of the GPLv3 license.
-"""
-
-"""
-
-import itertools
-
-import numpy as np
-import pytest
-import sympy
-from sympy.functions import im, re
-
-import pystencils
-from pystencils import AssignmentCollection
-from pystencils.data_types import TypedSymbol, create_type
-
-X, Y = pystencils.fields('x, y: complex64[2d]')
-A, B = pystencils.fields('a, b: float32[2d]')
-S1, S2, T = sympy.symbols('S1, S2, T')
-
-TEST_ASSIGNMENTS = [
- AssignmentCollection({X[0, 0]: 1j}),
- AssignmentCollection({
- S1: re(Y.center),
- S2: im(Y.center),
- X[0, 0]: 2j * S1 + S2
- }),
- AssignmentCollection({
- A.center: re(Y.center),
- B.center: im(Y.center),
- }),
- AssignmentCollection({
- Y.center: re(Y.center) + X.center + 2j,
- }),
- AssignmentCollection({
- T: 2 + 4j,
- Y.center: X.center / T,
- })
-]
-
-SCALAR_DTYPES = ['float32', 'float64']
-
-
-@pytest.mark.parametrize("assignment, scalar_dtypes",
- itertools.product(TEST_ASSIGNMENTS, (np.float32,)))
-@pytest.mark.parametrize('target', (pystencils.Target.CPU, pystencils.Target.GPU))
-def test_complex_numbers(assignment, scalar_dtypes, target):
- ast = pystencils.create_kernel(assignment,
- target=target,
- data_type=scalar_dtypes)
- code = pystencils.get_code_str(ast)
-
- print(code)
- assert "Not supported" not in code
-
- if target == pystencils.Target.GPU:
- pytest.importorskip('pycuda')
-
- kernel = ast.compile()
- assert kernel is not None
-
-
-X, Y = pystencils.fields('x, y: complex128[2d]')
-A, B = pystencils.fields('a, b: float64[2d]')
-S1, S2 = sympy.symbols('S1, S2')
-T128 = TypedSymbol('ts', create_type('complex128'))
-
-TEST_ASSIGNMENTS = [
- AssignmentCollection({X[0, 0]: 1j}),
- AssignmentCollection({
- S1: re(Y.center),
- S2: im(Y.center),
- X[0, 0]: 2j * S1 + S2
- }),
- AssignmentCollection({
- A.center: re(Y.center),
- B.center: im(Y.center),
- }),
- AssignmentCollection({
- Y.center: re(Y.center) + X.center + 2j,
- }),
- AssignmentCollection({
- T128: 2 + 4j,
- Y.center: X.center / T128,
- })
-]
-
-SCALAR_DTYPES = ['float64']
-
-
-@pytest.mark.parametrize("assignment", TEST_ASSIGNMENTS)
-@pytest.mark.parametrize('target', (pystencils.Target.CPU, pystencils.Target.GPU))
-def test_complex_numbers_64(assignment, target):
- ast = pystencils.create_kernel(assignment,
- target=target,
- data_type='double')
- code = pystencils.get_code_str(ast)
-
- print(code)
- assert "Not supported" not in code
-
- if target == pystencils.Target.GPU:
- pytest.importorskip('pycuda')
-
- kernel = ast.compile()
- assert kernel is not None
-
-
-@pytest.mark.parametrize('dtype', (np.float32, np.float64))
-@pytest.mark.parametrize('target', (pystencils.Target.CPU, pystencils.Target.GPU))
-@pytest.mark.parametrize('with_complex_argument', ('with_complex_argument', False))
-def test_complex_execution(dtype, target, with_complex_argument):
-
- complex_dtype = f'complex{64 if dtype ==np.float32 else 128}'
- x, y = pystencils.fields(f'x, y: {complex_dtype}[2d]')
-
- x_arr = np.zeros((20, 30), complex_dtype)
- y_arr = np.zeros((20, 30), complex_dtype)
-
- if with_complex_argument:
- a = pystencils.TypedSymbol('a', create_type(complex_dtype))
- else:
- a = (2j+1)
-
- assignments = AssignmentCollection({
- y.center: x.center + a
- })
-
- if target == pystencils.Target.GPU:
- pytest.importorskip('pycuda')
- from pycuda.gpuarray import zeros
- x_arr = zeros((20, 30), complex_dtype)
- y_arr = zeros((20, 30), complex_dtype)
-
- kernel = pystencils.create_kernel(assignments, target=target, data_type=dtype).compile()
-
- if with_complex_argument:
- kernel(x=x_arr, y=y_arr, a=2j+1)
- else:
- kernel(x=x_arr, y=y_arr)
-
- if target == pystencils.Target.GPU:
- y_arr = y_arr.get()
- assert np.allclose(y_arr, 2j+1)
-
diff --git a/pystencils_tests/test_conditional_field_access.py b/pystencils_tests/test_conditional_field_access.py
index a4bd53228476ea49f977e08f71acfd1d596231fe..f8026c7dc22acf4e3664f637855aab3c029d0e26 100644
--- a/pystencils_tests/test_conditional_field_access.py
+++ b/pystencils_tests/test_conditional_field_access.py
@@ -35,11 +35,11 @@ def add_fixed_constant_boundary_handling(assignments, with_cse):
for a in assignment.rhs.atoms(Field.Access) if not a.is_absolute_access
})) for assignment in assignments.all_assignments]
- subs = [{a: ConditionalFieldAccess(a, is_out_of_bound(
- sp.Matrix(a.offsets) + x_vector(ndim), common_shape))
- for a in assignment.rhs.atoms(Field.Access) if not a.is_absolute_access
- } for assignment in assignments.all_assignments]
- print(subs)
+ # subs = [{a: ConditionalFieldAccess(a, is_out_of_bound(
+ # sp.Matrix(a.offsets) + x_vector(ndim), common_shape))
+ # for a in assignment.rhs.atoms(Field.Access) if not a.is_absolute_access
+ # } for assignment in assignments.all_assignments]
+ # print(subs)
if with_cse:
safe_assignments = sympy_cse(ps.AssignmentCollection(safe_assignments))
@@ -48,22 +48,20 @@ def add_fixed_constant_boundary_handling(assignments, with_cse):
return ps.AssignmentCollection(safe_assignments)
+@pytest.mark.parametrize('dtype', ('float64', 'float32'))
@pytest.mark.parametrize('with_cse', (False, 'with_cse'))
-def test_boundary_check(with_cse):
+def test_boundary_check(dtype, with_cse):
+ f, g = ps.fields(f"f, g : {dtype}[2D]")
+ stencil = ps.Assignment(g[0, 0], (f[1, 0] + f[-1, 0] + f[0, 1] + f[0, -1]) / 4)
- f, g = ps.fields("f, g : [2D]")
- stencil = ps.Assignment(g[0, 0],
- (f[1, 0] + f[-1, 0] + f[0, 1] + f[0, -1]) / 4)
-
- f_arr = np.random.rand(1000, 1000)
+ f_arr = np.random.rand(10, 10).astype(dtype=dtype)
g_arr = np.zeros_like(f_arr)
- # kernel(f=f_arr, g=g_arr)
assignments = add_fixed_constant_boundary_handling(ps.AssignmentCollection([stencil]), with_cse)
- print(assignments)
- kernel_checked = ps.create_kernel(assignments, ghost_layers=0).compile()
- ps.show_code(kernel_checked)
+ config = ps.CreateKernelConfig(data_type=dtype, default_number_float=dtype, ghost_layers=0)
+ kernel_checked = ps.create_kernel(assignments, config=config).compile()
+ # ps.show_code(kernel_checked)
# No SEGFAULT, please!!
kernel_checked(f=f_arr, g=g_arr)
diff --git a/pystencils_tests/test_conditional_vec.py b/pystencils_tests/test_conditional_vec.py
index 1a962d00f8cb92c5f2bf6619307ce17777190c4b..6cb60006d05f6e5afa5b562173b0e66c9c689920 100644
--- a/pystencils_tests/test_conditional_vec.py
+++ b/pystencils_tests/test_conditional_vec.py
@@ -3,10 +3,11 @@ import sympy as sp
import pytest
import pystencils as ps
-from pystencils.astnodes import Block, Conditional
+from pystencils.astnodes import Block, Conditional, SympyAssignment
from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets, get_vector_instruction_set
from pystencils.enums import Target
from pystencils.cpu.vectorization import vec_all, vec_any
+from pystencils.node_collection import NodeCollection
supported_instruction_sets = get_supported_instruction_sets() if get_supported_instruction_sets() else []
@@ -24,12 +25,12 @@ def test_vec_any(instruction_set, dtype):
data = ps.fields(f"data: {dtype}[2D]", data=data_arr)
c = [
- ps.Assignment(sp.Symbol("t1"), vec_any(data.center() > 0.0)),
- Conditional(vec_any(data.center() > 0.0), Block([
- ps.Assignment(data.center(), 2.0)
- ]))
+ SympyAssignment(sp.Symbol("t1"), vec_any(data.center() > 0.0)),
+ Conditional(vec_any(data.center() > 0.0), Block([SympyAssignment(data.center(), 2.0)]))
]
- ast = ps.create_kernel(c, target=ps.Target.CPU,
+
+ assignmets = NodeCollection(c)
+ ast = ps.create_kernel(assignments=assignmets, target=ps.Target.CPU,
cpu_vectorize_info={'instruction_set': instruction_set})
kernel = ast.compile()
kernel(data=data_arr)
@@ -52,12 +53,9 @@ def test_vec_all(instruction_set, dtype):
data_arr[3:9, 1:3 * width - 1] = 1.0
data = ps.fields(f"data: {dtype}[2D]", data=data_arr)
- c = [
- Conditional(vec_all(data.center() > 0.0), Block([
- ps.Assignment(data.center(), 2.0)
- ]))
- ]
- ast = ps.create_kernel(c, target=Target.CPU,
+ c = [Conditional(vec_all(data.center() > 0.0), Block([SympyAssignment(data.center(), 2.0)]))]
+ assignmets = NodeCollection(c)
+ ast = ps.create_kernel(assignmets, target=Target.CPU,
cpu_vectorize_info={'instruction_set': instruction_set})
kernel = ast.compile()
kernel(data=data_arr)
@@ -88,26 +86,25 @@ def test_boolean_before_loop():
ast = ps.create_kernel(a, cpu_vectorize_info={'instruction_set': supported_instruction_sets[-1]})
kernel = ast.compile()
kernel(f=f_arr, g=g_arr, t2=1.0)
- print(g)
+ # print(g)
np.testing.assert_array_equal(g_arr, 1.0)
kernel(f=f_arr, g=g_arr, t2=-1.0)
np.testing.assert_array_equal(g_arr, 42.0)
@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
-@pytest.mark.parametrize('dtype', ('float', 'double'))
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
def test_vec_maskstore(instruction_set, dtype):
- data_arr = np.zeros((16, 16), dtype=np.float64 if dtype == 'double' else np.float32)
+ data_arr = np.zeros((16, 16), dtype=np.float64 if dtype == 'float64' else np.float32)
data_arr[3:-3, 3:-3] = 1.0
data = ps.fields(f"data: {dtype}[2D]", data=data_arr)
- c = [
- Conditional(data.center() < 1.0, Block([
- ps.Assignment(data.center(), 2.0)
- ]))
- ]
- ast = ps.create_kernel(c, target=Target.CPU,
- cpu_vectorize_info={'instruction_set': instruction_set})
+ c = [Conditional(data.center() < 1.0, Block([SympyAssignment(data.center(), 2.0)]))]
+
+ assignmets = NodeCollection(c)
+ config = ps.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set}, default_number_float=dtype)
+ ast = ps.create_kernel(assignmets, config=config)
+ print(ps.get_code_str(ast))
kernel = ast.compile()
kernel(data=data_arr)
np.testing.assert_equal(data_arr[:3, :], 2.0)
diff --git a/pystencils_tests/test_create_kernel_backwards_compability.py b/pystencils_tests/test_create_kernel_backwards_compability.py
index bb1c9771500fd9c357da008a94f7092b7bf82f0e..53137e9103c076b5693a585faf93c1c517fa616d 100644
--- a/pystencils_tests/test_create_kernel_backwards_compability.py
+++ b/pystencils_tests/test_create_kernel_backwards_compability.py
@@ -5,6 +5,9 @@ import numpy as np
# This test aims to trigger deprication warnings. Thus the warnings should not be displayed in the warning summary.
+import pystencils.config
+
+
def test_create_kernel_backwards_compatibility():
size = (30, 20)
@@ -24,7 +27,7 @@ def test_create_kernel_backwards_compatibility():
ast_string = ps.create_kernel(jacobi, target='cpu').compile()
# noinspection PyTypeChecker
with pytest.warns(DeprecationWarning):
- ast_config = ps.create_kernel(jacobi, config=ps.CreateKernelConfig(target='cpu')).compile()
+ ast_config = ps.create_kernel(jacobi, config=pystencils.config.CreateKernelConfig(target='cpu')).compile()
ast_enum(f=src_field_enum, d=dst_field_enum)
ast_string(f=src_field_string, d=dst_field_string)
ast_config(f=src_field_config, d=dst_field_config)
diff --git a/pystencils_tests/test_create_kernel_config.py b/pystencils_tests/test_create_kernel_config.py
index 86a1c0ca8b2d726e3a5cb1681800842d9c1a0408..e8ad310c778e2ace3e49681acc3aef552b8f22bc 100644
--- a/pystencils_tests/test_create_kernel_config.py
+++ b/pystencils_tests/test_create_kernel_config.py
@@ -1,22 +1,23 @@
import numpy as np
import pystencils as ps
+import pystencils.config
def test_create_kernel_config():
- c = ps.CreateKernelConfig()
+ c = pystencils.config.CreateKernelConfig()
assert c.backend == ps.Backend.C
assert c.target == ps.Target.CPU
- c = ps.CreateKernelConfig(target=ps.Target.GPU)
+ c = pystencils.config.CreateKernelConfig(target=ps.Target.GPU)
assert c.backend == ps.Backend.CUDA
- c = ps.CreateKernelConfig(backend=ps.Backend.CUDA)
+ c = pystencils.config.CreateKernelConfig(backend=ps.Backend.CUDA)
assert c.target == ps.Target.CPU
assert c.backend == ps.Backend.CUDA
def test_kernel_decorator_config():
- config = ps.CreateKernelConfig()
+ config = pystencils.config.CreateKernelConfig()
a, b, c = ps.fields(a=np.ones(100), b=np.ones(100), c=np.ones(100))
@ps.kernel_config(config)
diff --git a/pystencils_tests/test_cuda_known_functions.py b/pystencils_tests/test_cuda_known_functions.py
deleted file mode 100644
index 32b7d9b76de939769a47b117d8529aeb5ff3e20f..0000000000000000000000000000000000000000
--- a/pystencils_tests/test_cuda_known_functions.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import sympy
-
-import pytest
-
-import pystencils
-from pystencils.astnodes import get_dummy_symbol
-from pystencils.backends.cuda_backend import CudaSympyPrinter
-from pystencils.data_types import address_of
-from pystencils.enums import Target
-
-
-def test_cuda_known_functions():
- printer = CudaSympyPrinter()
- print(printer.known_functions)
-
- x, y = pystencils.fields('x,y: float32 [2d]')
-
- assignments = pystencils.AssignmentCollection({
- get_dummy_symbol(): sympy.Function('atomicAdd')(address_of(y.center()), 2),
- y.center(): sympy.Function('rsqrtf')(x[0, 0])
- })
-
- ast = pystencils.create_kernel(assignments, target=Target.GPU)
- pytest.importorskip('pycuda')
- pystencils.show_code(ast)
- kernel = ast.compile()
- assert(kernel is not None)
-
-
-def test_cuda_but_not_c():
- x, y = pystencils.fields('x,y: float32 [2d]')
-
- assignments = pystencils.AssignmentCollection({
- get_dummy_symbol(): sympy.Function('atomicAdd')(address_of(y.center()), 2),
- y.center(): sympy.Function('rsqrtf')(x[0, 0])
- })
-
- ast = pystencils.create_kernel(assignments, target=Target.CPU)
- pystencils.show_code(ast)
-
-
-def test_cuda_unknown():
- x, y = pystencils.fields('x,y: float32 [2d]')
-
- assignments = pystencils.AssignmentCollection({
- get_dummy_symbol(): sympy.Function('wtf')(address_of(y.center()), 2),
- })
-
- ast = pystencils.create_kernel(assignments, target=Target.GPU)
- pystencils.show_code(ast)
diff --git a/pystencils_tests/test_cudagpu.py b/pystencils_tests/test_cudagpu.py
index 520d859bf5cd94195a7622702bfed83432959afd..a65a08ba6d24b30002822e9916b2d3d44639d26a 100644
--- a/pystencils_tests/test_cudagpu.py
+++ b/pystencils_tests/test_cudagpu.py
@@ -4,9 +4,8 @@ import pycuda.gpuarray as gpuarray
import sympy as sp
from scipy.ndimage import convolve
-from pystencils import Assignment, Field, fields
-from pystencils.gpucuda import BlockIndexing, create_cuda_kernel, make_python_function
-from pystencils.gpucuda.indexing import LineIndexing
+from pystencils import Assignment, Field, fields, CreateKernelConfig, create_kernel, Target
+from pystencils.gpucuda import BlockIndexing
from pystencils.simp import sympy_cse_on_assignment_list
from pystencils.slicing import add_ghost_layers, make_slice, remove_ghost_layers
@@ -22,8 +21,9 @@ def test_averaging_kernel():
update_rule = Assignment(dst_field[0, 0],
(src_field[0, 1] + src_field[0, -1] + src_field[1, 0] + src_field[-1, 0]) / 4)
- ast = create_cuda_kernel(sympy_cse_on_assignment_list([update_rule]))
- kernel = make_python_function(ast)
+ config = CreateKernelConfig(target=Target.GPU)
+ ast = create_kernel(sympy_cse_on_assignment_list([update_rule]), config=config)
+ kernel = ast.compile()
gpu_src_arr = gpuarray.to_gpu(src_arr)
gpu_dst_arr = gpuarray.to_gpu(dst_arr)
@@ -43,8 +43,9 @@ def test_variable_sized_fields():
update_rule = Assignment(dst_field[0, 0],
(src_field[0, 1] + src_field[0, -1] + src_field[1, 0] + src_field[-1, 0]) / 4)
- ast = create_cuda_kernel(sympy_cse_on_assignment_list([update_rule]))
- kernel = make_python_function(ast)
+ config = CreateKernelConfig(target=Target.GPU)
+ ast = create_kernel(sympy_cse_on_assignment_list([update_rule]), config=config)
+ kernel = ast.compile()
size = (3, 3)
src_arr = np.random.rand(*size)
@@ -76,8 +77,9 @@ def test_multiple_index_dimensions():
update_rule = Assignment(dst_field[0, 0],
sum([src_field[offset[0], offset[1]](i) for i in range(src_size[-1])]))
- ast = create_cuda_kernel([update_rule])
- kernel = make_python_function(ast)
+ config = CreateKernelConfig(target=Target.GPU)
+ ast = create_kernel([update_rule], config=config)
+ kernel = ast.compile()
gpu_src_arr = gpuarray.to_gpu(src_arr)
gpu_dst_arr = gpuarray.to_gpu(dst_arr)
@@ -102,8 +104,10 @@ def test_ghost_layer():
update_rule = Assignment(dst_field[0, 0], src_field[0, 0])
ghost_layers = [(1, 2), (2, 1)]
- ast = create_cuda_kernel([update_rule], ghost_layers=ghost_layers, indexing_creator=LineIndexing)
- kernel = make_python_function(ast)
+
+ config = CreateKernelConfig(target=Target.GPU, ghost_layers=ghost_layers, gpu_indexing="line")
+ ast = create_kernel(sympy_cse_on_assignment_list([update_rule]), config=config)
+ kernel = ast.compile()
gpu_src_arr = gpuarray.to_gpu(src_arr)
gpu_dst_arr = gpuarray.to_gpu(dst_arr)
@@ -122,9 +126,11 @@ def test_setting_value():
iteration_slice = make_slice[:, :]
f = Field.create_generic("f", 2)
update_rule = [Assignment(f(0), sp.Symbol("value"))]
- ast = create_cuda_kernel(update_rule, iteration_slice=iteration_slice, indexing_creator=LineIndexing)
- kernel = make_python_function(ast)
+ config = CreateKernelConfig(target=Target.GPU, gpu_indexing="line", iteration_slice=iteration_slice)
+ ast = create_kernel(sympy_cse_on_assignment_list(update_rule), config=config)
+ kernel = ast.compile()
+
kernel(f=arr_gpu, value=np.float64(42.0))
np.testing.assert_equal(arr_gpu.get(), np.ones((5, 5)) * 42.0)
diff --git a/pystencils_tests/test_custom_backends.py b/pystencils_tests/test_custom_backends.py
index 696d1be2772a82de387c5da18376458e35000349..3d0088796e6d6ea6683f69124731cd64fad09507 100644
--- a/pystencils_tests/test_custom_backends.py
+++ b/pystencils_tests/test_custom_backends.py
@@ -1,7 +1,6 @@
from subprocess import CalledProcessError
import pytest
-import sympy
import pystencils
import pystencils.cpu.cpujit
@@ -25,10 +24,10 @@ class ScreamingGpuBackend(CudaBackend):
def test_custom_backends_cpu():
- z, x, y = pystencils.fields("z, y, x: [2d]")
+ z, y, x = pystencils.fields("z, y, x: [2d]")
normal_assignments = pystencils.AssignmentCollection([pystencils.Assignment(
- z[0, 0], x[0, 0] * sympy.log(x[0, 0] * y[0, 0]))], [])
+ z[0, 0], x[0, 0] * x[0, 0] * y[0, 0])], [])
ast = pystencils.create_kernel(normal_assignments, target=Target.CPU)
pystencils.show_code(ast, ScreamingBackend())
@@ -44,7 +43,7 @@ def test_custom_backends_gpu():
z, x, y = pystencils.fields("z, y, x: [2d]")
normal_assignments = pystencils.AssignmentCollection([pystencils.Assignment(
- z[0, 0], x[0, 0] * sympy.log(x[0, 0] * y[0, 0]))], [])
+ z[0, 0], x[0, 0] * x[0, 0] * y[0, 0])], [])
ast = pystencils.create_kernel(normal_assignments, target=Target.GPU)
pystencils.show_code(ast, ScreamingGpuBackend())
diff --git a/pystencils_tests/test_datahandling.py b/pystencils_tests/test_datahandling.py
index be695d078384e678d93f2116b5932379035e878a..afd5f70dac6795ee7bea2d32f3437eb7c3c057cc 100644
--- a/pystencils_tests/test_datahandling.py
+++ b/pystencils_tests/test_datahandling.py
@@ -132,7 +132,7 @@ def kernel_execution_jacobi(dh, target):
def jacobi():
dh.fields.tmp.center @= sum(dh.fields.f.neighbors(stencil)) / len(stencil)
- kernel = create_kernel(jacobi, target=target).compile()
+ kernel = create_kernel(jacobi, config=ps.CreateKernelConfig(target=target)).compile()
for b in dh.iterate(ghost_layers=1):
b['f'].fill(42)
dh.run_kernel(kernel)
diff --git a/pystencils_tests/test_dot_printer.ipynb b/pystencils_tests/test_dot_printer.ipynb
index 67c0e14a947167b13ba012cc71fa6d46841f9aba..35ff1cecb5ec1d5e983dfc2141fa429ae0a8fba1 100644
--- a/pystencils_tests/test_dot_printer.ipynb
+++ b/pystencils_tests/test_dot_printer.ipynb
@@ -1,15 +1,5 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pytest\n",
- "pytest.importorskip('graphviz')"
- ]
- },
{
"cell_type": "code",
"execution_count": 1,
@@ -17,7 +7,7 @@
"outputs": [],
"source": [
"from pystencils.session import *\n",
- "from pystencils.astnodes import Block, Conditional"
+ "from pystencils.astnodes import Block, Conditional, SympyAssignment"
]
},
{
@@ -28,10 +18,10 @@
"source": [
"src, dst = ps.fields(\"src, dst: double[2D]\", layout='c')\n",
"\n",
- "true_block = Block([ps.Assignment(dst[0, 0], src[-1, 0])])\n",
- "false_block = Block([ps.Assignment(dst[0, 0], src[1, 0])])\n",
+ "true_block = Block([SympyAssignment(dst[0, 0], src[-1, 0])])\n",
+ "false_block = Block([SympyAssignment(dst[0, 0], src[1, 0])])\n",
"ur = [true_block, Conditional(dst.center() > 0.0, true_block, false_block)]\n",
- " \n",
+ "\n",
"ast = ps.create_kernel(ur)"
]
},
@@ -44,265 +34,167 @@
"outputs": [
{
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- "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
- " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
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- " -->\n",
- "<!-- Title: %3 Pages: 1 -->\n",
- "<svg width=\"684pt\" height=\"290pt\"\n",
- " viewBox=\"0.00 0.00 684.00 289.51\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
- "<g id=\"graph0\" class=\"graph\" transform=\"scale(.4128 .4128) rotate(0) translate(4 697.3797)\">\n",
- "<title>%3</title>\n",
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+ "<div class=\"highlight\"><pre><span></span><span class=\"n\">FUNC_PREFIX</span><span class=\"w\"> </span><span class=\"kt\">void</span><span class=\"w\"> </span><span class=\"n\">kernel</span><span class=\"p\">(</span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_1</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_src_0</span><span class=\"p\">,</span><span class=\"w\"> </span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"k\">const</span><span class=\"w\"> </span><span class=\"n\">_stride_src_1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_src_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_src_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">for</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"kt\">int64_t</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\"><</span><span class=\"w\"> </span><span class=\"n\">_size_dst_1</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">;</span><span class=\"w\"> </span><span class=\"n\">ctr_1</span><span class=\"w\"> </span><span class=\"o\">+=</span><span class=\"w\"> </span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_src_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"k\">if</span><span class=\"w\"> </span><span class=\"p\">(</span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">></span><span class=\"w\"> </span><span class=\"mf\">0.0</span><span class=\"p\">)</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_0m1</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_src_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">-</span><span class=\"w\"> </span><span class=\"n\">_stride_src_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src_0m1</span><span class=\"p\">[</span><span class=\"n\">_stride_src_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"> </span><span class=\"k\">else</span><span class=\"w\"> </span><span class=\"p\">{</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_dst</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_dst_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"kt\">double</span><span class=\"w\"> </span><span class=\"o\">*</span><span class=\"w\"> </span><span class=\"n\">RESTRICT</span><span class=\"w\"> </span><span class=\"n\">_data_src_01</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_src_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"w\"> </span><span class=\"o\">+</span><span class=\"w\"> </span><span class=\"n\">_stride_src_0</span><span class=\"p\">;</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"n\">_data_dst_00</span><span class=\"p\">[</span><span class=\"n\">_stride_dst_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">]</span><span class=\"w\"> </span><span class=\"o\">=</span><span class=\"w\"> </span><span class=\"n\">_data_src_01</span><span class=\"p\">[</span><span class=\"n\">_stride_src_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">];</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"w\"> </span><span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "<span class=\"p\">}</span><span class=\"w\"></span>\n",
+ "</pre></div>\n"
],
"text/plain": [
- "<graphviz.files.Source at 0x7f62452c4110>"
+ "FUNC_PREFIX void kernel(double * RESTRICT _data_dst, double * RESTRICT const _data_src, int64_t const _size_dst_0, int64_t const _size_dst_1, int64_t const _stride_dst_0, int64_t const _stride_dst_1, int64_t const _stride_src_0, int64_t const _stride_src_1)\n",
+ "{\n",
+ " for (int64_t ctr_0 = 1; ctr_0 < _size_dst_0 - 1; ctr_0 += 1)\n",
+ " {\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + _stride_dst_0*ctr_0;\n",
+ " double * RESTRICT _data_src_0m1 = _data_src + _stride_src_0*ctr_0 - _stride_src_0;\n",
+ " for (int64_t ctr_1 = 1; ctr_1 < _size_dst_1 - 1; ctr_1 += 1)\n",
+ " {\n",
+ " _data_dst_00[_stride_dst_1*ctr_1] = _data_src_0m1[_stride_src_1*ctr_1];\n",
+ " {\n",
+ " \n",
+ " }\n",
+ " if (_data_dst_00[_stride_dst_1*ctr_1] > 0.0)\n",
+ " {\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + _stride_dst_0*ctr_0;\n",
+ " double * RESTRICT _data_src_0m1 = _data_src + _stride_src_0*ctr_0 - _stride_src_0;\n",
+ " _data_dst_00[_stride_dst_1*ctr_1] = _data_src_0m1[_stride_src_1*ctr_1];\n",
+ " } else {\n",
+ " double * RESTRICT _data_dst_00 = _data_dst + _stride_dst_0*ctr_0;\n",
+ " double * RESTRICT _data_src_01 = _data_src + _stride_src_0*ctr_0 + _stride_src_0;\n",
+ " _data_dst_00[_stride_dst_1*ctr_1] = _data_src_01[_stride_src_1*ctr_1];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}"
]
},
- "execution_count": 3,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
}
],
"source": [
- "ps.to_dot(ast, graph_style={'size': \"9.5,12.5\"})"
+ "ps.show_code(ast)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -316,7 +208,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.7"
+ "version": "3.9.9"
}
},
"nbformat": 4,
diff --git a/pystencils_tests/test_dot_printer.py b/pystencils_tests/test_dot_printer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9d362c4fc2be1b46e969fe943c8179c532fdb36
--- /dev/null
+++ b/pystencils_tests/test_dot_printer.py
@@ -0,0 +1,13 @@
+import pystencils as ps
+
+from pystencils.astnodes import Block, Conditional, SympyAssignment
+
+
+def test_dot_print():
+ src, dst = ps.fields("src, dst: double[2D]", layout='c')
+
+ true_block = Block([SympyAssignment(dst[0, 0], src[-1, 0])])
+ false_block = Block([SympyAssignment(dst[0, 0], src[1, 0])])
+ ur = [true_block, Conditional(dst.center() > 0.0, true_block, false_block)]
+
+ ast = ps.create_kernel(ur)
diff --git a/pystencils_tests/test_field.py b/pystencils_tests/test_field.py
index 596f9f4da896146a62b16e03c17369bd8ff61000..14c75133608f5dbd4b9baae25c580b64593d64df 100644
--- a/pystencils_tests/test_field.py
+++ b/pystencils_tests/test_field.py
@@ -4,7 +4,7 @@ import sympy as sp
import pystencils as ps
from pystencils import TypedSymbol
-from pystencils.data_types import create_type
+from pystencils.typing import create_type
from pystencils.field import Field, FieldType, layout_string_to_tuple
diff --git a/pystencils_tests/test_field_equality.ipynb b/pystencils_tests/test_field_equality.ipynb
index 8de31e83b5e496cd57e7e1f7d91a1847588108d8..95959038ec0b3322289a2a6016d3ff43676c1288 100644
--- a/pystencils_tests/test_field_equality.ipynb
+++ b/pystencils_tests/test_field_equality.ipynb
@@ -6,8 +6,7 @@
"metadata": {},
"outputs": [],
"source": [
- "from pystencils.session import *\n",
- "from pystencils.data_types import cast_func"
+ "from pystencils.session import *"
]
},
{
@@ -164,13 +163,13 @@
"output_type": "stream",
"text": [
"Field Accesses:\n",
- " - f[0], hash -3276894289571194847, offsets (0,), index (), (('f_C', ('commutative', True)), ((0,), (_size_f_0,), (_stride_f_0,), 3146377891102027609, <FieldType.GENERIC: 0>, 'f', None), 0)\n",
- " - f[0], hash -1516451775709390846, offsets (0,), index (), (('f_C', ('commutative', True)), ((0,), (_size_f_0,), (_stride_f_0,), -1421177580377734245, <FieldType.GENERIC: 0>, 'f', None), 0)\n",
+ " - f[0], hash -8859424145258271267, offsets (0,), index (), ((('f_C', ('commutative', True), ('complex', True), ('extended_real', True), ('finite', True), ('hermitian', True), ('imaginary', False), ('infinite', False), ('real', True)), 2305067722319023373), ((0,), (_size_f_0,), (_stride_f_0,), <FieldType.GENERIC: 0>, 'f', None, double), 0)\n",
+ " - f[0], hash -6454673863007224785, offsets (0,), index (), ((('f_C', ('commutative', True), ('complex', True), ('extended_real', True), ('finite', True), ('hermitian', True), ('imaginary', False), ('infinite', False), ('real', True)), 4093629613697528859), ((0,), (_size_f_0,), (_stride_f_0,), <FieldType.GENERIC: 0>, 'f', None, float), 0)\n",
"\n",
" -> 0,1 f[0] == f[0]: False\n",
"Fields\n",
- " - f, 140548694371968, shape (_size_f_0,), strides (_stride_f_0,), double, FieldType.GENERIC, layout (0,)\n",
- " - f, 140548693963104, shape (_size_f_0,), strides (_stride_f_0,), float, FieldType.GENERIC, layout (0,)\n",
+ " - f, 4881406800, shape (_size_f_0,), strides (_stride_f_0,), double, FieldType.GENERIC, layout (0,)\n",
+ " - f, 4881445024, shape (_size_f_0,), strides (_stride_f_0,), float, FieldType.GENERIC, layout (0,)\n",
"\n",
" - f == f: False, ids equal False, hash equal False\n"
]
@@ -183,7 +182,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -197,9 +196,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.8"
+ "version": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/pystencils_tests/test_floor_ceil_int_optimization.py b/pystencils_tests/test_floor_ceil_int_optimization.py
index 7ec81b05baa07cf1da80ca24060c0c5e57adcc23..ce06f0559144fd3640acc388680f5ec520c3b03e 100644
--- a/pystencils_tests/test_floor_ceil_int_optimization.py
+++ b/pystencils_tests/test_floor_ceil_int_optimization.py
@@ -11,7 +11,7 @@
import sympy as sp
import pystencils
-from pystencils.data_types import create_type
+from pystencils.typing import create_type
def test_floor_ceil_int_optimization():
diff --git a/pystencils_tests/test_fvm.py b/pystencils_tests/test_fvm.py
index f4c0a663ea069bcbbb2d694e82c14259adc97bfb..9c7c1323311eeda5243b72fa42b87bf4734ff5eb 100644
--- a/pystencils_tests/test_fvm.py
+++ b/pystencils_tests/test_fvm.py
@@ -4,6 +4,8 @@ import numpy as np
import pytest
from itertools import product
from pystencils.rng import random_symbol
+from pystencils.astnodes import SympyAssignment
+from pystencils.node_collection import NodeCollection
def advection_diffusion(dim: int):
@@ -315,7 +317,6 @@ def diffusion_reaction(fluctuations: bool):
fluct = sp.sqrt(2 * dens * D) * sp.sqrt(1 / length) * stencil_factor
# add fluctuations
fluct *= 2 * (next(rng_symbol_gen) - 0.5) * sp.sqrt(3)
-
flux.main_assignments[i] = ps.Assignment(flux.main_assignments[i].lhs, flux.main_assignments[i].rhs + fluct)
# Add the folding to the flux, so that the random numbers persist through the ghostlayers.
@@ -323,26 +324,30 @@ def diffusion_reaction(fluctuations: bool):
ps.astnodes.LoopOverCoordinate.get_loop_counter_symbol(i) % L[i] for i in range(len(L))}
flux.subs(fold)
- r_flux = ps.AssignmentCollection([ps.Assignment(j_fields[i].center, 0) for i in range(species)])
+ r_flux = NodeCollection([SympyAssignment(j_fields[i].center, 0) for i in range(species)])
reaction = r_rate_const
for i in range(species):
reaction *= sp.Pow(n_fields[i].center, r_order[i])
- if(fluctuations):
- rng_symbol_gen = random_symbol(r_flux.subexpressions, dim=dh.dim)
+ new_assignments = []
+ if fluctuations:
+ rng_symbol_gen = random_symbol(new_assignments, dim=dh.dim)
reaction_fluctuations = sp.sqrt(sp.Abs(reaction)) * 2 * (next(rng_symbol_gen) - 0.5) * sp.sqrt(3)
reaction_fluctuations *= sp.Min(1, sp.Abs(reaction**2))
else:
reaction_fluctuations = 0.0
for i in range(species):
- r_flux.main_assignments[i] = ps.Assignment(
+ r_flux.all_assignments[i] = SympyAssignment(
r_flux_fields[i].center, (reaction + reaction_fluctuations) * r_coefs[i])
+ [r_flux.all_assignments.insert(0, new) for new in new_assignments]
- continuity_assignments.append(ps.Assignment(n_fields[0].center, n_fields[0].center + r_flux_fields[0].center))
+ continuity_assignments = [SympyAssignment(*assignment.args) for assignment in continuity_assignments]
+ continuity_assignments.append(SympyAssignment(n_fields[0].center, n_fields[0].center + r_flux_fields[0].center))
flux_kernel = ps.create_staggered_kernel(flux).compile()
reaction_kernel = ps.create_kernel(r_flux).compile()
- pde_kernel = ps.create_kernel(continuity_assignments).compile()
+ config = ps.CreateKernelConfig(allow_double_writes=True)
+ pde_kernel = ps.create_kernel(continuity_assignments, config=config).compile()
sync_conc = dh.synchronization_function([n_fields[0].name, n_fields[1].name])
@@ -412,7 +417,7 @@ advection_diffusion_fluctuations.runners = {}
@pytest.mark.parametrize("density", [27.0, 56.5])
@pytest.mark.parametrize("fluctuations", [False, True])
@pytest.mark.longrun
-def test_diffusion_reaction(density, velocity, fluctuations):
+def test_diffusion_reaction(fluctuations, density, velocity):
diffusion_reaction.runner = diffusion_reaction(fluctuations)
diffusion_reaction.runner(density, velocity)
diff --git a/pystencils_tests/test_global_definitions.py b/pystencils_tests/test_global_definitions.py
index c08557018feb49fe7d8d6beeaa9e5ab0e43e99f5..8b6ee1b5bfb030cddfed2d7e0e70f91d8ccdfc04 100644
--- a/pystencils_tests/test_global_definitions.py
+++ b/pystencils_tests/test_global_definitions.py
@@ -2,7 +2,7 @@ import sympy
import pystencils.astnodes
from pystencils.backends.cbackend import CBackend
-from pystencils.data_types import TypedSymbol
+from pystencils.typing import TypedSymbol
class BogusDeclaration(pystencils.astnodes.Node):
@@ -95,7 +95,7 @@ def test_global_definitions_with_global_symbol():
z, x, y = pystencils.fields("z, y, x: [2d]")
normal_assignments = pystencils.AssignmentCollection([pystencils.Assignment(
- z[0, 0], x[0, 0] * sympy.log(x[0, 0] * y[0, 0]))], [])
+ z[0, 0], x[0, 0] * x[0, 0] * y[0, 0])], [])
ast = pystencils.create_kernel(normal_assignments)
print(pystencils.show_code(ast))
@@ -115,7 +115,7 @@ def test_global_definitions_without_global_symbol():
z, x, y = pystencils.fields("z, y, x: [2d]")
normal_assignments = pystencils.AssignmentCollection([pystencils.Assignment(
- z[0, 0], x[0, 0] * sympy.log(x[0, 0] * y[0, 0]))], [])
+ z[0, 0], x[0, 0] * x[0, 0] * y[0, 0])], [])
ast = pystencils.create_kernel(normal_assignments)
print(pystencils.show_code(ast))
diff --git a/pystencils_tests/test_indexed_kernels.py b/pystencils_tests/test_indexed_kernels.py
index fd994c7f9326d0b175a1adf7042e43938e621ad3..fa06a8f166702b53519a398bc544fcdc30f5cc94 100644
--- a/pystencils_tests/test_indexed_kernels.py
+++ b/pystencils_tests/test_indexed_kernels.py
@@ -1,7 +1,6 @@
import numpy as np
-
-from pystencils import Assignment, Field
-from pystencils.cpu import create_indexed_kernel, make_python_function
+import pystencils as ps
+from pystencils import Assignment, Field, CreateKernelConfig, create_kernel, Target
def test_indexed_kernel():
@@ -15,9 +14,12 @@ def test_indexed_kernel():
indexed_field = Field.create_from_numpy_array('index', index_arr)
normal_field = Field.create_from_numpy_array('f', arr)
update_rule = Assignment(normal_field[0, 0], indexed_field('value'))
- ast = create_indexed_kernel([update_rule], [indexed_field])
- kernel = make_python_function(ast)
+
+ config = CreateKernelConfig(index_fields=[indexed_field])
+ ast = create_kernel([update_rule], config=config)
+ kernel = ast.compile()
kernel(f=arr, index=index_arr)
+ code = ps.get_code_str(kernel)
for i in range(index_arr.shape[0]):
np.testing.assert_allclose(arr[index_arr[i]['x'], index_arr[i]['y']], index_arr[i]['value'], atol=1e-13)
@@ -29,9 +31,7 @@ def test_indexed_cuda_kernel():
pycuda = None
if pycuda:
- from pystencils.gpucuda import make_python_function
import pycuda.gpuarray as gpuarray
- from pystencils.gpucuda.kernelcreation import created_indexed_cuda_kernel
arr = np.zeros((3, 4))
dtype = np.dtype([('x', int), ('y', int), ('value', arr.dtype)])
@@ -43,8 +43,10 @@ def test_indexed_cuda_kernel():
indexed_field = Field.create_from_numpy_array('index', index_arr)
normal_field = Field.create_from_numpy_array('f', arr)
update_rule = Assignment(normal_field[0, 0], indexed_field('value'))
- ast = created_indexed_cuda_kernel([update_rule], [indexed_field])
- kernel = make_python_function(ast)
+
+ config = CreateKernelConfig(target=Target.GPU, index_fields=[indexed_field])
+ ast = create_kernel([update_rule], config=config)
+ kernel = ast.compile()
gpu_arr = gpuarray.to_gpu(arr)
gpu_index_arr = gpuarray.to_gpu(index_arr)
diff --git a/pystencils_tests/test_json_backend.py b/pystencils_tests/test_json_backend.py
index a3fb2420c95d46c705a70a220a2e638d3afd7e77..d09c13c7114b3bc8e4f484ae4e281de763909939 100644
--- a/pystencils_tests/test_json_backend.py
+++ b/pystencils_tests/test_json_backend.py
@@ -21,13 +21,15 @@ def test_json_backend():
a = sympy.Symbol('a')
assignments = pystencils.AssignmentCollection({
- z[0, 0]: x[0, 0] * sympy.log(a * x[0, 0] * y[0, 0])
+ z[0, 0]: x[0, 0] * a * x[0, 0] * y[0, 0]
})
ast = pystencils.create_kernel(assignments)
- print(print_json(ast))
- print(print_yaml(ast))
+ pj = print_json(ast)
+ # print(pj)
+ py = print_yaml(ast)
+ # print(py)
temp_dir = tempfile.TemporaryDirectory()
write_json(temp_dir.name + '/test.json', ast)
diff --git a/pystencils_tests/test_kernel_data_type.py b/pystencils_tests/test_kernel_data_type.py
deleted file mode 100644
index 2fbab3ff145689a7153802fae729a7cf1b6a6979..0000000000000000000000000000000000000000
--- a/pystencils_tests/test_kernel_data_type.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from collections import defaultdict
-
-import numpy as np
-import pytest
-from sympy.abc import x, y
-
-from pystencils import Assignment, create_kernel, fields, CreateKernelConfig
-from pystencils.transformations import adjust_c_single_precision_type
-
-
-@pytest.mark.parametrize("data_type", ("float", "double"))
-def test_single_precision(data_type):
- dtype = f"float{64 if data_type == 'double' else 32}"
- s = fields(f"s: {dtype}[1D]")
- assignments = [Assignment(x, y), Assignment(s[0], x)]
- ast = create_kernel(assignments, config=CreateKernelConfig(data_type=data_type))
- assert ast.body.args[0].lhs.dtype.numpy_dtype == np.dtype(dtype)
- assert ast.body.args[0].rhs.dtype.numpy_dtype == np.dtype(dtype)
- assert ast.body.args[1].body.args[0].rhs.dtype.numpy_dtype == np.dtype(dtype)
-
-
-def test_adjustment_dict():
- d = dict({"x": "float", "y": "double"})
- adjust_c_single_precision_type(d)
- assert np.dtype(d["x"]) == np.dtype("float32")
- assert np.dtype(d["y"]) == np.dtype("float64")
-
-
-def test_adjustement_default_dict():
- dd = defaultdict(lambda: "float")
- dd["x"]
- adjust_c_single_precision_type(dd)
- dd["y"]
- assert np.dtype(dd["x"]) == np.dtype("float32")
- assert np.dtype(dd["y"]) == np.dtype("float32")
- assert np.dtype(dd["z"]) == np.dtype("float32")
diff --git a/pystencils_tests/test_logarithm.py b/pystencils_tests/test_logarithm.py
new file mode 100644
index 0000000000000000000000000000000000000000..85d7814a336663f76ecb40ccaf9bcc2e5ef14102
--- /dev/null
+++ b/pystencils_tests/test_logarithm.py
@@ -0,0 +1,26 @@
+import pytest
+import numpy as np
+import sympy as sp
+
+import pystencils as ps
+
+
+@pytest.mark.parametrize('dtype', ["float64", "float32"])
+def test_log(dtype):
+ a = sp.Symbol("a")
+ x = ps.fields(f'x: {dtype}[1d]')
+
+ assignments = ps.AssignmentCollection({x.center(): sp.log(a)})
+
+ ast = ps.create_kernel(assignments)
+ code = ps.get_code_str(ast)
+ kernel = ast.compile()
+
+ # ps.show_code(ast)
+
+ if dtype == "float64":
+ assert "float" not in code
+
+ array = np.zeros((10,), dtype=dtype)
+ kernel(x=array, a=100)
+ assert np.allclose(array, 4.60517019)
diff --git a/pystencils_tests/test_loop_cutting.py b/pystencils_tests/test_loop_cutting.py
index 9c833aca66b2143a984eb6d5b29d514c1b2a2da4..a21acb50aed510852b21e3d634c7c1e6aa66c610 100644
--- a/pystencils_tests/test_loop_cutting.py
+++ b/pystencils_tests/test_loop_cutting.py
@@ -29,6 +29,10 @@ def offsets_in_plane(normal_plane, offset_int, dimension):
return result
+# TODO this fails because the condition of the Conditional is not simplified anymore:
+# TODO: ---> transformation.simplify_conditionals
+# TODO this should be fixed
+@pytest.mark.xfail
def test_staggered_iteration():
dim = 2
f_arr = np.arange(5**dim).reshape([5]*dim).astype(np.float64)
@@ -50,7 +54,9 @@ def test_staggered_iteration():
sum(f[o] for o in offsets_in_plane(d, -1, dim)))
cond = sp.And(*[conditions[i] for i in range(dim) if d != i])
eqs.append(Conditional(cond, eq))
- func = create_kernel(eqs, ghost_layers=[(1, 0), (1, 0), (1, 0)]).compile()
+ # TODO: correct type hint
+ config = ps.CreateKernelConfig(target=ps.Target.CPU, ghost_layers=[(1, 0), (1, 0), (1, 0)])
+ func = ps.create_kernel(eqs, config=config).compile()
# --- Built-in optimized
expressions = []
@@ -93,7 +99,8 @@ def test_staggered_iteration_manual():
cond = sp.And(*[conditions2])
eqs.append(Conditional(cond, eq))
- kernel_ast = create_kernel(eqs, ghost_layers=[(1, 0), (1, 0), (1, 0)])
+ config = ps.CreateKernelConfig(target=ps.Target.CPU, ghost_layers=[(1, 0), (1, 0), (1, 0)])
+ kernel_ast = ps.create_kernel(eqs, config=config)
func = make_python_function(kernel_ast)
func(f=f_arr, s=s_arr_ref)
diff --git a/pystencils_tests/test_match_subs_for_assignment_collection.py b/pystencils_tests/test_match_subs_for_assignment_collection.py
index 9bcc5ad6b5c174bd2b34e28e5b11785b68b8e148..ec305fa52d7c4f1651368f95f9d9c412ad1f5236 100644
--- a/pystencils_tests/test_match_subs_for_assignment_collection.py
+++ b/pystencils_tests/test_match_subs_for_assignment_collection.py
@@ -11,12 +11,12 @@
import sympy as sp
import pystencils
-from pystencils.data_types import create_type
+from pystencils.typing import TypedSymbol, BasicType
def test_wild_typed_symbol():
x = pystencils.fields('x: float32[3d]')
- typed_symbol = pystencils.data_types.TypedSymbol('a', create_type('float64'))
+ typed_symbol = TypedSymbol('a', BasicType('float64'))
assert x.center().match(sp.Wild('w1'))
assert typed_symbol.match(sp.Wild('w1'))
diff --git a/pystencils_tests/test_math_functions.py b/pystencils_tests/test_math_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..5655fbda60012d612ff7686f2d7288784b3a004f
--- /dev/null
+++ b/pystencils_tests/test_math_functions.py
@@ -0,0 +1,68 @@
+import pytest
+import sympy as sp
+import numpy as np
+import pystencils as ps
+
+
+@pytest.mark.parametrize('dtype', ["float64", "float32"])
+@pytest.mark.parametrize('func', [sp.Pow, sp.atan2])
+@pytest.mark.parametrize('target', [ps.Target.CPU, ps.Target.GPU])
+def test_two_arguments(dtype, func, target):
+ if target == ps.Target.GPU:
+ pytest.importorskip("pycuda")
+ dh = ps.create_data_handling(domain_size=(10, 10), periodicity=True, default_target=target)
+
+ x = dh.add_array('x', values_per_cell=1, dtype=dtype)
+ dh.fill("x", 0.0, ghost_layers=True)
+ y = dh.add_array('y', values_per_cell=1, dtype=dtype)
+ dh.fill("y", 1.0, ghost_layers=True)
+ z = dh.add_array('z', values_per_cell=1, dtype=dtype)
+ dh.fill("z", 2.0, ghost_layers=True)
+
+ config = ps.CreateKernelConfig(target=target)
+
+ # test sp.Max with one argument
+ up = ps.Assignment(x.center, func(y.center, z.center))
+ ast = ps.create_kernel(up, config=config)
+ code = ps.get_code_str(ast)
+ if dtype == 'float32':
+ assert func.__name__.lower() in code
+ kernel = ast.compile()
+
+ dh.all_to_gpu()
+ dh.run_kernel(kernel)
+ dh.all_to_cpu()
+
+ np.testing.assert_allclose(dh.gather_array("x")[0, 0], float(func(1.0, 2.0).evalf()),
+ 13 if dtype == 'float64' else 5)
+
+
+@pytest.mark.parametrize('dtype', ["float64", "float32"])
+@pytest.mark.parametrize('func', [sp.sin, sp.cos, sp.sinh, sp.cosh, sp.atan])
+@pytest.mark.parametrize('target', [ps.Target.CPU, ps.Target.GPU])
+def test_single_arguments(dtype, func, target):
+ if target == ps.Target.GPU:
+ pytest.importorskip("pycuda")
+ dh = ps.create_data_handling(domain_size=(10, 10), periodicity=True, default_target=target)
+
+ x = dh.add_array('x', values_per_cell=1, dtype=dtype)
+ dh.fill("x", 0.0, ghost_layers=True)
+ y = dh.add_array('y', values_per_cell=1, dtype=dtype)
+ dh.fill("y", 1.0, ghost_layers=True)
+
+ config = ps.CreateKernelConfig(target=target)
+
+ # test sp.Max with one argument
+ up = ps.Assignment(x.center, func(y.center))
+ ast = ps.create_kernel(up, config=config)
+ code = ps.get_code_str(ast)
+ if dtype == 'float32':
+ assert func.__name__.lower() in code
+ kernel = ast.compile()
+
+ dh.all_to_gpu()
+ dh.run_kernel(kernel)
+ dh.all_to_cpu()
+
+ np.testing.assert_allclose(dh.gather_array("x")[0, 0], float(func(1.0).evalf()),
+ rtol=10**-3 if dtype == 'float32' else 10**-5)
diff --git a/pystencils_tests/test_nodecollection.py b/pystencils_tests/test_nodecollection.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab24e58e7dc4a70985e6a9c631b9085b39a3f00f
--- /dev/null
+++ b/pystencils_tests/test_nodecollection.py
@@ -0,0 +1,13 @@
+import sympy as sp
+
+from pystencils import AssignmentCollection, Assignment
+from pystencils.node_collection import NodeCollection
+from pystencils.astnodes import SympyAssignment
+
+
+def test_node_collection_from_assignment_collection():
+ x = sp.symbols('x')
+ assignment_collection = AssignmentCollection([Assignment(x, 2)])
+ node_collection = NodeCollection.from_assignment_collection(assignment_collection)
+
+ assert node_collection.all_assignments[0] == SympyAssignment(x, 2)
diff --git a/pystencils_tests/test_pickle_support.py b/pystencils_tests/test_pickle_support.py
index 462645198881a54ec5a33ba10c2ccea69e44d702..87268a777be6390533db71ba184dbd9bb7dcbe2d 100644
--- a/pystencils_tests/test_pickle_support.py
+++ b/pystencils_tests/test_pickle_support.py
@@ -1,7 +1,7 @@
from copy import copy, deepcopy
from pystencils.field import Field
-from pystencils.data_types import TypedSymbol
+from pystencils.typing import TypedSymbol
def test_field_access():
diff --git a/pystencils_tests/test_quicktests.py b/pystencils_tests/test_quicktests.py
new file mode 100644
index 0000000000000000000000000000000000000000..d694b30b4c19bd7c80547002ce93b99e66cadf00
--- /dev/null
+++ b/pystencils_tests/test_quicktests.py
@@ -0,0 +1,74 @@
+import numpy as np
+
+import pystencils as ps
+from pystencils.cpu.vectorization import get_supported_instruction_sets
+from pystencils.cpu.vectorization import replace_inner_stride_with_one, vectorize
+
+
+def test_basic_kernel():
+ for domain_shape in [(4, 5), (3, 4, 5)]:
+ dh = ps.create_data_handling(domain_size=domain_shape, periodicity=True)
+ assert all(dh.periodicity)
+
+ f = dh.add_array('f', values_per_cell=1)
+ tmp = dh.add_array('tmp', values_per_cell=1)
+
+ stencil_2d = [(1, 0), (-1, 0), (0, 1), (0, -1)]
+ stencil_3d = [(1, 0, 0), (-1, 0, 0), (0, 1, 0), (0, -1, 0), (0, 0, 1), (0, 0, -1)]
+ stencil = stencil_2d if dh.dim == 2 else stencil_3d
+
+ jacobi = ps.Assignment(tmp.center, sum(f.neighbors(stencil)) / len(stencil))
+ kernel = ps.create_kernel(jacobi).compile()
+
+ for b in dh.iterate(ghost_layers=1):
+ b['f'].fill(42)
+ dh.run_kernel(kernel)
+ for b in dh.iterate(ghost_layers=0):
+ np.testing.assert_equal(b['f'], 42)
+
+ float_seq = [1.0, 2.0, 3.0, 4.0]
+ int_seq = [1, 2, 3]
+ for op in ('min', 'max', 'sum'):
+ assert (dh.reduce_float_sequence(float_seq, op) == float_seq).all()
+ assert (dh.reduce_int_sequence(int_seq, op) == int_seq).all()
+
+
+def test_basic_blocking_staggered():
+ f = ps.fields("f: double[2D]")
+ stag = ps.fields("stag(2): double[2D]", field_type=ps.FieldType.STAGGERED)
+ terms = [
+ f[0, 0] - f[-1, 0],
+ f[0, 0] - f[0, -1],
+ ]
+ assignments = [ps.Assignment(stag.staggered_access(d), terms[i]) for i, d in enumerate(stag.staggered_stencil)]
+ kernel = ps.create_staggered_kernel(assignments, cpu_blocking=(3, 16)).compile()
+ reference_kernel = ps.create_staggered_kernel(assignments).compile()
+
+ f_arr = np.random.rand(80, 33)
+ stag_arr = np.zeros((80, 33, 3))
+ stag_ref = np.zeros((80, 33, 3))
+ kernel(f=f_arr, stag=stag_arr)
+ reference_kernel(f=f_arr, stag=stag_ref)
+ np.testing.assert_almost_equal(stag_arr, stag_ref)
+
+
+def test_basic_vectorization():
+ supported_instruction_sets = get_supported_instruction_sets()
+ if supported_instruction_sets:
+ instruction_set = supported_instruction_sets[-1]
+ else:
+ instruction_set = None
+
+ f, g = ps.fields("f, g : double[2D]")
+ update_rule = [ps.Assignment(g[0, 0], f[0, 0] + f[-1, 0] + f[1, 0] + f[0, 1] + f[0, -1] + 42.0)]
+ ast = ps.create_kernel(update_rule)
+
+ replace_inner_stride_with_one(ast)
+ vectorize(ast, instruction_set=instruction_set)
+ func = ast.compile()
+
+ arr = np.ones((23 + 2, 17 + 2)) * 5.0
+ dst = np.zeros_like(arr)
+
+ func(g=dst, f=arr)
+ np.testing.assert_equal(dst[1:-1, 1:-1], 5 * 5.0 + 42.0)
\ No newline at end of file
diff --git a/pystencils_tests/test_random.py b/pystencils_tests/test_random.py
index d1f509e6518d78df2345aa6a83b43ccb1f69d3a6..535d62ac99664cf1ca3bb2872dde8d5838c91210 100644
--- a/pystencils_tests/test_random.py
+++ b/pystencils_tests/test_random.py
@@ -3,10 +3,12 @@ import numpy as np
import pytest
import pystencils as ps
+from pystencils.astnodes import SympyAssignment
+from pystencils.node_collection import NodeCollection
from pystencils.rng import PhiloxFourFloats, PhiloxTwoDoubles, AESNIFourFloats, AESNITwoDoubles, random_symbol
from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets
from pystencils.cpu.cpujit import get_compiler_config
-from pystencils.data_types import TypedSymbol
+from pystencils.typing import TypedSymbol
from pystencils.enums import Target
RNGs = {('philox', 'float'): PhiloxFourFloats, ('philox', 'double'): PhiloxTwoDoubles,
@@ -22,8 +24,7 @@ if get_compiler_config()['os'] == 'windows':
instruction_sets.remove('avx512')
-@pytest.mark.parametrize('target,rng', (
-(Target.CPU, 'philox'), (Target.CPU, 'aesni'), (Target.GPU, 'philox')))
+@pytest.mark.parametrize('target, rng', ((Target.CPU, 'philox'), (Target.CPU, 'aesni'), (Target.GPU, 'philox')))
@pytest.mark.parametrize('precision', ('float', 'double'))
@pytest.mark.parametrize('dtype', ('float', 'double'))
def test_rng(target, rng, precision, dtype, t=124, offsets=(0, 0), keys=(0, 0), offset_values=None):
@@ -42,7 +43,7 @@ def test_rng(target, rng, precision, dtype, t=124, offsets=(0, 0), keys=(0, 0),
dh.fill(f.name, 42.0)
rng_node = RNGs[(rng, precision)](dh.dim, offsets=offsets, keys=keys)
- assignments = [rng_node] + [ps.Assignment(f(i), s) for i, s in enumerate(rng_node.result_symbols)]
+ assignments = [rng_node] + [SympyAssignment(f(i), s) for i, s in enumerate(rng_node.result_symbols)]
kernel = ps.create_kernel(assignments, target=dh.default_target).compile()
dh.all_to_gpu()
@@ -130,7 +131,7 @@ def test_rng_vectorized(target, rng, precision, dtype, t=130, offsets=(1, 3), ke
ref = dh.add_array("ref", values_per_cell=4 if precision == 'float' else 2)
rng_node = RNGs[(rng, precision)](dh.dim, offsets=offsets)
- assignments = [rng_node] + [ps.Assignment(ref(i), s) for i, s in enumerate(rng_node.result_symbols)]
+ assignments = [rng_node] + [SympyAssignment(ref(i), s) for i, s in enumerate(rng_node.result_symbols)]
kernel = ps.create_kernel(assignments, target=dh.default_target).compile()
kwargs = {'time_step': t}
@@ -139,7 +140,7 @@ def test_rng_vectorized(target, rng, precision, dtype, t=130, offsets=(1, 3), ke
dh.run_kernel(kernel, **kwargs)
rng_node = RNGs[(rng, precision)](dh.dim, offsets=offsets)
- assignments = [rng_node] + [ps.Assignment(f(i), s) for i, s in enumerate(rng_node.result_symbols)]
+ assignments = [rng_node] + [SympyAssignment(f(i), s) for i, s in enumerate(rng_node.result_symbols)]
kernel = ps.create_kernel(assignments, target=dh.default_target, cpu_vectorize_info=cpu_vectorize_info).compile()
dh.run_kernel(kernel, **kwargs)
@@ -153,24 +154,25 @@ def test_rng_vectorized(target, rng, precision, dtype, t=130, offsets=(1, 3), ke
@pytest.mark.parametrize('vectorized', (False, True))
def test_rng_symbol(vectorized):
"""Make sure that the RNG symbol generator generates symbols and that the resulting code compiles"""
+ cpu_vectorize_info = None
if vectorized:
if not instruction_sets:
pytest.skip("cannot detect CPU instruction set")
else:
cpu_vectorize_info = {'assume_inner_stride_one': True, 'assume_aligned': True,
'instruction_set': instruction_sets[-1]}
- else:
- cpu_vectorize_info = None
dh = ps.create_data_handling((8, 8), default_ghost_layers=0, default_target=Target.CPU)
f = dh.add_array("f", values_per_cell=2 * dh.dim, alignment=True)
- ac = ps.AssignmentCollection([ps.Assignment(f(i), 0) for i in range(f.shape[-1])])
- rng_symbol_gen = random_symbol(ac.subexpressions, dim=dh.dim)
+ nc = NodeCollection([SympyAssignment(f(i), 0) for i in range(f.shape[-1])])
+ subexpressions = []
+ rng_symbol_gen = random_symbol(subexpressions, dim=dh.dim)
for i in range(f.shape[-1]):
- ac.main_assignments[i] = ps.Assignment(ac.main_assignments[i].lhs, next(rng_symbol_gen))
- symbols = [a.rhs for a in ac.main_assignments]
+ nc.all_assignments[i] = SympyAssignment(nc.all_assignments[i].lhs, next(rng_symbol_gen))
+ symbols = [a.rhs for a in nc.all_assignments]
+ [nc.all_assignments.insert(0, subexpression) for subexpression in subexpressions]
assert len(symbols) == f.shape[-1] and len(set(symbols)) == f.shape[-1]
- ps.create_kernel(ac, target=dh.default_target, cpu_vectorize_info=cpu_vectorize_info).compile()
+ ps.create_kernel(nc, target=dh.default_target, cpu_vectorize_info=cpu_vectorize_info).compile()
@pytest.mark.parametrize('vectorized', (False, True))
diff --git a/pystencils_tests/test_simplification_strategy.py b/pystencils_tests/test_simplification_strategy.py
index 31fa435449f5738e0d16639bacf806dd419e0d47..40b350af343a85490e945bd6197ce07a99f04ef8 100644
--- a/pystencils_tests/test_simplification_strategy.py
+++ b/pystencils_tests/test_simplification_strategy.py
@@ -71,6 +71,7 @@ def test_split_inner_loop():
ast = ps.create_kernel(ac)
code = ps.get_code_str(ast)
+ ps.show_code(ast)
# we have four inner loops as indicated in split groups (4 elements) plus one outer loop
assert code.count('for') == 5
ast = ps.create_kernel(ac, target=ps.Target.GPU)
diff --git a/pystencils_tests/test_simplifications.py b/pystencils_tests/test_simplifications.py
index 1c9ed3c0cc88e34fed30fe0f115c4e8afa3dc281..ef8ae7ce61a07c992d09807933061c73e61484a1 100644
--- a/pystencils_tests/test_simplifications.py
+++ b/pystencils_tests/test_simplifications.py
@@ -1,7 +1,10 @@
from sys import version_info as vs
import pytest
+
+import pystencils.config
import sympy as sp
import pystencils as ps
+import numpy as np
from pystencils.simp import subexpression_substitution_in_main_assignments
from pystencils.simp import add_subexpressions_for_divisions
@@ -141,29 +144,27 @@ def test_add_subexpressions_for_field_reads():
@pytest.mark.parametrize('target', (ps.Target.CPU, ps.Target.GPU))
-@pytest.mark.parametrize('simplification', (True, False))
+@pytest.mark.parametrize('dtype', ('float32', 'float64'))
@pytest.mark.skipif((vs.major, vs.minor, vs.micro) == (3, 8, 2), reason="does not work on python 3.8.2 for some reason")
-def test_sympy_optimizations(target, simplification):
+def test_sympy_optimizations(target, dtype):
if target == ps.Target.GPU:
pytest.importorskip("pycuda")
- src, dst = ps.fields('src, dst: float32[2d]')
+ src, dst = ps.fields(f'src, dst: {dtype}[2d]')
- # Triggers Sympy's expm1 optimization
- # Sympy's expm1 optimization is tedious to use and the behaviour is highly depended on the sympy version. In
- # some cases the exp expression has to be encapsulated in brackets or multiplied with 1 or 1.0
- # for sympy to work properly ...
assignments = ps.AssignmentCollection({
src[0, 0]: 1.0 * (sp.exp(dst[0, 0]) - 1)
})
- config = ps.CreateKernelConfig(target=target, default_assignment_simplifications=simplification)
+ config = pystencils.config.CreateKernelConfig(target=target, default_number_float=dtype)
ast = ps.create_kernel(assignments, config=config)
+ ps.show_code(ast)
+
code = ps.get_code_str(ast)
- if simplification:
- assert 'expm1(' in code
- else:
- assert 'expm1(' not in code
+ if dtype == 'float32':
+ assert 'expf(' in code
+ elif dtype == 'float64':
+ assert 'exp(' in code
@pytest.mark.parametrize('target', (ps.Target.CPU, ps.Target.GPU))
@@ -174,16 +175,12 @@ def test_evaluate_constant_terms(target, simplification):
pytest.importorskip("pycuda")
src, dst = ps.fields('src, dst: float32[2d]')
- # Triggers Sympy's cos optimization
+ # cos of a number will always be simplified
assignments = ps.AssignmentCollection({
src[0, 0]: -sp.cos(1) + dst[0, 0]
})
- config = ps.CreateKernelConfig(target=target, default_assignment_simplifications=simplification)
+ config = pystencils.config.CreateKernelConfig(target=target, default_assignment_simplifications=simplification)
ast = ps.create_kernel(assignments, config=config)
code = ps.get_code_str(ast)
- if simplification:
- assert 'cos(' not in code
- else:
- assert 'cos(' in code
- print(code)
+ assert 'cos(' not in code
diff --git a/pystencils_tests/test_size_and_layout_checks.py b/pystencils_tests/test_size_and_layout_checks.py
index 27696e19fca91061b804a516a991d5c402e6cc05..08b747f74344c3484315a9d2d5c090ea0940019c 100644
--- a/pystencils_tests/test_size_and_layout_checks.py
+++ b/pystencils_tests/test_size_and_layout_checks.py
@@ -1,5 +1,7 @@
import numpy as np
import pytest
+
+import pystencils
import sympy as sp
from pystencils import Assignment, Field, create_kernel, fields
@@ -104,13 +106,20 @@ def test_loop_independence_checks():
Assignment(g[0, 0], f[1, 0])])
assert 'Field g is written at two different locations' in str(e.value)
- # This is allowed - because only one element of g is accessed
+ # This is not allowed - because this is not SSA (it can be overwritten with allow_double_writes)
+ with pytest.raises(ValueError) as e:
+ create_kernel([Assignment(g[0, 2], f[0, 1]),
+ Assignment(g[0, 2], 2 * g[0, 2])])
+
+ # This is allowed - because allow_double_writes is True now
create_kernel([Assignment(g[0, 2], f[0, 1]),
- Assignment(g[0, 2], 2 * g[0, 2])])
+ Assignment(g[0, 2], 2 * g[0, 2])],
+ config=pystencils.CreateKernelConfig(allow_double_writes=True))
- create_kernel([Assignment(v[0, 2](1), f[0, 1]),
- Assignment(v[0, 1](0), 4),
- Assignment(v[0, 2](1), 2 * v[0, 2](1))])
+ with pytest.raises(ValueError) as e:
+ create_kernel([Assignment(v[0, 2](1), f[0, 1]),
+ Assignment(v[0, 1](0), 4),
+ Assignment(v[0, 2](1), 2 * v[0, 2](1))])
with pytest.raises(ValueError) as e:
create_kernel([Assignment(g[0, 1], 3),
diff --git a/pystencils_tests/test_small_block_benchmark.ipynb b/pystencils_tests/test_small_block_benchmark.ipynb
index 81101c5a0d33e45300300ab24c70c4c464eb5eac..24d815bde0de4c196496aad4857d13be059ff6d7 100644
--- a/pystencils_tests/test_small_block_benchmark.ipynb
+++ b/pystencils_tests/test_small_block_benchmark.ipynb
@@ -2,9 +2,20 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<module 'waLBerla' from '/Users/holzer/walberla/python/waLBerla/__init__.py'>"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"import pytest\n",
"pytest.importorskip('waLBerla')"
@@ -12,7 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -31,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -44,7 +55,7 @@
"[2, 4, 8, 16, 32, 64, 128]"
]
},
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -58,7 +69,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -105,20 +116,27 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Computing size 2\n",
- "Computing size 4\n",
- "Computing size 8\n",
- "Computing size 16\n",
- "Computing size 32\n",
- "Computing size 64\n",
- "Computing size 128\n"
+ "Computing size 2\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "Cannot create parallel data handling because walberla module is not available",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/07/0d7kq8fd0sx24cs53zz90_qc0000gp/T/ipykernel_12649/2009975470.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mname_to_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouter_repeats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'block_size'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/var/folders/07/0d7kq8fd0sx24cs53zz90_qc0000gp/T/ipykernel_12649/3509370390.py\u001b[0m in \u001b[0;36mbenchmark_datahandling\u001b[0;34m(domain_size, parallel)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbenchmark_datahandling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdomain_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparallel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mdh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_data_handling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdomain_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparallel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparallel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0mf_src\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'src'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mf_dst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dst'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/pystencils/pystencils/pystencils/datahandling/__init__.py\u001b[0m in \u001b[0;36mcreate_data_handling\u001b[0;34m(domain_size, periodicity, default_layout, default_target, parallel, default_ghost_layers)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mparallel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwlb\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot create parallel data handling because walberla module is not available\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mperiodicity\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mperiodicity\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: Cannot create parallel data handling because walberla module is not available"
]
}
],
@@ -139,22 +157,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"if 'is_test_run' not in globals():\n",
" import pandas as pd\n",
@@ -174,7 +179,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -188,7 +193,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "version": "3.9.9"
}
},
"nbformat": 4,
diff --git a/pystencils_tests/test_source_code_comment.py b/pystencils_tests/test_source_code_comment.py
index 79c25ae797be49b93409d2d810efb01a9eb02233..b1006a941f5c5922b1168944f17d2c9aceba66ba 100644
--- a/pystencils_tests/test_source_code_comment.py
+++ b/pystencils_tests/test_source_code_comment.py
@@ -9,6 +9,7 @@
"""
import pystencils
import pystencils.astnodes
+import pystencils.config
def test_source_code_comment():
@@ -19,7 +20,7 @@ def test_source_code_comment():
{a.center(): b[0, 2] + b[0, 0]}, {}
)
- config = pystencils.CreateKernelConfig(target=pystencils.Target.CPU)
+ config = pystencils.config.CreateKernelConfig(target=pystencils.Target.CPU)
ast = pystencils.create_kernel(assignments, config=config)
ast.body.append(pystencils.astnodes.SourceCodeComment("Hallo"))
diff --git a/pystencils_tests/test_subexpression_insertion.py b/pystencils_tests/test_subexpression_insertion.py
index 9ae64d9fe0693016fde25c5aed0fc93942f1d763..790d97d7601f139640455cfd5164689aa0cfd1c1 100644
--- a/pystencils_tests/test_subexpression_insertion.py
+++ b/pystencils_tests/test_subexpression_insertion.py
@@ -1,4 +1,3 @@
-import sympy as sp
from pystencils import fields, Assignment, AssignmentCollection
from pystencils.simp.subexpression_insertion import *
diff --git a/pystencils_tests/test_sum_prod.py b/pystencils_tests/test_sum_prod.py
index 2f6bf7359ad9d74c85b5d679dab772b5d7c00803..af19d5c02d9c8d2d31c94ed369ba3c4a564dbef3 100644
--- a/pystencils_tests/test_sum_prod.py
+++ b/pystencils_tests/test_sum_prod.py
@@ -9,125 +9,97 @@
"""
import pytest
import numpy as np
+
+import pystencils.config
import sympy as sp
import sympy.abc
import pystencils as ps
-from pystencils.data_types import create_type
+from pystencils.typing import create_type
-@pytest.mark.parametrize('default_assignment_simplifications', [False, True])
-def test_sum(default_assignment_simplifications):
+@pytest.mark.parametrize('dtype', ["float64", "float32"])
+def test_sum(dtype):
sum = sp.Sum(sp.abc.k, (sp.abc.k, 1, 100))
expanded_sum = sum.doit()
- print(sum)
- print(expanded_sum)
-
- x = ps.fields('x: float32[1d]')
-
- assignments = ps.AssignmentCollection({x.center(): sum})
-
- config = ps.CreateKernelConfig(default_assignment_simplifications=default_assignment_simplifications)
- ast = ps.create_kernel(assignments, config=config)
- code = ps.get_code_str(ast)
- kernel = ast.compile()
-
- print(code)
- if default_assignment_simplifications is False:
- assert 'double sum' in code
-
- array = np.zeros((10,), np.float32)
-
- kernel(x=array)
-
- assert np.allclose(array, int(expanded_sum) * np.ones_like(array))
-
-
-@pytest.mark.parametrize('default_assignment_simplifications', [False, True])
-def test_sum_use_float(default_assignment_simplifications):
-
- sum = sympy.Sum(sp.abc.k, (sp.abc.k, 1, 100))
- expanded_sum = sum.doit()
-
- print(sum)
- print(expanded_sum)
+ # print(sum)
+ # print(expanded_sum)
- x = ps.fields('x: float32[1d]')
+ x = ps.fields(f'x: {dtype}[1d]')
assignments = ps.AssignmentCollection({x.center(): sum})
- config = ps.CreateKernelConfig(default_assignment_simplifications=default_assignment_simplifications,
- data_type=create_type('float32'))
- ast = ps.create_kernel(assignments, config=config)
+ ast = ps.create_kernel(assignments)
code = ps.get_code_str(ast)
kernel = ast.compile()
- print(code)
- if default_assignment_simplifications is False:
- assert 'float sum' in code
+ # ps.show_code(ast)
- array = np.zeros((10,), np.float32)
+ if dtype == "float32":
+ assert "5050.0f;" in code
+ array = np.zeros((10,), dtype=dtype)
kernel(x=array)
-
assert np.allclose(array, int(expanded_sum) * np.ones_like(array))
-@pytest.mark.parametrize('default_assignment_simplifications', [False, True])
-def test_product(default_assignment_simplifications):
+@pytest.mark.parametrize('dtype', ["int32", "int64", "float64", "float32"])
+def test_product(dtype):
- k = ps.TypedSymbol('k', create_type('int64'))
+ k = ps.TypedSymbol('k', create_type(dtype))
sum = sympy.Product(k, (k, 1, 10))
expanded_sum = sum.doit()
- print(sum)
- print(expanded_sum)
+ # print(sum)
+ # print(expanded_sum)
- x = ps.fields('x: int64[1d]')
+ x = ps.fields(f'x: {dtype}[1d]')
assignments = ps.AssignmentCollection({x.center(): sum})
- config = ps.CreateKernelConfig(default_assignment_simplifications=default_assignment_simplifications)
+ config = pystencils.config.CreateKernelConfig()
ast = ps.create_kernel(assignments, config=config)
code = ps.get_code_str(ast)
kernel = ast.compile()
- print(code)
- if default_assignment_simplifications is False:
- assert 'int64_t product' in code
-
- array = np.zeros((10,), np.int64)
+ # print(code)
+ if dtype == "int64" or dtype == "int32":
+ assert '3628800;' in code
+ elif dtype == "float32":
+ assert '3628800.0f;' in code
+ else:
+ assert '3628800.0;' in code
+ array = np.zeros((10,), dtype=dtype)
kernel(x=array)
-
assert np.allclose(array, int(expanded_sum) * np.ones_like(array))
-
-def test_prod_var_limit():
-
- k = ps.TypedSymbol('k', create_type('int64'))
- limit = ps.TypedSymbol('limit', create_type('int64'))
-
- sum = sympy.Sum(k, (k, 1, limit))
- expanded_sum = sum.replace(limit, 100).doit()
-
- print(sum)
- print(expanded_sum)
-
- x = ps.fields('x: int64[1d]')
-
- assignments = ps.AssignmentCollection({x.center(): sum})
-
- ast = ps.create_kernel(assignments)
- ps.show_code(ast)
- kernel = ast.compile()
-
- array = np.zeros((10,), np.int64)
-
- kernel(x=array, limit=100)
-
- assert np.allclose(array, int(expanded_sum) * np.ones_like(array))
+# TODO: See Issue !55
+# def test_prod_var_limit():
+#
+# k = ps.TypedSymbol('k', create_type('int64'))
+# limit = ps.TypedSymbol('limit', create_type('int64'))
+#
+# sum = sympy.Sum(k, (k, 1, limit))
+# expanded_sum = sum.replace(limit, 100).doit()
+#
+# print(sum)
+# print(expanded_sum)
+#
+# x = ps.fields('x: int64[1d]')
+#
+# assignments = ps.AssignmentCollection({x.center(): sum})
+#
+# ast = ps.create_kernel(assignments)
+# ps.show_code(ast)
+# kernel = ast.compile()
+#
+# array = np.zeros((10,), np.int64)
+#
+# kernel(x=array, limit=100)
+#
+# assert np.allclose(array, int(expanded_sum) * np.ones_like(array))
diff --git a/pystencils_tests/test_transformations.py b/pystencils_tests/test_transformations.py
index 9b002498096bcf345670ccbb5d2cb2c863b7e02e..3ede70a85cac1ad1b10c46a90dab62390002f8a2 100644
--- a/pystencils_tests/test_transformations.py
+++ b/pystencils_tests/test_transformations.py
@@ -1,7 +1,7 @@
import pystencils as ps
from pystencils import TypedSymbol
from pystencils.astnodes import LoopOverCoordinate, SympyAssignment
-from pystencils.data_types import create_type
+from pystencils.typing import create_type
from pystencils.transformations import filtered_tree_iteration, get_loop_hierarchy, get_loop_counter_symbol_hierarchy
diff --git a/pystencils_tests/test_type_interference.py b/pystencils_tests/test_type_interference.py
index 953b87742304b2d629a1bd564fc23e0982d4f6d9..d240cebcd5b2efe651dd116d67b5d56fdfe0b182 100644
--- a/pystencils_tests/test_type_interference.py
+++ b/pystencils_tests/test_type_interference.py
@@ -1,25 +1,31 @@
-from sympy.abc import a, b, c, d, e, f
+from sympy.abc import a, b, c, d, e, f, g
import pystencils
-from pystencils.data_types import cast_func, create_type
+from pystencils.typing import CastFunc, create_type
def test_type_interference():
x = pystencils.fields('x: float32[3d]')
assignments = pystencils.AssignmentCollection({
- a: cast_func(10, create_type('float64')),
- b: cast_func(10, create_type('uint16')),
+ a: CastFunc(10, create_type('float64')),
+ b: CastFunc(10, create_type('uint16')),
e: 11,
c: b,
f: c + b,
d: c + b + x.center + e,
- x.center: c + b + x.center
+ x.center: c + b + x.center,
+ g: a + b + d
})
ast = pystencils.create_kernel(assignments)
+ code = pystencils.get_code_str(ast)
+ # print(code)
- code = str(pystencils.get_code_str(ast))
- assert 'double a' in code
- assert 'uint16_t b' in code
- assert 'uint16_t f' in code
- assert 'int64_t e' in code
+ assert 'const double a' in code
+ assert 'const uint16_t b' in code
+ assert 'const uint16_t f' in code
+ assert 'const int64_t e' in code
+
+ assert 'const float d = ((float)(b)) + ((float)(c)) + ((float)(e)) + _data_x_00_10[_stride_x_2*ctr_2];' in code
+ assert '_data_x_00_10[_stride_x_2*ctr_2] = ((float)(b)) + ((float)(c)) + _data_x_00_10[_stride_x_2*ctr_2];' in code
+ assert 'const double g = a + ((double)(b)) + ((double)(d));' in code
diff --git a/pystencils_tests/test_types.py b/pystencils_tests/test_types.py
index b6a7cd81cf8b7618ab69f6e0dd69094f93de3238..16466df5238dde45ae711e124db451c688182e17 100644
--- a/pystencils_tests/test_types.py
+++ b/pystencils_tests/test_types.py
@@ -1,24 +1,92 @@
+import pytest
+
+import pystencils.config
import sympy as sp
import numpy as np
import pystencils as ps
-from pystencils import data_types
-from pystencils.data_types import TypedSymbol, get_type_of_expression, VectorType, collate_types, create_type, \
- typed_symbols, type_all_numbers, matrix_symbols, cast_func, pointer_arithmetic_func, PointerType
+from pystencils.typing import TypedSymbol, get_type_of_expression, VectorType, collate_types, \
+ typed_symbols, CastFunc, PointerArithmeticFunc, PointerType, result_type, BasicType
+
+
+def test_result_type():
+ i = np.dtype('int32')
+ l = np.dtype('int64')
+ ui = np.dtype('uint32')
+ ul = np.dtype('uint64')
+ f = np.dtype('float32')
+ d = np.dtype('float64')
+ b = np.dtype('bool')
+
+ assert result_type(i, l) == l
+ assert result_type(l, i) == l
+ assert result_type(ui, i) == i
+ assert result_type(ui, l) == l
+ assert result_type(ul, i) == i
+ assert result_type(ul, l) == l
+ assert result_type(d, f) == d
+ assert result_type(f, d) == d
+ assert result_type(i, f) == f
+ assert result_type(l, f) == f
+ assert result_type(ui, f) == f
+ assert result_type(ul, f) == f
+ assert result_type(i, d) == d
+ assert result_type(l, d) == d
+ assert result_type(ui, d) == d
+ assert result_type(ul, d) == d
+ assert result_type(b, i) == i
+ assert result_type(b, l) == l
+ assert result_type(b, ui) == ui
+ assert result_type(b, ul) == ul
+ assert result_type(b, f) == f
+ assert result_type(b, d) == d
+
+
+@pytest.mark.parametrize('dtype', ('float64', 'float32', 'int64', 'int32', 'uint32', 'uint64'))
+def test_simple_add(dtype):
+ constant = 1.0
+ if dtype[0] in 'ui':
+ constant = 1
+ f = ps.fields(f"f: {dtype}[1D]")
+ d = TypedSymbol("d", dtype)
+
+ test_arr = np.array([constant], dtype=dtype)
+
+ ur = ps.Assignment(f[0], f[0] + d)
+
+ ast = ps.create_kernel(ur)
+ code = ps.get_code_str(ast)
+ kernel = ast.compile()
+ kernel(f=test_arr, d=constant)
+
+ assert test_arr[0] == constant+constant
+
+
+@pytest.mark.parametrize('dtype1', ('float64', 'float32', 'int64', 'int32', 'uint32', 'uint64'))
+@pytest.mark.parametrize('dtype2', ('float64', 'float32', 'int64', 'int32', 'uint32', 'uint64'))
+def test_mixed_add(dtype1, dtype2):
+
+ constant = 1
+ f = ps.fields(f"f: {dtype1}[1D]")
+ g = ps.fields(f"g: {dtype2}[1D]")
+ test_f = np.array([constant], dtype=dtype1)
+ test_g = np.array([constant], dtype=dtype2)
-def test_parsing():
- assert str(data_types.create_composite_type_from_string("const double *")) == "double const *"
- assert str(data_types.create_composite_type_from_string("double const *")) == "double const *"
+ ur = ps.Assignment(f[0], f[0] + g[0])
- t1 = data_types.create_composite_type_from_string("const double * const * const restrict")
- t2 = data_types.create_composite_type_from_string(str(t1))
- assert t1 == t2
+ # TODO Markus: check for the logging if colate_types(dtype1, dtype2) != dtype1
+ ast = ps.create_kernel(ur)
+ code = ps.get_code_str(ast)
+ kernel = ast.compile()
+ kernel(f=test_f, g=test_g)
+
+ assert test_f[0] == constant+constant
def test_collation():
- double_type = create_type("double")
- float_type = create_type("float32")
+ double_type = BasicType('float64')
+ float_type = BasicType('float32')
double4_type = VectorType(double_type, 4)
float4_type = VectorType(float_type, 4)
assert collate_types([double_type, float_type]) == double_type
@@ -27,20 +95,23 @@ def test_collation():
def test_vector_type():
- double_type = create_type("double")
- float_type = create_type("float32")
+ double_type = BasicType('float64')
+ float_type = BasicType('float32')
double4_type = VectorType(double_type, 4)
float4_type = VectorType(float_type, 4)
assert double4_type.item_size == 4
assert float4_type.item_size == 4
- assert not double4_type == 4
+ double4_type2 = VectorType(double_type, 4)
+ assert double4_type == double4_type2
+ assert double4_type != 4
+ assert double4_type != float4_type
def test_pointer_type():
- double_type = create_type("double")
- float_type = create_type("float32")
+ double_type = BasicType('float64')
+ float_type = BasicType('float32')
double4_type = PointerType(double_type, restrict=True)
float4_type = PointerType(float_type, restrict=False)
@@ -72,96 +143,103 @@ def test_assumptions():
assert x.shape[0].is_nonnegative
assert (2 * x.shape[0]).is_nonnegative
assert (2 * x.shape[0]).is_integer
- assert (TypedSymbol('a', create_type('uint64'))).is_nonnegative
- assert (TypedSymbol('a', create_type('uint64'))).is_positive is None
- assert (TypedSymbol('a', create_type('uint64')) + 1).is_positive
+ assert (TypedSymbol('a', BasicType('uint64'))).is_nonnegative
+ assert (TypedSymbol('a', BasicType('uint64'))).is_positive is None
+ assert (TypedSymbol('a', BasicType('uint64')) + 1).is_positive
assert (x.shape[0] + 1).is_real
-def test_sqrt_of_integer():
+@pytest.mark.parametrize('dtype', ('float64', 'float32'))
+def test_sqrt_of_integer(dtype):
"""Regression test for bug where sqrt(3) was classified as integer"""
- f = ps.fields("f: [1D]")
- tmp = sp.symbols("tmp")
+ f = ps.fields(f'f: {dtype}[1D]')
+ tmp = sp.symbols('tmp')
assignments = [ps.Assignment(tmp, sp.sqrt(3)),
ps.Assignment(f[0], tmp)]
- arr_double = np.array([1], dtype=np.float64)
- kernel = ps.create_kernel(assignments).compile()
- kernel(f=arr_double)
- assert 1.7 < arr_double[0] < 1.8
+ arr = np.array([1], dtype=dtype)
+ config = pystencils.config.CreateKernelConfig(data_type=dtype, default_number_float=dtype)
- f = ps.fields("f: float32[1D]")
- tmp = sp.symbols("tmp")
+ ast = ps.create_kernel(assignments, config=config)
+ kernel = ast.compile()
+ kernel(f=arr)
+ assert 1.7 < arr[0] < 1.8
- assignments = [ps.Assignment(tmp, sp.sqrt(3)),
- ps.Assignment(f[0], tmp)]
- arr_single = np.array([1], dtype=np.float32)
- config = ps.CreateKernelConfig(data_type="float32")
- kernel = ps.create_kernel(assignments, config=config).compile()
- kernel(f=arr_single)
-
- code = ps.get_code_str(kernel.ast)
- # ps.show_code(kernel.ast)
- # 1.7320508075688772935 --> it is actually correct to round to ...773. This was wrong before !282
- assert "1.7320508075688773f" in code
- assert 1.7 < arr_single[0] < 1.8
+ code = ps.get_code_str(ast)
+ constant = '1.7320508075688772f'
+ if dtype == 'float32':
+ assert constant in code
+ else:
+ assert constant not in code
-def test_integer_comparision():
- f = ps.fields("f [2D]")
- d = sp.Symbol("dir")
+@pytest.mark.parametrize('dtype', ('float64', 'float32'))
+def test_integer_comparision(dtype):
+ f = ps.fields(f"f: {dtype}[2D]")
+ d = TypedSymbol("dir", "int64")
ur = ps.Assignment(f[0, 0], sp.Piecewise((0, sp.Equality(d, 1)), (f[0, 0], True)))
ast = ps.create_kernel(ur)
code = ps.get_code_str(ast)
- assert "_data_f_00[_stride_f_1*ctr_1] = ((((dir) == (1))) ? (0.0): (_data_f_00[_stride_f_1*ctr_1]));" in code
+ # There should be an explicit cast for the integer zero to the type of the field on the rhs
+ if dtype == 'float64':
+ t = "_data_f_00[_stride_f_1*ctr_1] = ((((dir) == (1))) ? (0.0): (_data_f_00[_stride_f_1*ctr_1]));"
+ else:
+ t = "_data_f_00[_stride_f_1*ctr_1] = ((((dir) == (1))) ? (0.0f): (_data_f_00[_stride_f_1*ctr_1]));"
+ assert t in code
-def test_Basic_data_type():
+def test_typed_symbols_dtype():
assert typed_symbols(("s", "f"), np.uint) == typed_symbols("s, f", np.uint)
t_symbols = typed_symbols(("s", "f"), np.uint)
s = t_symbols[0]
assert t_symbols[0] == TypedSymbol("s", np.uint)
assert s.dtype.is_uint()
- assert s.dtype.is_complex() == 0
- assert typed_symbols("s", str).dtype.is_other()
- assert typed_symbols("s", bool).dtype.is_other()
- assert typed_symbols("s", np.void).dtype.is_other()
-
- assert typed_symbols("s", np.float64).dtype.base_name == 'double'
- # removed for old sympy version
- # assert typed_symbols(("s"), np.float64).dtype.sympy_dtype == typed_symbols(("s"), float).dtype.sympy_dtype
-
- f, g = ps.fields("f, g : double[2D]")
-
- expr = ps.Assignment(f.center(), 2 * g.center() + 5)
- new_expr = type_all_numbers(expr, np.float64)
-
- assert "cast_func(2, double)" in str(new_expr)
- assert "cast_func(5, double)" in str(new_expr)
-
- m = matrix_symbols("a, b", np.uint, 3, 3)
- assert len(m) == 2
- m = m[0]
- for i, elem in enumerate(m):
- assert elem == TypedSymbol(f"a{i}", np.uint)
- assert elem.dtype.is_uint()
+ assert typed_symbols("s", np.float64).dtype.c_name == 'double'
+ assert typed_symbols("s", np.float32).dtype.c_name == 'float'
assert TypedSymbol("s", np.uint).canonical == TypedSymbol("s", np.uint)
assert TypedSymbol("s", np.uint).reversed == TypedSymbol("s", np.uint)
def test_cast_func():
- assert cast_func(TypedSymbol("s", np.uint), np.int64).canonical == TypedSymbol("s", np.uint).canonical
+ assert CastFunc(TypedSymbol("s", np.uint), np.int64).canonical == TypedSymbol("s", np.uint).canonical
- a = cast_func(5, np.uint)
+ a = CastFunc(5, np.uint)
assert a.is_negative is False
assert a.is_nonnegative
def test_pointer_arithmetic_func():
- assert pointer_arithmetic_func(TypedSymbol("s", np.uint), 1).canonical == TypedSymbol("s", np.uint).canonical
+ assert PointerArithmeticFunc(TypedSymbol("s", np.uint), 1).canonical == TypedSymbol("s", np.uint).canonical
+
+
+def test_division():
+ f = ps.fields('f(10): float32[2D]')
+ m, tau = sp.symbols("m, tau")
+
+ up = [ps.Assignment(tau, 1 / (0.5 + (3.0 * m))),
+ ps.Assignment(f.center, tau)]
+ config = pystencils.config.CreateKernelConfig(data_type='float32', default_number_float='float32')
+ ast = ps.create_kernel(up, config=config)
+ code = ps.get_code_str(ast)
+
+ assert "((1.0f) / (m*3.0f + 0.5f))" in code
+
+
+def test_pow():
+ f = ps.fields('f(10): float32[2D]')
+ m, tau = sp.symbols("m, tau")
+
+ up = [ps.Assignment(tau, m ** 1.5),
+ ps.Assignment(f.center, tau)]
+
+ config = pystencils.config.CreateKernelConfig(data_type="float32", default_number_float='float32')
+ ast = ps.create_kernel(up, config=config)
+ code = ps.get_code_str(ast)
+
+ assert "1.5f" in code
diff --git a/pystencils_tests/test_vectorization.py b/pystencils_tests/test_vectorization.py
index ae4524fda19ab0caeff96be06573eb3970f97363..19f266b12b55ce66f971e0634e11f7178c6f70bc 100644
--- a/pystencils_tests/test_vectorization.py
+++ b/pystencils_tests/test_vectorization.py
@@ -1,8 +1,12 @@
import numpy as np
+
+import pytest
+
+import pystencils.config
import sympy as sp
import pystencils as ps
-from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets
+from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets, get_vector_instruction_set
from pystencils.cpu.vectorization import vectorize
from pystencils.fast_approximation import insert_fast_sqrts, insert_fast_divisions
from pystencils.enums import Target
@@ -15,6 +19,8 @@ else:
instruction_set = None
+
+# TODO: Skip tests if no instruction set is available and check all codes if they are really vectorised !
def test_vector_type_propagation(instruction_set=instruction_set):
a, b, c, d, e = sp.symbols("a b c d e")
arr = np.ones((2 ** 2 + 2, 2 ** 3 + 2))
@@ -28,13 +34,16 @@ def test_vector_type_propagation(instruction_set=instruction_set):
ast = ps.create_kernel(update_rule)
vectorize(ast, instruction_set=instruction_set)
+ # ps.show_code(ast)
+
func = ast.compile()
dst = np.zeros_like(arr)
func(g=dst, f=arr)
np.testing.assert_equal(dst[1:-1, 1:-1], 2 * 10.0 + 3)
-def test_aligned_and_nt_stores(instruction_set=instruction_set, openmp=False):
+@pytest.mark.parametrize('openmp', [True, False])
+def test_aligned_and_nt_stores(openmp, instruction_set=instruction_set):
domain_size = (24, 24)
# create a datahandling object
dh = ps.create_data_handling(domain_size, periodicity=(True, True), parallel=False, default_target=Target.CPU)
@@ -48,7 +57,7 @@ def test_aligned_and_nt_stores(instruction_set=instruction_set, openmp=False):
opt = {'instruction_set': instruction_set, 'assume_aligned': True, 'nontemporal': True,
'assume_inner_stride_one': True}
update_rule = [ps.Assignment(f.center(), 0.25 * (g[-1, 0] + g[1, 0] + g[0, -1] + g[0, 1]))]
- config = ps.CreateKernelConfig(target=dh.default_target, cpu_vectorize_info=opt, cpu_openmp=openmp)
+ config = pystencils.config.CreateKernelConfig(target=dh.default_target, cpu_vectorize_info=opt, cpu_openmp=openmp)
ast = ps.create_kernel(update_rule, config=config)
if instruction_set in ['sse'] or instruction_set.startswith('avx'):
assert 'stream' in ast.instruction_set
@@ -62,14 +71,12 @@ def test_aligned_and_nt_stores(instruction_set=instruction_set, openmp=False):
assert ast.instruction_set[instruction].split('{')[0] in ps.get_code_str(ast)
kernel = ast.compile()
+ # ps.show_code(ast)
+
dh.run_kernel(kernel)
np.testing.assert_equal(np.sum(dh.cpu_arrays['f']), np.prod(domain_size))
-def test_aligned_and_nt_stores_openmp(instruction_set=instruction_set):
- test_aligned_and_nt_stores(instruction_set, True)
-
-
def test_inplace_update(instruction_set=instruction_set):
shape = (9, 9, 3)
arr = np.ones(shape, order='f')
@@ -85,7 +92,7 @@ def test_inplace_update(instruction_set=instruction_set):
f1 @= 2 * s.tmp0
f2 @= 2 * s.tmp0
- config = ps.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+ config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
ast = ps.create_kernel(update_rule, config=config)
kernel = ast.compile()
kernel(f=arr)
@@ -93,6 +100,7 @@ def test_inplace_update(instruction_set=instruction_set):
def test_vectorization_fixed_size(instruction_set=instruction_set):
+ instructions = get_vector_instruction_set(instruction_set=instruction_set)
configurations = []
# Fixed size - multiple of four
arr = np.ones((20 + 2, 24 + 2)) * 5.0
@@ -112,6 +120,10 @@ def test_vectorization_fixed_size(instruction_set=instruction_set):
ast = ps.create_kernel(update_rule)
vectorize(ast, instruction_set=instruction_set)
+ code = ps.get_code_str(ast)
+ add_instruction = instructions["+"][:instructions["+"].find("(")]
+ assert add_instruction in code
+ # print(code)
func = ast.compile()
dst = np.zeros_like(arr)
@@ -165,7 +177,9 @@ def test_piecewise2(instruction_set=instruction_set):
g[0, 0] @= s.result
ast = ps.create_kernel(test_kernel)
+ # ps.show_code(ast)
vectorize(ast, instruction_set=instruction_set)
+ # ps.show_code(ast)
func = ast.compile()
func(f=arr, g=arr)
np.testing.assert_equal(arr, np.ones_like(arr))
@@ -181,7 +195,9 @@ def test_piecewise3(instruction_set=instruction_set):
g[0, 0] @= 1.0 / (s.b + s.k) if f[0, 0] > 0.0 else 1.0
ast = ps.create_kernel(test_kernel)
+ # ps.show_code(ast)
vectorize(ast, instruction_set=instruction_set)
+ # ps.show_code(ast)
ast.compile()
@@ -236,6 +252,7 @@ def test_vectorised_pow(instruction_set=instruction_set):
ast = ps.create_kernel(as1)
vectorize(ast, instruction_set=instruction_set)
+ print(ast)
ast.compile()
ast = ps.create_kernel(as2)
@@ -260,6 +277,7 @@ def test_vectorised_pow(instruction_set=instruction_set):
def test_vectorised_fast_approximations(instruction_set=instruction_set):
+ # fast_approximations are a gpu thing
arr = np.zeros((24, 24))
f, g = ps.fields(f=arr, g=arr)
@@ -267,18 +285,24 @@ def test_vectorised_fast_approximations(instruction_set=instruction_set):
assignment = ps.Assignment(g[0, 0], insert_fast_sqrts(expr))
ast = ps.create_kernel(assignment)
vectorize(ast, instruction_set=instruction_set)
- ast.compile()
+
+ with pytest.raises(Exception):
+ ast.compile()
expr = f[0, 0] / f[1, 0]
assignment = ps.Assignment(g[0, 0], insert_fast_divisions(expr))
ast = ps.create_kernel(assignment)
vectorize(ast, instruction_set=instruction_set)
- ast.compile()
+
+ with pytest.raises(Exception):
+ ast.compile()
assignment = ps.Assignment(sp.Symbol("tmp"), 3 / sp.sqrt(f[0, 0] + f[1, 0]))
ast = ps.create_kernel(insert_fast_sqrts(assignment))
vectorize(ast, instruction_set=instruction_set)
- ast.compile()
+
+ with pytest.raises(Exception):
+ ast.compile()
def test_issue40(*_):
@@ -290,7 +314,7 @@ def test_issue40(*_):
eq = [ps.Assignment(sp.Symbol('rho'), 1.0),
ps.Assignment(src[0, 0](0), sp.Rational(4, 9) * sp.Symbol('rho'))]
- config = ps.CreateKernelConfig(target=Target.CPU, cpu_vectorize_info=opt, data_type='float64')
+ config = pystencils.config.CreateKernelConfig(target=Target.CPU, cpu_vectorize_info=opt, data_type='float64')
ast = ps.create_kernel(eq, config=config)
code = ps.get_code_str(ast)
diff --git a/pystencils_tests/test_vectorization_specific.py b/pystencils_tests/test_vectorization_specific.py
index b13d8bc28f4a4daf621aa23b75578a87175a826d..367250dda361c2d08c3f614741c8566b545423ae 100644
--- a/pystencils_tests/test_vectorization_specific.py
+++ b/pystencils_tests/test_vectorization_specific.py
@@ -1,6 +1,8 @@
import pytest
import numpy as np
+
+import pystencils.config
import sympy as sp
import pystencils as ps
@@ -28,7 +30,7 @@ def test_vectorisation_varying_arch(instruction_set):
f1 @= 2 * s.tmp0
f2 @= 2 * s.tmp0
- config = ps.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+ config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
ast = ps.create_kernel(update_rule, config=config)
kernel = ast.compile()
kernel(f=arr)
@@ -47,7 +49,7 @@ def test_vectorized_abs(instruction_set, dtype):
f, g = ps.fields(f=arr, g=arr)
update_rule = [ps.Assignment(g.center(), sp.Abs(f.center()))]
- config = ps.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+ config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
ast = ps.create_kernel(update_rule, config=config)
func = ast.compile()
@@ -59,28 +61,47 @@ def test_vectorized_abs(instruction_set, dtype):
@pytest.mark.parametrize('dtype', ('float', 'double'))
@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
def test_strided(instruction_set, dtype):
- f, g = ps.fields(f"f, g : float{64 if dtype == 'double' else 32}[2D]")
+ type_string = "float64" if dtype == 'double' else "float32"
+
+ f, g = ps.fields(f"f, g : {type_string}[2D]")
update_rule = [ps.Assignment(g[0, 0], f[0, 0] + f[-1, 0] + f[1, 0] + f[0, 1] + f[0, -1] + 42.0)]
- if 'storeS' not in get_vector_instruction_set(dtype, instruction_set) and not instruction_set in ['avx512', 'rvv'] and not instruction_set.startswith('sve'):
+ if 'storeS' not in get_vector_instruction_set(dtype, instruction_set) and instruction_set not in ['avx512',
+ 'rvv'] and not instruction_set.startswith(
+ 'sve'):
with pytest.warns(UserWarning) as warn:
- config = ps.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+ config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set},
+ default_number_float=type_string)
ast = ps.create_kernel(update_rule, config=config)
assert 'Could not vectorize loop' in warn[0].message.args[0]
else:
with pytest.warns(None) as warn:
- config = ps.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set})
+ config = pystencils.config.CreateKernelConfig(cpu_vectorize_info={'instruction_set': instruction_set},
+ default_number_float=type_string)
ast = ps.create_kernel(update_rule, config=config)
assert len(warn) == 0
+
+ # ps.show_code(ast)
func = ast.compile()
- ref_func = ps.create_kernel(update_rule).compile()
+ ref_config = pystencils.config.CreateKernelConfig(default_number_float=type_string)
+ ref_func = ps.create_kernel(update_rule, config=ref_config).compile()
- arr = np.random.random((23 + 2, 17 + 2)).astype(np.float64 if dtype == 'double' else np.float32)
- dst = np.zeros_like(arr, dtype=np.float64 if dtype == 'double' else np.float32)
- ref = np.zeros_like(arr, dtype=np.float64 if dtype == 'double' else np.float32)
+ # For some reason other array creations fail on the emulated ppc pipeline
+ size = (25, 19)
+ arr = np.zeros(size).astype(type_string)
+ for i in range(size[0]):
+ for j in range(size[1]):
+ arr[i, j] = i * j
+
+ dst = np.zeros_like(arr, dtype=type_string)
+ ref = np.zeros_like(arr, dtype=type_string)
func(g=dst, f=arr)
ref_func(g=ref, f=arr)
- np.testing.assert_almost_equal(dst, ref, 13 if dtype == 'double' else 5)
+
+ # print("dst: ", dst)
+ # print("np array: ", arr)
+
+ np.testing.assert_almost_equal(dst[1:-1, 1:-1], ref[1:-1, 1:-1], 13 if dtype == 'double' else 5)
@pytest.mark.parametrize('dtype', ('float', 'double'))
@@ -99,7 +120,7 @@ def test_alignment_and_correct_ghost_layers(gl_field, gl_kernel, instruction_set
update_rule = ps.Assignment(dst[0, 0], src[0, 0])
opt = {'instruction_set': instruction_set, 'assume_aligned': True,
'nontemporal': True, 'assume_inner_stride_one': True}
- config = ps.CreateKernelConfig(target=dh.default_target, cpu_vectorize_info=opt, ghost_layers=gl_kernel)
+ config = pystencils.config.CreateKernelConfig(target=dh.default_target, cpu_vectorize_info=opt, ghost_layers=gl_kernel)
ast = ps.create_kernel(update_rule, config=config)
kernel = ast.compile()
if gl_kernel != gl_field:
@@ -122,11 +143,11 @@ def test_cacheline_size(instruction_set):
assert cacheline_size & (cacheline_size - 1) == 0, "Cache line size is not a power of 2"
-# test_vectorization is not parametrized because it is supposed to run without pytest, so we parametrize it here
+# TODO move to vectorise
@pytest.mark.parametrize('instruction_set',
sorted(set(supported_instruction_sets) - {test_vectorization.instruction_set}))
@pytest.mark.parametrize('function',
- [f for f in test_vectorization.__dict__ if f.startswith('test_') and f != 'test_hardware_query'])
+ [f for f in test_vectorization.__dict__ if f.startswith('test_') and f not in ['test_hardware_query', 'test_aligned_and_nt_stores']])
def test_vectorization_other(instruction_set, function):
test_vectorization.__dict__[function](instruction_set)
@@ -135,8 +156,8 @@ def test_vectorization_other(instruction_set, function):
@pytest.mark.parametrize('instruction_set', supported_instruction_sets)
@pytest.mark.parametrize('field_layout', ('fzyx', 'zyxf'))
def test_square_root(dtype, instruction_set, field_layout):
- config = ps.CreateKernelConfig(data_type=dtype,
- cpu_vectorize_info={'instruction_set': instruction_set,
+ config = pystencils.config.CreateKernelConfig(data_type=dtype,
+ cpu_vectorize_info={'instruction_set': instruction_set,
'assume_inner_stride_one': True,
'assume_aligned': False, 'nontemporal': False})
diff --git a/setup.py b/setup.py
index ed4e3faff472aeef204ee6c2ef29705fd0c844a9..79f1f108bb5aaf67db4db229575829e97d3fdd47 100644
--- a/setup.py
+++ b/setup.py
@@ -16,10 +16,9 @@ except ImportError:
USE_CYTHON = False
quick_tests = [
- 'test_datahandling.test_kernel',
- 'test_blocking_staggered.test_blocking_staggered',
- 'test_blocking_staggered.test_blocking_staggered',
- 'test_vectorization.test_vectorization_variable_size',
+ 'test_quicktests.test_basic_kernel',
+ 'test_quicktests.test_basic_blocking_staggered',
+ 'test_quicktests.test_basic_vectorization',
]
@@ -91,7 +90,7 @@ setuptools.setup(name='pystencils',
author_email='cs10-codegen@fau.de',
url='https://i10git.cs.fau.de/pycodegen/pystencils/',
packages=['pystencils'] + ['pystencils.' + s for s in setuptools.find_packages('pystencils')],
- install_requires=['sympy>=1.5.1,<=1.10', 'numpy>=1.8.0', 'appdirs', 'joblib'],
+ install_requires=['sympy>=1.6,<=1.10', 'numpy>=1.8.0', 'appdirs', 'joblib'],
package_data={'pystencils': ['include/*.h',
'backends/cuda_known_functions.txt',
'backends/opencl1.1_known_functions.txt',