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Commit b556372a authored by Frederik Hennig's avatar Frederik Hennig
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Reverted API name change

parent 3842b69c
Pipeline #34471 passed with stages
in 40 minutes and 13 seconds
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lbmpy/macroscopic_value_kernels.py
View file @ b556372a
......@@ -8,11 +8,11 @@ from lbmpy.advanced_streaming.utility import get_accessor, Timestep
def pdf_initialization_assignments(lb_method, density, velocity, pdfs,
streaming_pattern='pull', timestep=Timestep.BOTH,
streaming_pattern='pull', previous_timestep=Timestep.BOTH,
set_pre_collision_pdfs=False):
"""Assignments to initialize the pdf field with equilibrium"""
if isinstance(pdfs, Field):
accessor = get_accessor(streaming_pattern, timestep)
accessor = get_accessor(streaming_pattern, previous_timestep)
if set_pre_collision_pdfs:
field_accesses = accessor.read(pdfs, lb_method.stencil)
else:
......@@ -32,10 +32,10 @@ def pdf_initialization_assignments(lb_method, density, velocity, pdfs,
def macroscopic_values_getter(lb_method, density, velocity, pdfs,
streaming_pattern='pull', timestep=Timestep.BOTH,
streaming_pattern='pull', previous_timestep=Timestep.BOTH,
use_pre_collision_pdfs=False):
if isinstance(pdfs, Field):
accessor = get_accessor(streaming_pattern, timestep)
accessor = get_accessor(streaming_pattern, previous_timestep)
if use_pre_collision_pdfs:
field_accesses = accessor.read(pdfs, lb_method.stencil)
else:
......
lbmpy_tests/advanced_streaming/test_fully_periodic_flow.py
View file @ b556372a
......@@ -105,14 +105,14 @@ def test_fully_periodic_flow(target, stencil, streaming_pattern):
setter = macroscopic_values_setter(
lb_method, density, velocity, pdfs,
streaming_pattern=streaming_pattern, timestep=zeroth_timestep)
streaming_pattern=streaming_pattern, previous_timestep=zeroth_timestep)
setter_kernel = create_kernel(setter, ghost_layers=1, target=target).compile()
getter_kernels = []
for t in timesteps:
getter = macroscopic_values_getter(
lb_method, density_field, velocity_field, pdfs,
streaming_pattern=streaming_pattern, timestep=t)
streaming_pattern=streaming_pattern, previous_timestep=t)
getter_kernels.append(create_kernel(getter, ghost_layers=1, target=target).compile())
# Periodicity
......
lbmpy_tests/advanced_streaming/test_periodic_pipe_with_force.py
View file @ b556372a
......@@ -106,14 +106,14 @@ class PeriodicPipeFlow:
setter = macroscopic_values_setter(
self.lb_method, self.density, self.velocity, self.pdfs,
streaming_pattern=self.streaming_pattern, timestep=self.zeroth_timestep)
streaming_pattern=self.streaming_pattern, previous_timestep=self.zeroth_timestep)
self.init_kernel = create_kernel(setter, ghost_layers=1, target=self.target).compile()
self.getter_kernels = []
for t in self.timesteps:
getter = macroscopic_values_getter(
self.lb_method, self.density_field, self.velocity_field, self.pdfs,
streaming_pattern=self.streaming_pattern, timestep=t)
streaming_pattern=self.streaming_pattern, previous_timestep=t)
self.getter_kernels.append(create_kernel(getter, ghost_layers=1, target=self.target).compile())
# Periodicity
......
lbmpy_tests/centeredcumulant/test_flow_around_sphere.py
View file @ b556372a
......@@ -104,7 +104,7 @@ def flow_around_sphere(stencil, galilean_correction, L_LU, total_steps):
init_eqs = pdf_initialization_assignments(lb_method, 1.0, initial_velocity, pdf_field,
streaming_pattern=streaming_pattern,
timestep=timesteps[0])
previous_timestep=timesteps[0])
init_kernel = create_kernel(init_eqs, target=target).compile()
output = {
......
lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
View file @ b556372a
......@@ -99,18 +99,18 @@
self.velocity = (u_x,) * self.dim
self.velocity_field = self.dh.add_array('u', self.dim)
setter = macroscopic_values_setter(
self.lb_method, self.density, self.velocity, self.pdfs,
streaming_pattern=self.streaming_pattern, timestep=self.zeroth_timestep)
streaming_pattern=self.streaming_pattern, previous_timestep=self.zeroth_timestep)
self.init_kernel = create_kernel(setter, ghost_layers=1, target=self.target).compile()
self.getter_kernels = []
for t in self.timesteps:
getter = macroscopic_values_getter(
self.lb_method, self.density_field, self.velocity_field, self.pdfs,
streaming_pattern=self.streaming_pattern, timestep=t)
streaming_pattern=self.streaming_pattern, previous_timestep=t)
self.getter_kernels.append(create_kernel(getter, ghost_layers=1, target=self.target).compile())
# Periodicity
self.periodicity_handler = LBMPeriodicityHandling(
self.stencil, self.dh, self.pdfs.name, streaming_pattern=self.streaming_pattern)
......@@ -225,11 +225,11 @@
ps.plot.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7faa51244c40>
<matplotlib.colorbar.Colorbar at 0x7fd9a51311c0>
%%%% Output: display_data
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA00AAAFoCAYAAACL/RCAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAqe0lEQVR4nO3df4wc533f8c/n9vZ+8I4ySekkMZRUKTEThAka2iVkAS4KJ45sSg1Ku4AAqa6tOkZpIxJgAy4KxfkjTo0CRhDbiQtVghwRllHVggXbNWGwkVnVgWsgdkS5iiyFUcWqikWLFilRFHkk725v99s/bhivz3d735m7JXdv3y9gwN3Zz849t7M7x+8+zzzjiBAAAAAAYGlDl7oBAAAAANDLKJoAAAAAoAOKJgAAAADogKIJAAAAADqgaAIAAACADiiaAAAAAKADiiYAAAAAPcn2btvP2T5i+54lHrftzxePP237rcX6Mdt/bftvbD9r+4/anrPF9kHbzxf/bl6pHRRNAAAAAHqO7ZqkeyXdImmHpDts71gUu0XS9mLZK+m+Yv2spN+KiN+QtFPSbts3FY/dI+nxiNgu6fHifkcUTQAAAAB60Y2SjkTECxExJ+kRSXsWZfZI+lIs+J6kTba3Fveni0y9WKLtOQ8Vtx+S9J6VGjK8ut+jnCuuuCKuv/76i/kjAeCi+j9PvpDOeqhL31u5C9uMlSOVNttqpbO//E9+sTuNAIAe8OSTT74aEVOXuh1lvPs3J+K1k83Kz3/y6dlnJc20rXogIh5ou79N0ktt949KetuizSyV2SbpWNFT9aSkN0u6NyK+X2SuiohjkhQRx2xfuVJbL2rRdP311+vQoUMX80cCwEV189Bt6ezQ+Ib8hmu1dNRe+6opokTV1Mz/AW2dO5fOHjz0aL4NANBnbP/9pW5DWa+ebOr7j11T+fn1rf93JiJ2dYgs9Qdt8R+kZTMR0ZS00/YmSV+3/esR8UyVtjI8DwAAAEAvOirp2rb710h6uWwmIk5J+ktJu4tVr9jeKknFv8dXaghFEwAAAIAKQs1oVV4SnpC03fYNtkck3S5p/6LMfkkfKGbRu0nSG8WQu6mih0m2xyX9tqS/a3vOncXtOyV9Y6WGXNTheQAAAADWh5DU6tZJr5IiYt723ZIek1STtC8inrX9keLx+yUdkHSrpCOSzkn6YPH0rZIeKs5rGpL0lYj4ZvHYpyV9xfaHJP1I0opj6ymaAAAAAFTSUn5Cnyoi4oAWCqP2dfe33Q5Jdy3xvKclvWWZbb4m6Z1l2sHwPAAAAADogJ4mAAAAAKWFQs0ys6v2MYomAAAAAJV085ymXkLRBAAAAKC0kNSkaAKA/lLqwrITE+msN+QvQlvb8cvpbHNyNJ1tjeQvbqva2l/cVs38H8WhufzFbWvTs+ns7qt+L52NEhfNbZ09m84ebHGBXQBoNyg9TUwEAQAAAAAd0NMEAAAAoLSQmAgCAAAAADrp7lWaegdFEwAAAIDSQsFEEAAAAACwrCg1T1BfYyIIAAAAAOiAniYAAAAApYU4pwkAAAAAOrCa6sK1AXsQRRMAAACA0kJSi3OaAAAAAAD0NAHoae8afV86W9v+i+ls8/LJdLYxWc9nN+YPq/Pj+SENzXo+G7V0NM3NfLbWyH/tOHx+Qzpb/4WN+ex0I52tvTadzpZ5P35r9uF0FgD6FcPzAAAAAGAZIYomAAAAAOioFRRNAAAAALCkQeppYiIIAAAAAOiAniYAAAAApYWs5oD0wVA0AQAAAKiEc5oAAAAAYBmDdE4TRRMAAACACqxmDMbwvMH4LQEAAACgInqaAAAAAJQWkloD0gdD0QRgTdw8dFs6W7t8SzrrX3tzOnv+6ol0dmZzLZ1tTObHazcm8tnmaDqqVomjdeR/tTQ389mh+fxrUJvNb7d+Nv+HuT5dT2fHNuV3xNjEWDq7e+rD6WzztZOp3MHWo+ltAsDFwDlNAAAAALCMCM5pAgAAAACIniYAAAAAFbUYngcAAAAAS1u4TtNgDFyjaAIAAABQweCc00TRBAAAAKC0QZpyfDB+SwAAAACoiJ4mAAAAAJU0g4kgAAAAAGBJITMRBAAAAAB00mIiCACQ3jVyRypXe/MN6W02tm5KZ89fOZLOzmzJH7hn35QfTjA/mY5qfizS2VaJbNRLZIfy2Sy38q+XG/ns0Ew+O1wmO53PNiZLZCc2prPjG0fT2fqxN6Vy2c+jJH1r7svpLABUMUhTjg/GbwkAAAAAFa1YNNm+1va3bR+2/aztjxbrP2n7x7afKpZbu99cAAAAAL0gZDWj+tJPMsPz5iV9PCJ+YHujpCdtHywe+1xE/En3mgcAAACgVw3KdZpWLJoi4pikY8XtM7YPS9rW7YYBAAAA6F0RUnNAJoIo9Vvavl7SWyR9v1h1t+2nbe+zvXmtGwcAAAAAl1q6aLI9Kemrkj4WEacl3SfplyTt1EJP1GeWed5e24dsHzpx4sTqWwwAAACgB1itVSz9JFU02a5roWB6OCK+JkkR8UpENCOiJekLkm5c6rkR8UBE7IqIXVNTU2vVbgAAAACXUGhheF7VpZ+seE6TbUt6UNLhiPhs2/qtxflOkvReSc90p4kAAAAAetGgXKcpM3ve2yW9X9IPbT9VrPuEpDts79RCkfmipA93oX0AAAAAelDIavXZ1OFVZWbP+6605KDDA2vfHAAAAADoLZmeJgDrzM1Dt6WztTffkMrNXrclvc1zV9fT2fOX57v95zalo5q7LNLZ5mQzv+GxfLY2ms/Wh/PZoaH875bVauW/SZyfr+WzsyWyM/lsbSKfbY6X+N3GSmx3dDSd3VDPfX7yWyz3OT/YerTElgHgp7o9PM/2bkl/Jqkm6c8j4tOLHnfx+K2Szkn6N8X1Za+V9CVJV0tqSXogIv6seM4nJf1bSRdmqftERHTsEKJoAgAAAFBaSGp1cUIH2zVJ90q6WdJRSU/Y3h8Rf9sWu0XS9mJ5mxZm+H6bpHlJHy8KqI2SnrR9sO25n4uIP8m2haIJAAAAQAVWs7tTh98o6UhEvCBJth+RtEdSe9G0R9KXIiIkfc/2prYJ645JUkScsX1Y0rZFz00bjOkuAAAAAKypCz1NVRdJV1y4nmux7F30I7ZJeqnt/tFiXamM7eslvUXS99tW3237adv7bG9e6XelaAIAAABwKbx64XquxfLAoseX6sZafOJux4ztSS1cb/ZjEXG6WH2fpF+StFMLvVGfWamhDM8DAAAAUEmXh+cdlXRt2/1rJL2czdiua6FgejgivnYhEBGvXLht+wuSvrlSQ+hpAgAAAFBahFc7PG8lT0jabvsG2yOSbpe0f1Fmv6QPeMFNkt6IiGPFrHoPSjocEZ9tf4LtrW133yvpmZUaQk8TAAAAgEqaXZw9LyLmbd8t6TEtTDm+LyKetf2R4vH7tXDt2FslHdHClOMfLJ7+dknvl/RD208V6y5MLf7HtndqYRjfi5I+vFJbKJoAAAAA9KSiyDmwaN39bbdD0l1LPO+7Wvp8J0XE+8u2g6IJAAAAQGkhqdXdc5p6BkUTMIBql29JZxtbN6Vy566up7d57sp8V/7slsWT5CxvblMrnY2N8+ns6MRcOrthrER2pJHOjtfz2Zrzr0NWmeEX5xsl3gtzJbIzI+ns7Fg+OzOa/1PYqudfh6iVGbKSex2GGpvyW3w9/zkHgGrc1eF5vYSiCQAAAEBpC9dpoqcJAAAAAJbVHJDJuAfjtwQAAACAiuhpAgAAAFBayAzPAwAAAIBOWgMycI2iCQAAAEBpEVKTniYAAAAAWN6gDM8bjP40AAAAAKiIniYAAAAApS1MBDEYfTAUTQAAAAAqaWowhudRNAHrxLtG35fO+tfenM6ev3Ikl7s8/03T7JZIZ+e2tNJZb5pLZzdOzqSzmzecT2c3jeazl43ks+O1Rjpbd/41y2qU+CbxfLOezp6eG09nT43ls6+P5LPT9bF0dm4o93lYkH/N3Mxla7P5nz985up0tszx41uzD6ezANa3EOc0AQAAAABETxMAAACASjinCQAAAAA6anFOEwAAAAAsjYvbAgAAAMAKBmV43mD8lgAAAABQET1NAAAAAEpbuLgtw/MAAAAAYFlMBAEAAAAAy+DitgAAAAAASfQ0AT3t5qHb0tna9l9MZ89fPZHOzmzJfbcytym9Sc1taqWz3jSXzr7psnPp7JWT0/ns+Jl0dmokv93LhmfS2Q212XS27mY6m9WIWjp7rjmazp4eGUtnT4xMprNjwxvT2eND+ffjG+mkNNcaSWeHGrnXd2Ym/11n/Wz+cz5+9pp0tsxx6WDr0XQWQH8alNnzKJoAAAAAlBdMBAEAAAAAywoxEQQAAAAAdDQoPU2DMQgRAAAAACqipwkAAABAaYM05ThFEwAAAIBKKJoAAAAAYBkhZs8DAAAAgI4GZfY8JoIAAAAAgA7oaQIAAABQXnBOE4AeMDQxkc42L59MZ2c219LZ2TflDoZzl0V6m7FxPp3dODmTzl45OZ3ObtvwRjq7dSyfvaJ+Jp3dUsu3d2JoLp2tO//6ZjUi/+fibGsknT05nH/fjtca6WzdrXS2jGYrP0DjdCOfnZvNZWvn8/85qU/nP+cjJY4fQy/nj0sA1jdmzwMAAACAFQxK0cQ5TQAAAADQwYpFk+1rbX/b9mHbz9r+aLF+i+2Dtp8v/t3c/eYCAAAA6AUXphyvuvSTTE/TvKSPR8SvSrpJ0l22d0i6R9LjEbFd0uPFfQAAAAADIsKVl36yYtEUEcci4gfF7TOSDkvaJmmPpIeK2EOS3tOlNgIAAADoQS258tJPSk0EYft6SW+R9H1JV0XEMWmhsLJ95TLP2StpryRdd911q2osAAAAgN4QAzTleHoiCNuTkr4q6WMRcTr7vIh4ICJ2RcSuqampKm0EAAAAgEsm1dNku66FgunhiPhasfoV21uLXqatko53q5EAAAAAek+/nZtUVWb2PEt6UNLhiPhs20P7Jd1Z3L5T0jfWvnkAAAAAetPgzJ6X6Wl6u6T3S/qh7aeKdZ+Q9GlJX7H9IUk/knRbV1oIAAAAoCcNSk/TikVTRHxXWnZ6i3eubXMAtPOGDelsY7JeIps/wM1P5nLNyWZ6m6MTc+ns5g3n09krx8+ks1vH3khnrxk5mc5ODefbsKl2Np2dcP41qzu/L7IaUUtnz8ZIOjsxlP+9xtxIZ8toRP467zPz+c/Z7Fx+rqXZmdzrO382v80yn/Myx4+xEsclAOtbiIkgAAAAAACiaAIAAABQRSxMO151ybC92/Zzto/YvmeJx23788XjT9t+a7H+Wtvftn3Y9rO2P9r2nC22D9p+vvh380rtoGgCAAAAUEk3L25ruybpXkm3SNoh6Q7bOxbFbpG0vVj2SrqvWD8v6eMR8auSbpJ0V9tz75H0eERsl/R4cb8jiiYAAAAApYUWJoKouiTcKOlIRLwQEXOSHpG0Z1Fmj6QvxYLvSdp04bJIEfEDSYqIM5IOS9rW9pyHitsPSXrPSg2haAIAAABwKVxh+1DbsnfR49skvdR2/6h+WvikM7avl/QWSd8vVl0VEcckqfj3ypUamp+GBwAAAAD+waqvt/RqROzq+AN+3uKzoTpmbE9K+qqkj0XE6fJNXEBPEwAAAIBKujwRxFFJ17bdv0bSy9mM7boWCqaHI+JrbZlXbG8tMlslHV+pIRRNAAAAACrp8jlNT0jabvsG2yOSbpe0f1Fmv6QPFLPo3STpjYg4ZtuSHpR0OCI+u8Rz7ixu3ynpGys1hOF5AAAAAEpb6DHq3sVtI2Le9t2SHpNUk7QvIp61/ZHi8fslHZB0q6Qjks5J+mDx9LdLer+kH9p+qlj3iYg4IOnTkr5i+0OSfiTptpXaQtEEAAAAoCcVRc6BRevub7sdku5a4nnf1dLnOykiXpP0zjLtoGgCLrKbh1b8MuMf1Hb8cjrb2Jj/ODcm8t8KzY8lrz431kxvc8PYXDq7afR8Ojs1Mp3OXlE/k9/ucJls/hzTTUMz6exGz6ez9S586ddIvg0k6Uw00tm68++bMmains6eb+azp0fH89mx0XR2dmwklZsfq6W3WeZzXub4MTa14jUg/0GZ493B1qPpLIDescqJIPoGRRMAAACASpITOvQ9iiYAAAAAlXTznKZeQtEEAAAAoLRQeha8vseU4wAAAADQAT1NAAAAACoZkFOaKJoAAAAAVNDl6zT1EoomAAAAANUMSFcT5zQBAAAAQAf0NAEAAACohOF5AAAAANABF7cF0BVDGzaks83J0XR2fjz/TU8zv1m1xnJHw9poM73NDSONdPaykfP57PBMOrulNp3ObqqdzWeHSrRhaD6dnXB+NHW9RDarEa38z4/87yXlX69GrZbOnm2NpLNvDOc/k2XejxtGJtLZ08nPT/bzKEnN0fwxodTxo8RxqczxDkD/CdHTBAAAAADLC0kDUjQxEQQAAAAAdEBPEwAAAIBKOKcJAAAAADqhaAIAAACA5ZiJIAAAAACgowHpaWIiCAAAAADogJ4mAAAAAOUF12kCAAAAgM4GZHgeRRMAAACAiuhpAtANtVo62hrJZ5v1/EGrVeKTH/XcV0j14WZ6m+P1Rj5by2c31GbT2YmhuXzW+exGz5fYbv600g1D9XR2WPn3TVbd+f2rVn6fNUq8XudK7Icy+7fM+6bM+7HM+3w4+fmZS34epXKf81LHjxLHpaESxzsA6GUUTQAAAACqYXgeAAAAAHRA0QQAAAAAywhJzJ4HAAAAAMuLAelp4uK2AAAAANABPU0AAAAAqhmQniaKJgAAAADVcE4TAAAAACzP9DQBAAAAwDJCAzM8j4kgAAAAAKADepqAi8wuMfa3ls9GLb/ZUtmh3FdIQ8mcJNXcSmfrpbLNEtn5Lm03HVXd+e+thpXfabUS200r8U1iuder3/Zvvr1l3ufZz0/28yh175hQ5rhU6ngHoA+Zc5oAAAAAoCOG5y2wvc/2cdvPtK37pO0f236qWG7tbjMBAAAA9JxYxdJHMmM4vihp9xLrPxcRO4vlwNo2CwAAAAB6w4pFU0R8R9LJi9AWAAAAAP2EnqYV3W376WL43ublQrb32j5k+9CJEydW8eMAAAAA9IzQwkQQVZc+UrVouk/SL0naKemYpM8sF4yIByJiV0TsmpqaqvjjAAAAAPQaR/Wln1QqmiLilYhoRkRL0hck3bi2zQIAAADQ8xietzzbW9vuvlfSM8tlAQAAAKCfrXidJttflvQOSVfYPirpDyW9w/ZOLdSIL0r6cPeaCAAAAACXzopFU0TcscTqB7vQFgAAAAB9pN/OTapqxaIJAAAAAJbUZ7PgVUXRBAAAAKC8PpzQoarVXKcJAAAAANY9epoAAAAAVENPEwAAAAAsr9sXt7W92/Zzto/YvmeJx23788XjT9t+a9tj+2wft/3Moud80vaPbT9VLLeu1A6KJgAAAADVdPHitrZrku6VdIukHZLusL1jUewWSduLZa+k+9oe+6Kk3cts/nMRsbNYDqzUFoomAAAAAL3oRklHIuKFiJiT9IikPYsyeyR9KRZ8T9Im21slKSK+I+nkWjSEogkAAABANavrabrC9qG2Ze+irW+T9FLb/aPFurKZpdxdDOfbZ3vzSmEmggAAAABQWplzk5bxakTs6vQjlli3+CdmMovdJ+lTRe5Tkj4j6Xc7PYGiCQAAAEA13b247VFJ17bdv0bSyxUyPyMiXrlw2/YXJH1zpYZQNAEXWUSJr2Sa+ayb+c2WyrZyB8NWMidJzciPDG6UytZKZPOHv3LbTUfViFY6Wy+z07ow/eu88j+/zO9V7vXqhf2bfz+WeZ9nPz/Zz6PUvWNCmeNSqeMdgP7U3Y/5E5K2275B0o8l3S7pXy3K7NfCULtHJL1N0hsRcazTRm1vbcu8V9IznfISRRMAAACAHhQR87bvlvSYpJqkfRHxrO2PFI/fL+mApFslHZF0TtIHLzzf9pclvUML504dlfSHEfGgpD+2vVMLJd+Lkj68UlsomgAAAABUsspzmlZUTAd+YNG6+9tuh6S7lnnuHcusf3/ZdlA0AQAAAKhmQEbhUjQBAAAAKG/1s+f1Da7TBAAAAAAd0NMEAAAAoJoB6WmiaAIAAABQDUUTAAAAACyPc5oAAAAAABRNAAAAANAJw/OAi63ZTEeH5vLZWiPfPz4073TWjVx2fr6W3ub5Rj2fbeaz55qj6ezZ1kg+G/nsmWiks/WYT2fVKrFd5983WY1opbNnS2TPRP7PUJn9UGb/lnnflHk/lnmfZz8/2c+jJA2VeHuVOn6UOC6VOd4B6FMDMjyPogkAAABAeQN0nSaKJgAAAADVUDQBAAAAQAcDUjQxEQQAAAAAdEBPEwAAAIDSLM5pAgAAAIDOKJoAAAAAYBkDNHse5zQBAAAAQAf0NAEAAACoZkB6miiaAAAAAFRD0QSgG1rnzqWztenZdHb4/Ib8dvOb1dCMU7n52Vp6m+fm6uns6bnxfHZkLJ09OTyZzk4MzaWzdTfTWWkmnWx4vkQbWiXakPz5Jf4onon8n5ZTrfw+O9WcSGdPNvP79/R8vg1l3o9l3ufN5OdnOPl5lMp9zofP53dwmeNSs8TxDkB/GpRzmiiaAAAAAFQzIEUTE0EAAAAAQAf0NAEAAAAoLzQwPU0UTQAAAAAq4ZwmAAAAAOiEogkAAAAAljcoPU1MBAEAAAAAHdDTBAAAAKCaAelpomgCAAAAUB6z5wEAAADA8lwsg4CiCbjIDrYeTWd3X/V76Wz9Fzbms2fzpzMOz+QOh/MztfQ2z82MpLOnxsbT2RMjk+nseK2Rzo45ny2jUSvxmnkuna27WaU5HTUi39azUWL/NifS2RPz+ff4q4189sRc/n1zajb/fizzPlfy85P9PEpS/Wz+69/6mfl0VideT0fLHO8AoJdRNAEAAACohuF5AAAAALA8phwv2N5n+7jtZ9rWbbF90Pbzxb+bu9tMAAAAAD0nVrH0kcyJDV+UtHvRunskPR4R2yU9XtwHAAAAMEgomhZExHcknVy0eo+kh4rbD0l6z9o2CwAAAAB6Q9Vzmq6KiGOSFBHHbF+5XND2Xkl7Jem6666r+OMAAAAA9JTgnKY1ExEPRMSuiNg1NTXV7R8HAAAA4GJheF5Hr9jeKknFv8fXrkkAAAAA+oGj+tJPqhZN+yXdWdy+U9I31qY5AAAAAPoGPU0LbH9Z0l9J+hXbR21/SNKnJd1s+3lJNxf3AQAAAGDdWXEiiIi4Y5mH3rnGbQGwSJw7l87WpxslsvV0dnjaqVxtopbe5uzYSDr7+sh4Ojs2vDGdrbuVzpYxE/nX9mwr/zpMDM2ls3XPp7NZjcjPG1Tm9zrZnExnX23k9++xmTels8fP57f7+rn8+3H2bP51qE3nPj/D0+lNqj6d/xq3zPGjzHEJwPrXb8Psqqo6ex4AAACAQdaHw+yqomgCAAAAUM2AFE1dn3IcAAAAAPoZPU0AAAAASrM4pwkAAAAAOhuQoonheQAAAAAqcUTlJbV9e7ft52wfsX3PEo/b9ueLx5+2/da2x/bZPm77mUXP2WL7oO3ni383r9QOiiYAAAAA5a3mwraJmsl2TdK9km6RtEPSHbZ3LIrdIml7seyVdF/bY1+UtHuJTd8j6fGI2C7p8eJ+RxRNAAAAAHrRjZKORMQLETEn6RFJexZl9kj6Uiz4nqRNtrdKUkR8R9LJJba7R9JDxe2HJL1npYZQNAEAAACoxFF9kXSF7UNty95Fm98m6aW2+0eLdWUzi10VEcckqfj3ypV+TyaCAAAAAFDN6iaCeDUidnV43ImfmMmsGkUT0MNaZ8+ms7XXptPZsU2j6Wxjcqlj0c9rjudykjQzmj/0TNfH0tnjQ610toxG5Dvlzzfr6ewbwxvS2Q212XS27mY6m9WIWjp7rpl/f52ez+/fE3OT6ezx8xvz2en8dqen8+31mfz7fOR07vMz+kb+/wFjr+ffB2WOH80SxyUA61+Xpxw/KunatvvXSHq5QmaxV2xvjYhjxVC+4ys1hOF5AAAAAKrp4kQQkp6QtN32DbZHJN0uaf+izH5JHyhm0btJ0hsXht51sF/SncXtOyV9Y6WGUDQBAAAA6DkRMS/pbkmPSTos6SsR8aztj9j+SBE7IOkFSUckfUHS7114vu0vS/orSb9i+6jtDxUPfVrSzbafl3Rzcb8jhucBAAAAKC+6PjxPEXFAC4VR+7r7226HpLuWee4dy6x/TdI7y7SDogkAAABANV0umnoFRRMAAACA0qzu9zT1Cs5pAgAAAIAO6GkCAAAAUE0MRlcTRRMAAACASgZleB5FEwAAAIDy8tdb6nsUTQAAAAAqcetSt+DioGgCetjB1qPp7LtG35fOjk2MpbONiY2p3PxYLb3NVj0/B83c0Eg6+0Y6KTVb+TbMzNfT2dOj4+nsZSPn09nxWiOdrXfhL1gj8q/X+WaJ12su/3qdms1nXz+Xz05P5z8PcSr/fhw5lX/NRk7lcmMn8/t27Cdn09nWi0fT2TLHJQBYLyiaAAAAAFTD8DwAAAAAWB4TQQAAAADAckJMOQ4AAAAAnQxKT1P+LFUAAAAAGED0NAEAAACoZkB6miiaAAAAAJRmDc7wPIomAAAAAOVFDMxEEJzTBAAAAAAd0NMEAAAAoBKG5wHoK9+afTid3T314XR2fONoKtcczeUkKWplOrnz2bnWSDp7upHf7uxc/lB5eiz/OmwYmUhnx+uNdLbmVjqb1Yz863W+UU9nz82VyM7k9+/s2XzWZ/L7d+RU/nUYPel0dvy13D4bPz6X3qZf+kk6+1iJ4wcA/AyKJgAAAABYHj1NAAAAALCckNQajKqJiSAAAAAAoAN6mgAAAABUMxgdTRRNAAAAAKrhnCYAAAAA6GRALm5L0QQAAACgkkHpaWIiCAAAAADogJ4mAAAAAOWFmAgCAAAAAJZjSeacJgDrVfO1k+ls/dibUrkN9S0lWlBPJ93MjyIeatTS2bnZ/HZnZ/LbnR0bSWdPjzbT2eHhfHZoaO3/gLVaTmfn5/OvV3M2n1WJ/VCbzmdHTud/t5FT6ajGX2ulsxt+0kjl6sfyDSjzOQeAyvKHur7GOU0AAAAA0MGqeppsvyjpjKSmpPmI2LUWjQIAAADQ+xiel/ebEfHqGmwHAAAAQL9gIggAAAAA6CQG5uK2qz2nKSR9y/aTtvcuFbC91/Yh24dOnDixyh8HAAAAoFc4qi/9ZLVF09sj4q2SbpF0l+1/tjgQEQ9ExK6I2DU1NbXKHwcAAAAAF9eqiqaIeLn497ikr0u6cS0aBQAAAKAPRFRf+kjlosn2hO2NF25LepekZ9aqYQAAAAB6WEhuVV/6yWomgrhK0tdtX9jOf42Iv1iTVgEAAADofX3WY1RV5aIpIl6Q9Btr2BYAAAAA6DlMOQ4MoIOtR9PZd43ckcqNlvj5Q41N6WxtdiSdnZnJjziunXc6O382f6icH6uls62x/Ldzc/V8NobW/ls/t/Kvlxv57PBMl7LT6ahG38i/XmMn8+NJxo/PpbP1Y6dSudaLL6W3WeZzDgCVDUZHE0UTAAAAgGrM8DwAAAAA6ICiCQAAAACWEZL6bBa8qlZ7cVsAAAAAWNfoaQIAAABQmhWc0wQAAAAAHVE0AQAAAEAHA1I0cU4TAAAAgPIuTARRdUmwvdv2c7aP2L5nicdt+/PF40/bfutKz7X9Sds/tv1Usdy6UjsomgAAAAD0HNs1SfdKukXSDkl32N6xKHaLpO3FslfSfcnnfi4idhbLgZXawvA8AB19a+7LqdzNQ7elt1l/fUs6O3zm6vx2z07ks9O1dLYx6Xx2Ip9tjuazrRJH68j/amlu5rND8/lsbTafrZ/NDwGpT+ezY6/nf7mxn5xNZ/3ST9LZ5msnU7mDrUfT2wSAi6HLE0HcKOlIRLwgSbYfkbRH0t+2ZfZI+lJEhKTv2d5ke6uk6xPPTaOnCQAAAEA1EdUX6Qrbh9qWvYu2vk3SS233jxbrMpmVnnt3MZxvn+3NK/2a9DQBAAAAqCBWOxHEqxGxq8PjSw3JWPwDl8t0eu59kj5V3P+UpM9I+t1ODaVoAgAAANCLjkq6tu3+NZJeTmZGlntuRLxyYaXtL0j65koNYXgeAAAAgPJCqx2et5InJG23fYPtEUm3S9q/KLNf0geKWfRukvRGRBzr9NzinKcL3ivpmZUaQk8TAAAAgGqSU4dXERHztu+W9JikmqR9EfGs7Y8Uj98v6YCkWyUdkXRO0gc7PbfY9B/b3qmFsu9FSR9eqS0UTQAAAAAq6fLseSqmAz+waN39bbdD0l3Z5xbr31+2HRRNAAAAAKrpctHUKzinCQAAAAA6oKcJAAAAQHkhqTUYPU0UTQAAAAAqWPV1mvoGRROANXGw9WhXtvuu0fels+Nnr0lnRy6fTGcbk/V8dmP+sDo/vtR195bWrOezUUtH09zMZ2uN/B/Q4fP5bP3MfD473Uhna69Np7OtF4+ms4/NPpzOAkDfomgCAAAAgA4GpGhiIggAAAAA6ICeJgAAAADlMREEAAAAAHQSUrQudSMuCoomAAAAANVwThMAAAAAgJ4mAAAAAOVxThMAAAAArGBAhudRNAEAAACohqIJAAAAAJYTFE0A0Au+NftwOnvz0G3p7NDLE+ns2IYN+ezU5nS2OTmazrZGaumsas5ns5r5P4pDc810tjY9m2/DidfT0Th3Lp1tnj2bzh5sPZrOAgDWD4omAAAAAOWFpBbXaQIAAACA5TE8DwAAAAA6oGgCAAAAgOXEwFynaehSNwAAAAAAehk9TQAAAADKCymCiSAAAAAAYHkDMjyPogkAAABANQMyEQTnNAEAAABAB/Q0AQAAACgvgovbAkC/Odh69FI3QTcP3ZbODm3YkM/Waums7XQ2K8oMv2g289Fz59LZXti/AIBFBmR4HkUTAAAAgEpiQHqaVnVOk+3dtp+zfcT2PWvVKAAAAAC9LhZ6mqoufaRy0WS7JuleSbdI2iHpDts71qphAAAAANALVjM870ZJRyLiBUmy/YikPZL+di0aBgAAAKCHhQbmOk2rGZ63TdJLbfePFut+hu29tg/ZPnTixIlV/DgAAAAAPSVa1Zc+spqiaanpmX6u1IyIByJiV0TsmpqaWsWPAwAAANArQlK0ovLST1YzPO+opGvb7l8j6eXVNQcAAABAX4joux6jqlbT0/SEpO22b7A9Iul2SfvXplkAAAAA0Bsq9zRFxLztuyU9JqkmaV9EPLtmLQMAAADQ0/ptmF1Vq7q4bUQckHRgjdoCAAAAoJ8MyPA8x0W8sJTtE5L+/qL9wMF2haRXL3UjkMb+6j/ss/7DPus/7LP+wv5anX8UEX01a5rtv9DCfq/q1YjYvVbt6aaLWjTh4rF9KCJ2Xep2IIf91X/YZ/2HfdZ/2Gf9hf2F9Ww1E0EAAAAAwLpH0QQAAAAAHVA0rV8PXOoGoBT2V/9hn/Uf9ln/YZ/1F/YX1i3OaQIAAACADuhpAgAAAIAOKJoAAAAAoAOKpnXE9m22n7Xdsr1r0WO/b/uI7edsv/tStRE/z/buYr8csX3PpW4Pfp7tfbaP236mbd0W2wdtP1/8u/lSthE/Zfta29+2fbg4Jn60WM8+61G2x2z/te2/KfbZHxXr2Wc9zHbN9v+2/c3iPvsL6xZF0/ryjKR/Kek77Stt75B0u6Rfk7Rb0n+2Xbv4zcNixX64V9ItknZIuqPYX+gtX9TCZ6fdPZIej4jtkh4v7qM3zEv6eET8qqSbJN1VfK7YZ71rVtJvRcRvSNopabftm8Q+63UflXS47T77C+sWRdM6EhGHI+K5JR7aI+mRiJiNiP8n6YikGy9u67CMGyUdiYgXImJO0iNa2F/oIRHxHUknF63eI+mh4vZDkt5zMduE5UXEsYj4QXH7jBb+U7dN7LOeFQumi7v1Ygmxz3qW7Wsk/XNJf962mv2FdYuiaTBsk/RS2/2jxTpceuyb/nVVRByTFv6TLunKS9weLMH29ZLeIun7Yp/1tGKo11OSjks6GBHss972p5L+vaRW2zr2F9at4UvdAJRj+39IunqJh/4gIr6x3NOWWMdc872BfQN0ie1JSV+V9LGIOG0v9XFDr4iIpqSdtjdJ+rrtX7/ETcIybP+OpOMR8aTtd1zi5gAXBUVTn4mI367wtKOSrm27f42kl9emRVgl9k3/esX21og4ZnurFr4dR4+wXddCwfRwRHytWM0+6wMRccr2X2rhPEL2WW96u6R/YftWSWOSLrP9X8T+wjrG8LzBsF/S7bZHbd8gabukv77EbcKCJyRtt32D7REtTNix/xK3CTn7Jd1Z3L5T0nI9vbjIvNCl9KCkwxHx2baH2Gc9yvZU0cMk2+OSflvS34l91pMi4vcj4pqIuF4Lf7f+Z0T8a7G/sI45gpFA64Xt90r6T5KmJJ2S9FREvLt47A8k/a4WZpX6WET890vVTvys4pu6P5VUk7QvIv7jpW0RFrP9ZUnvkHSFpFck/aGk/ybpK5Kuk/QjSbdFxOLJInAJ2P6nkv6XpB/qp+dbfEIL5zWxz3qQ7X+shYkDalr4QvcrEfEfbF8u9llPK4bn/buI+B32F9YziiYAAAAA6IDheQAAAADQAUUTAAAAAHRA0QQAAAAAHVA0AQAAAEAHFE0AAAAA0AFFEwAAAAB0QNEEAAAAAB38f/SChoDhQ3vWAAAAAElFTkSuQmCC)
......@@ -286,11 +286,11 @@
ps.plot.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7faa5077ed30>
<matplotlib.colorbar.Colorbar at 0x7fd9a40bd5e0>
%%%% Output: display_data
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)
......@@ -349,11 +349,11 @@
ps.plot.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7faa506476a0>
<matplotlib.colorbar.Colorbar at 0x7fd99ddf0e20>
%%%% Output: display_data
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)
......
lbmpy_tests/test_compiled_in_boundaries.ipynb
View file @ b556372a
......@@ -48,11 +48,11 @@
update_rule_aa_odd = update_rule_with_push_boundaries(cr_odd, pdfs, boundaries, streaming_pattern, Timestep.ODD)
getter_assignments = macroscopic_values_getter(update_rule_aa_even.method, velocity=u.center_vector,
pdfs=pdfs, density=None,
streaming_pattern=streaming_pattern,
timestep=Timestep.EVEN)
previous_timestep=Timestep.EVEN)
getter_kernel = ps.create_kernel(getter_assignments, target=dh.default_target).compile()
even_kernel = ps.create_kernel(update_rule_aa_even, target=dh.default_target, ghost_layers=1).compile()
odd_kernel = ps.create_kernel(update_rule_aa_odd, target=dh.default_target, ghost_layers=1).compile()
```
......@@ -83,11 +83,11 @@
plt.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7f7c316778e0>
<matplotlib.colorbar.Colorbar at 0x7f0cfc8f5b50>
%%%% Output: display_data
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)
......@@ -106,10 +106,10 @@
plt.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7f7c30fd9d60>
<matplotlib.colorbar.Colorbar at 0x7f0cfca3ceb0>
%%%% Output: display_data
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)
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