diff --git a/lbmpy/macroscopic_value_kernels.py b/lbmpy/macroscopic_value_kernels.py
index 3f7a26033b83304917b3c26d06d816b6c0fce15d..4ec4f31ae4e44f0468ec58a191ea9843c8072a97 100644
--- a/lbmpy/macroscopic_value_kernels.py
+++ b/lbmpy/macroscopic_value_kernels.py
@@ -8,12 +8,16 @@ from lbmpy.advanced_streaming.utility import get_accessor, Timestep
 
 
 def pdf_initialization_assignments(lb_method, density, velocity, pdfs,
-                                   streaming_pattern='pull', previous_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):
-        previous_step_accessor = get_accessor(streaming_pattern, previous_timestep)
-        field_accesses = previous_step_accessor.write(pdfs, lb_method.stencil)
-    elif streaming_pattern == 'pull':
+        accessor = get_accessor(streaming_pattern, previous_timestep)
+        if set_pre_collision_pdfs:
+            field_accesses = accessor.read(pdfs, lb_method.stencil)
+        else:
+            field_accesses = accessor.write(pdfs, lb_method.stencil)
+    elif streaming_pattern == 'pull' and not set_pre_collision_pdfs:
         field_accesses = pdfs
     else:
         raise ValueError("Invalid value of pdfs: A PDF field reference is required to derive "
@@ -28,11 +32,15 @@ def pdf_initialization_assignments(lb_method, density, velocity, pdfs,
 
 
 def macroscopic_values_getter(lb_method, density, velocity, pdfs,
-                              streaming_pattern='pull', previous_timestep=Timestep.BOTH):
+                              streaming_pattern='pull', previous_timestep=Timestep.BOTH,
+                              use_pre_collision_pdfs=False):
     if isinstance(pdfs, Field):
-        previous_step_accessor = get_accessor(streaming_pattern, previous_timestep)
-        field_accesses = previous_step_accessor.write(pdfs, lb_method.stencil)
-    elif streaming_pattern == 'pull':
+        accessor = get_accessor(streaming_pattern, previous_timestep)
+        if use_pre_collision_pdfs:
+            field_accesses = accessor.read(pdfs, lb_method.stencil)
+        else:
+            field_accesses = accessor.write(pdfs, lb_method.stencil)
+    elif streaming_pattern == 'pull' and not use_pre_collision_pdfs:
         field_accesses = pdfs
     else:
         raise ValueError("Invalid value of pdfs: A PDF field reference is required to derive "
diff --git a/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb b/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
index cddc784870ea4b92f4fdf9b1b4abdbe3e515c0a2..ef7082dc03ffeaa2c1bec67a876345285218bc0d 100644
--- a/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
+++ b/lbmpy_tests/centeredcumulant/test_periodic_pipe_flow.ipynb
@@ -260,7 +260,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x122dae4f0>"
+       "<matplotlib.colorbar.Colorbar at 0x7f7e7bd42370>"
       ]
      },
      "execution_count": 7,
@@ -269,7 +269,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -316,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -352,7 +352,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -371,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -440,7 +440,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +484,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/lbmpy_tests/test_compiled_in_boundaries.ipynb b/lbmpy_tests/test_compiled_in_boundaries.ipynb
index 1a0adbd9a18406864f9aadd3583126f4554cad7f..559ad60b015c1823c88fbcc5b87e36535d751d5a 100644
--- a/lbmpy_tests/test_compiled_in_boundaries.ipynb
+++ b/lbmpy_tests/test_compiled_in_boundaries.ipynb
@@ -107,7 +107,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x11d5a7820>"
+       "<matplotlib.colorbar.Colorbar at 0x7fddd3f7a880>"
       ]
      },
      "execution_count": 6,
@@ -116,7 +116,7 @@
     },
     {
      "data": {
-      "image/png": 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VROxVOzHqXHdPx+8h6Y45tnta0jXzlPmKpI+WaQfD8wAAAACgC3qaAAAAAJR29ua2dUDSBAAAAKCS5IQOY4+kCQAAAEB5MdiJIEZJPVJDAAAAAKiIniYAAAAApYUGP3veqCBpAgAAAFBJXYbnkTQBAAAAKI3Z8wAMTOutt1NxE+euyhW4Ynm67sZb76biopH8ApyYyMU1m7k4SW62koHJNkbk4lrJelvJ8gYh+1qy780w684eYzMljp3sPkwet6Fc3T4zkytvWfLzIsnJWL+Tqzt9fJc5dpL7Jt5+J18mgLFTl6SJiSAAAAAAoAt6mgAAAACUFqrPlOMkTQAAAAAqYfY8AAAAAJhP1OeaJpImAAAAAKXVafY8JoIAAAAAgC7oaQIAAABQSV16mkiaAAAAAJTG7HkAAAAA0EOQNAEYpjh1KhXnRolLExu5LzY3k+XNZANLiOhvea1kef2ut4xh1p2VbWM6bgB/ZFvZwNxx61ayjU5+rsp8XrKx/d4vJcS7ue+oaKZ3DACMLJImAAAAAJVwnyYAAAAAmEdwnyYAAAAA6I5rmgAAAABgXvWZPY+b2wIAAABAF/Q0AQAAAKiE4XkAAAAAMI8QE0EAAAAAwPxiPG412A8kTQAAAAAq4T5NAAajkftyiVOn+161zzknGZj8Amw2c3ETE7m4MnW3Wvkyh6XfbWwk5+7J7pdB1D2s11zGTLKN2WNxIvvelDgdmz11m21jttp3T+WDTyVjk995ADDKSJoAAAAAlBaqz0QQPU+P2b7M9ndtH7F92PZnivUX2d5n+7ni54WDby4AAACA0dC+T1PVZZxkxhTMSPpcRPyqpOsk3WH7Kkk7JO2PiPWS9hePAQAAANRERPVlnPRMmiLiWET8qPj9TUlHJK2TtFXS7iJst6QbB9RGAAAAADVke7PtZ21P2f65Thq33VU8/7Tta4v1c46WK577gu2XbD9VLDf0akepa5psXyHpGklPSLokIo5J7cTK9sVlygIAAAAw3gZ5TZPtCUl3S7pe0rSkA7b3RMRPOsK2SFpfLB+UtLP4eXa03I9sXyDpSdv7Ora9MyK+km1Leloi2+dL+rakz0bEGyW222b7oO2DJ0+ezG4GAAAAYIS1h9m58pKwSdJURDwfEaclPaD2aLdOWyXdH22PS1pte22X0XKVpJIm28vVTpi+GRHfKVYft722eH6tpBNzbRsR90bExojYODk5WbWdAAAAAEbMgCeCWCfpxY7H0/r5xKdnzKzRcmdtL4bz7cpMaJeZPc+Svi7pSER8reOpPZJuK36/TdLDvcoCAAAAsHQscCKINWdHpBXLtlnFz5VZzZ5ComvMPKPldkp6n6QNko5J+mqv15m5pulDkm6V9GPbTxXrfl/SlyQ9aPtTkn4q6eOJsgAAAABAkl6OiI1dnp+WdFnH40slHc3GzDNaThFx/Ozvtu+T9EivhvZMmiLi+5o7g5Okj/baHgAAAMDSNOCb2x6QtN72lZJeknSzpN+aFbNH7aF2D6g9AcTrxSR1842W09lrnoqHN0k61KshpWbPA9AHzWYurtVKlpeMkxSnz+QCG7kvwPb3UUL2NQ/TONwwYpjv47DqLlNv9nhsJOdAyh4TM33+TA9AZOtu5d/vyO6bcfj8A6gklJ7QoVr5ETO2t0t6VNKEpF0Rcdj27cXz90jaK+kGSVOS3pH0yWLzOUfLRcReSV+2vUHtYXwvSPp0r7aQNAEAAACoZNCnHIskZ++sdfd0/B6S7phju3lHy0XErWXbQdIEAAAAoLwY+PC8kZG+TxMAAAAA1BE9TQAAAACqGYNLgvuBpAkAAABAJXUZnkfSBAAAAKCScZh8th+4pgkAAAAAuqCnCQAAAEBpIYbnAQAAAMD8QhJJE4BBiGYzFedknKKVr3xmJhc3kRu5GxMTufIGMODZ7u+XdNRlUPYSlj4iWiU+MxlDPHYi+5nOtrFV4rUky8x+5wEYT3X580nSBAAAAKCamiRNTAQBAAAAAF3Q0wQAAACgAjMRBAAAAAB0VZPheSRNAAAAAMqL+kw5zjVNAAAAANAFPU0AAAAAqmF4HgAAAAB0U4/heSRNAAAAAKqhpwnAMEWzlQvMxknysuRljNnbezebyYpLXD7ZyJ2ximHdgrxVot5I7psy7w/mFdm3cRxuX5/9bGVfS/K4jZmZXHmSIttGAEvbGHyl9gN/qQEAAACgC3qaAAAAAJQXkmoy5ThJEwAAAIBKxmHEcz+QNAEAAACohqQJAAAAALqoyfA8JoIAAAAAgC7oaQIAAABQiRmeBwAAAADzCHFNEwAAAADMz7W5pomkCVhs7vOlhM1mPnYiWXcrGZd+Ka1sYL7ufosSbUyXmTz9Fsl96OQfpkHM/5qte5iGdexklTnG+r0Ps3WX+T5pJdvY7+88ABgCkiYAAAAA1TA8DwAAAAC6IGkCAAAAgC5ImgAAAABgHqHaTATB1ZkAAAAARpLtzbaftT1le8ccz9v2XcXzT9u+tlh/me3v2j5i+7Dtz3Rsc5HtfbafK35e2KsdJE0AAAAAKnFUX3qWbU9IulvSFklXSbrF9lWzwrZIWl8s2yTtLNbPSPpcRPyqpOsk3dGx7Q5J+yNivaT9xeOuSJoAAAAAVBMLWHrbJGkqIp6PiNOSHpC0dVbMVkn3R9vjklbbXhsRxyLiR5IUEW9KOiJpXcc2u4vfd0u6sVdDSJoAAAAADMMa2wc7lm2znl8n6cWOx9P6+8QnHWP7CknXSHqiWHVJRByTpOLnxb0aykQQAAAAACrJDLPr4uWI2Nit+DnWza6xa4zt8yV9W9JnI+KN8k1so6cJAAAAwCialnRZx+NLJR3NxthernbC9M2I+E5HzHHba4uYtZJO9GoIPU3AqGr0fwrPaLZScZ5IFthKnncpc3ommrk49/n9iSHeaKKVrXuYN8NI1j2A4zYvd3wPzSCOsWby85I+xkrI7utkEwGMqcFOOX5A0nrbV0p6SdLNkn5rVsweSdttPyDpg5Jej4hjti3p65KORMTX5tjmNklfKn4+3KshPf+Vsb3L9gnbhzrWfcH2S7afKpYbepUDAAAAYAlZyCQQiXM5ETEjabukR9WeyOHBiDhs+3bbtxdheyU9L2lK0n2SfqdY/yFJt0r6yBw5y5ckXW/7OUnXF4+7yvQ0fUPSf5F0/6z1d0bEVxLbAwAAAFiKBjwIIiL2qp0Yda67p+P3kHTHHNt9X3Nf76SIeEXSR8u0o2dPU0Q8JunVMoUCAAAAwFKxkIkgthd33d2VuYsuAAAAgKVlkDe3HSVVk6adkt4naYOkY5K+Ol+g7W1n514/efJkxeoAAAAAjJzB3tx2ZFRKmiLieEQ0I6Kl9gVXm7rE3hsRGyNi4+TkZNV2AgAAABg1JE3zOzuveeEmSYfmiwUAAACw9CxkaN64Dc/rOXue7W9J+rCkNbanJX1e0odtb1A7R3xB0qcH10QAAAAAGJ6eSVNE3DLH6q8PoC0AAAAAxslgb247MjL3aQJQN61kn3mjlSyvxEjgRvLLN4bUr599bwYhku/3MJXZ1/02xKpTBnHsJMuMYX1eACx9Nfl6IWkCAAAAUMm4XZtUFUkTAAAAgGpqkjSN+mAGAAAAABgqepoAAAAAlDeGU4dXRdIEAAAAoBqSJgAAAADooiZJE9c0AQAAAEAX9DQBAAAAqKQu1zTR0wQAAAAAXdDTBIy7VisfOzHR57qTp5caJdrYWkLncqLE6+6n7H4po+FcXL9fs0scD4N43f00rOOhjDLfJwAg1eaaJpImAAAAAOXVaMrxJXRKFwAAAAD6j54mAAAAANXUpKeJpAkAAABANSRNAAAAADA3qz7XNJE0AQAAAKimJkkTE0EAAAAAQBf0NAEAAAAor0ZTjpM0AQAAAKiGpAnAktNqpcKi0d+Ru85V29YoE7xEtMbgL06/29hwLi5KHA/u84jzMnVnDGA/RyTLTH72AaC0MfgT1g9c0wQAAAAAXdDTBAAAAKASrmkCAAAAgG5ImgAAAABgHqHaJE1c0wQAAACgEkf1JVW+vdn2s7anbO+Y43nbvqt4/mnb13Y8t8v2CduHZm3zBdsv2X6qWG7o1Q6SJgAAAAAjx/aEpLslbZF0laRbbF81K2yLpPXFsk3Szo7nviFp8zzF3xkRG4plb6+2kDQBAAAAqCYWsPS2SdJURDwfEaclPSBp66yYrZLuj7bHJa22vVaSIuIxSa8u5OWdRdIEAAAAoJIFDs9bY/tgx7JtVvHrJL3Y8Xi6WFc2Zi7bi+F8u2xf2CuYpAkAAABANQvraXo5IjZ2LPfOKn2uO6HP7qPKxMy2U9L7JG2QdEzSV3vEM3seUCuRvOqy1crFNXLnXSJbryQnq05rzPVdOp7KvI/DYg/x/Y5+HzxJrdx+Gcj+y35Wx+DYATCGBj973rSkyzoeXyrpaIWYfyAijp/93fZ9kh7p1RB6mgAAAACMogOS1tu+0vYKSTdL2jMrZo+kTxSz6F0n6fWIONat0LPXPBVuknRovtiz6GkCAAAAUJo199i4fomIGdvbJT0qaULSrog4bPv24vl7JO2VdIOkKUnvSPrk37XP/pakD6t97dS0pM9HxNclfdn2BrX7yV6Q9OlebSFpAgAAAFDNgEf/FtOB75217p6O30PSHfNse8s8628t2w6SJgAAAACVZG9SO+64pgkAAAAAuqCnCQAAAEA1NelpImkCAAAAUA1JEwAAAADMI+pzTRNJEwAAAIBqapI0MREEAAAAAHRBTxMwqlq5UzdR4tSHW61cYCNZaLOZrDh/67tI1u1smcn3UY0B3J4vW3cdDXO/ZGU/gzGA/Zz9rGbrTpZX6rVwfANQfYbn9fzvxPYu2ydsH+pYd5HtfbafK35eONhmAgAAABg5sYBljGRO6X5D0uZZ63ZI2h8R6yXtLx4DAAAAqBFH9WWc9EyaIuIxSa/OWr1V0u7i992SbuxvswAAAABgNFS9pumSiDgmSRFxzPbFfWwTAAAAgFE3hsPsqhr47Hm2t9k+aPvgyZMnB10dAAAAgMXCNU1dHbe9VpKKnyfmC4yIeyNiY0RsnJycrFgdAAAAgFFicU1TL3sk3Vb8fpukh/vTHAAAAABjg56mNtvfkvQDSe+3PW37U5K+JOl6289Jur54DAAAAABLTs+JICLilnme+mif2wIAAABgjHgQN/geQVVnzwMwKlr5L6tIDsh1q5ULbAxgLplk3ZGs23ay3uF96Ue//+Bk918Zyfc7+1rS+2UQkvt6qPslW3f285Iurx7//ADokzEcZlcVSRMAAACASsZtQoeqSJoAAAAAVFOTpGng92kCAAAAgHFGTxMAAACAShieBwAAAADdkDQBAAAAwDyiPj1NXNMEAAAAAF3Q0wQAAACgmpr0NJE0AQAAACjNqs/wPJImYLFFKxnnXJxLjLJt5b7ZIlmkW8nX0ijRRidfN+ohecwOVfZzECVeS7LMyJY5iPcx/V2WjAMwnsp8t40xkiYAAAAAldSlp4mJIAAAAACgC3qaAAAAAJQXYiIIAAAAAOjGNblskeF5AAAAAKqJBSwJtjfbftb2lO0dczxv23cVzz9t+9qO53bZPmH70KxtLrK9z/Zzxc8Le7WDpAkAAABAJY7qS8+y7QlJd0vaIukqSbfYvmpW2BZJ64tlm6SdHc99Q9LmOYreIWl/RKyXtL943BVJEwAAAIBRtEnSVEQ8HxGnJT0gaeusmK2S7o+2xyWttr1WkiLiMUmvzlHuVkm7i993S7qxV0NImgAAAACUF2rfp6nq0ts6SS92PJ4u1pWNme2SiDgmScXPi3s1hIkgAAAAAFSywPs0rbF9sOPxvRFxb2fxc2wzu8ZMzIKRNAHjLkpMW+P+di5H8i7gbpVoYyPZxmSZkSzPnus7F0tR9rjNHmPJs6X58lSijf1W5vsEAKSFpicvR8TGLs9PS7qs4/Glko5WiJntuO21EXGsGMp3oldDGZ4HAAAAYBQdkLTe9pW2V0i6WdKeWTF7JH2imEXvOkmvnx1618UeSbcVv98m6eFeDSFpAgAAAFCaNdjZ8yJiRtJ2SY9KOiLpwYg4bPt227cXYXslPS9pStJ9kn7n79pnf0vSDyS93/a07U8VT31J0vW2n5N0ffG4K4bnAQAAACgvP6HDAqqIvWonRp3r7un4PSTdMc+2t8yz/hVJHy3TDpImAAAAAJUscCKIsUHSBAAAAKCamiRNXNMEAAAAAF3Q0wQAAACgEobnAQAAAMB8QlKrHlkTSRMAAACAauqRM5E0AaMqBnDmxo1WLrCVvNyx4eqNGTGRnDLVHoPX3OBy1drIfk9E8rNfwiC+owCMn7oMz+MvKwAAAAB0QU8TAAAAgGoGfHPbUUHSBAAAAKCSugzPI2kCAAAAUF6oNhNBcE0TAAAAAHRBTxMAAACA0izJXNMEAAAAAF30/44GI4mkCQAAAEAl9DQBAAAAwHyYCAIAAAAAINHTBGAhWrnTS1Hi9IxbycHRjWShfS4vSgxDsJ2OxdzKvN9p2WMiW3eyvFKvJfnZ6rcYUr0AxlVwc9sM2y9IelNSU9JMRGzsR6MAAAAAjD5ubpv3axHxch/KAQAAADBO6GkCAAAAgHmE5JpMOb7QiSBC0l/YftL2tn40CAAAAABGyUJ7mj4UEUdtXyxpn+1nIuKxzoAimdomSZdffvkCqwMAAAAwMmoyPG9BPU0RcbT4eULSQ5I2zRFzb0RsjIiNk5OTC6kOAAAAwCiJBSxjpHLSZPs82xec/V3Sr0s61K+GAQAAABhtjqi8jJOFDM+7RNJDxX1Ilkn604j48760CgAAAABGROWkKSKel/SBPrYFAAAAwDgZsx6jqphyHFhs6S+X5Byezo+yjVaubjf6X/fIayVfc6PE+53c10WPPVBe5I7b7Gd/EHXX5R8qoJZC6X9Xxh1JEwAAAIDSrPG7NqkqkiYAAAAA1dQkaVpCY2sAAAAAoP/oaQIAAABQTU16mkiaAAAAAJRXo4kgGJ4HAAAAoJJB39zW9mbbz9qesr1jjudt+67i+adtX9trW9tfsP2S7aeK5YZe7aCnCQAAAEA1AxyeZ3tC0t2Srpc0LemA7T0R8ZOOsC2S1hfLByXtlPTBxLZ3RsRXsm2hpwkAAADAKNokaSoino+I05IekLR1VsxWSfdH2+OSVttem9w2jaQJAAAAQAXR7mmqukhrbB/sWLbNqmCdpBc7Hk8X6zIxvbbdXgzn22X7wl6vlOF5wLiLEldgegmdJ2klX3cj+Zrt6m1BaTGI4RzZY6ImMz1VUub7BABCC/1OfTkiNnZ5fq4/zrMrnC+m27Y7Jf1h8fgPJX1V0m93ayhJEwAAAIBqBnuuZVrSZR2PL5V0NBmzYr5tI+L42ZW275P0SK+GLKHTzgAAAACWkAOS1tu+0vYKSTdL2jMrZo+kTxSz6F0n6fWIONZt2+Kap7NuknSoV0PoaQIAAABQSXbq8CoiYsb2dkmPSpqQtCsiDtu+vXj+Hkl7Jd0gaUrSO5I+2W3bougv296g9vC8FyR9uldbSJoAAAAAVDPg60QjYq/aiVHnuns6fg9Jd2S3LdbfWrYdJE0AAAAAygtJrXpMrkPSBAAAAKCCqM2MpEwEAQAAAABd0NMEAAAAoJqa9DSRNAEAAACohqQJwFBlv4Q81w2vF1h18qJON5J3tGvlRwJHnwcNu5VsYyNZcba8MmX2W6P/x8TQLvQt835nPzPJMqPf/wiUeQ8j2cZhXoBdk3+UAHTBRBAAAAAA0E2kT/KMOyaCAAAAAIAu6GkCAAAAUE1NhuqSNAEAAAAoj2uaAAAAAKCHmvQ0cU0TAAAAAHRBTxMAAACAamrS00TSBAAAAKCCIGkCAAAAgHmFyt2EfIyRNAEAAACohp4mAGOh1JdVf88GRSs7l0wzX2gy1A2n4tLvjpOvJVmvJNl9bmMj2cZkvaX0+49i8sxklKk3O+1tn+9eH8OcbrfPr6Uu//wAQFkkTQAAAACqqcnJFpImAAAAABUEN7cFAAAAgHmFFP0eJjyiuLktAAAAAHRBTxMAAACAahieBwAAAABdMBEEAAAAAMwjgpvbAgAAAEBXNelpYiIIAAAAAOiCniagTvp9Niia/S2vTNXDGg1gp0P7fu7NY3Cea5hTz9bkbCcAjJKoyfC8Bf0Ftr3Z9rO2p2zv6FejAAAAAIy6aJ+wqrqMkco9TbYnJN0t6XpJ05IO2N4TET/pV+MAAAAAjKgQU44nbJI0FRHPS5LtByRtlUTSBAAAANTBMIdlL6KFDM9bJ+nFjsfTxToAAAAAWDIW0tM019XQP9c/Z3ubpG2SdPnlly+gOgAAAACjIiRFTYbnLaSnaVrSZR2PL5V0dHZQRNwbERsjYuPk5OQCqgMAAAAwMiLaw/OqLmNkIT1NByStt32lpJck3Szpt/rSKgAAAAAjry49TZWTpoiYsb1d0qOSJiTtiojDfWsZAAAAAIyABd3cNiL2Strbp7YAAAAAGCdjNsyuKsci3ljK9klJf7NoFWIuayS9POxG4OewX0YT+2U0sV9GE/tlNLFfRtNc++WXI2KsJgCw/edqv5aqXo6Izf1qzyAtatKE4bN9MCI2Drsd+IfYL6OJ/TKa2C+jif0ymtgvo4n9Mn4WMnseAAAAACx5JE0AAAAA0AVJU/3cO+wGYE7sl9HEfhlN7JfRxH4ZTeyX0cR+GTNc0wQAAAAAXdDTBAAAAABdkDTVhO2P2z5su2V746znfs/2lO1nbf/GsNpYV7Y3F+/9lO0dw25PXdneZfuE7UMd6y6yvc/2c8XPC4fZxrqxfZnt79o+Unx/faZYz34ZItsrbf/Q9l8V++WLxXr2ywiwPWH7/9h+pHjMfhky2y/Y/rHtp2wfLNaxX8YMSVN9HJL0ryQ91rnS9lWSbpb0jyRtlvRfbU8sfvPqqXiv75a0RdJVkm4p9gkW3zfU/gx02iFpf0Ssl7S/eIzFMyPpcxHxq5Kuk3RH8flgvwzXKUkfiYgPSNogabPt68R+GRWfkXSk4zH7ZTT8WkRs6JhmnP0yZkiaaiIijkTEs3M8tVXSAxFxKiL+WtKUpE2L27pa2yRpKiKej4jTkh5Qe59gkUXEY5JenbV6q6Tdxe+7Jd24mG2qu4g4FhE/Kn5/U+1/BNeJ/TJU0fZW8XB5sYTYL0Nn+1JJ/1zSf+tYzX4ZTeyXMUPShHWSXux4PF2sw+Lg/R9tl0TEMan9D7yki4fcntqyfYWkayQ9IfbL0BVDwJ6SdELSvohgv4yGP5b0HyS1OtaxX4YvJP2F7SdtbyvWsV/GzLJhNwD9Y/svJf3iHE/9QUQ8PN9mc6xjSsXFw/sP9GD7fEnflvTZiHjDnutjg8UUEU1JG2yvlvSQ7auH3KTas/2bkk5ExJO2Pzzk5uAf+lBEHLV9saR9tp8ZdoNQHknTEhIRH6uw2bSkyzoeXyrpaH9ahATe/9F23PbaiDhme63aZ9WxiGwvVzth+mZEfKdYzX4ZERHxmu3vqX09IPtluD4k6V/avkHSSknvsf0nYr8MXUQcLX6esP2Q2kPz2S9jhuF52CPpZtvn2L5S0npJPxxym+rkgKT1tq+0vULtSTn2DLlN+Ht7JN1W/H6bpPl6bDEAbncpfV3SkYj4WsdT7Jchsj1Z9DDJ9ipJH5P0jNgvQxURvxcRl0bEFWr/LflfEfFvxH4ZKtvn2b7g7O+Sfl3tybnYL2OGm9vWhO2bJP1nSZOSXpP0VET8RvHcH0j6bbVnqvpsRPzZsNpZR8VZwT+WNCFpV0T80XBbVE+2vyXpw5LWSDou6fOS/qekByVdLumnkj4eEbMni8CA2P6nkv63pB/r76/R+H21r2tivwyJ7X+s9oXrE2qffH0wIv6j7V8Q+2UkFMPz/l1E/Cb7Zbhs/4qkh4qHyyT9aUT8Eftl/JA0AQAAAEAXDM8DAAAAgC5ImgAAAACgC5ImAAAAAOiCpAkAAAAAuiBpAgAAAIAuSJoAAAAAoAuSJgAAAADogqQJAAAAALr4/1QOCksD8jK6AAAAAElFTkSuQmCC\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -150,7 +150,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x11dcca970>"
+       "<matplotlib.colorbar.Colorbar at 0x7fddd28835e0>"
       ]
      },
      "execution_count": 7,
@@ -159,7 +159,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1152x432 with 2 Axes>"
       ]
@@ -205,7 +205,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,
diff --git a/lbmpy_tests/test_force.py b/lbmpy_tests/test_force.py
index 4215e957b8ee1948d52383e1bbbdf59ce01b933c..cc6b664de4393af61687e1cb9a3989a69a8ad4ec 100644
--- a/lbmpy_tests/test_force.py
+++ b/lbmpy_tests/test_force.py
@@ -7,7 +7,7 @@ from pystencils import Target
 
 from lbmpy.creationfunctions import create_lb_method, create_lb_update_rule, LBMConfig, LBMOptimisation
 from lbmpy.enums import Stencil, Method, ForceModel
-from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
+from lbmpy.macroscopic_value_kernels import macroscopic_values_setter, macroscopic_values_getter
 from lbmpy.moments import is_bulk_moment
 from lbmpy.stencils import LBStencil
 from lbmpy.updatekernels import create_stream_pull_with_output_kernel
@@ -37,17 +37,13 @@ def test_total_momentum(method_enum, force_model, omega):
     u = dh.add_array('u', values_per_cell=stencil.D)
 
     lbm_config = LBMConfig(method=method_enum, stencil=stencil, relaxation_rate=omega,
-                           compressible=True, force_model=force_model, force=F, kernel_type='collide_only')
+                           compressible=True, force_model=force_model, force=F, streaming_pattern='pull')
     lbm_opt = LBMOptimisation(symbolic_field=src)
 
     collision = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
 
-    stream = create_stream_pull_with_output_kernel(collision.method, src, dst,
-                                                   {'density': ρ, 'velocity': u})
-
     config = ps.CreateKernelConfig(cpu_openmp=True, target=dh.default_target)
 
-    stream_kernel = ps.create_kernel(stream, config=config).compile()
     collision_kernel = ps.create_kernel(collision, config=config).compile()
 
     def init():
@@ -55,24 +51,28 @@ def test_total_momentum(method_enum, force_model, omega):
         dh.fill(u.name, 0)
 
         setter = macroscopic_values_setter(collision.method, velocity=(0,) * dh.dim,
-                                           pdfs=src.center_vector, density=ρ.center)
-        kernel = ps.create_kernel(setter, ghost_layers=0).compile()
+                                           pdfs=src, density=ρ.center,
+                                           set_pre_collision_pdfs=True)
+        kernel = ps.create_kernel(setter).compile()
         dh.run_kernel(kernel)
 
     sync_pdfs = dh.synchronization_function([src.name])
 
+    getter = macroscopic_values_getter(collision.method, ρ.center, u.center_vector, src, use_pre_collision_pdfs=True)
+    getter_kernel = ps.create_kernel(getter).compile()
+
     def time_loop(steps):
         dh.all_to_gpu()
-        for i in range(steps):
+        for _ in range(steps):
             dh.run_kernel(collision_kernel)
-            sync_pdfs()
-            dh.run_kernel(stream_kernel)
             dh.swap(src.name, dst.name)
+            sync_pdfs()
         dh.all_to_cpu()
 
     t = 20
     init()
     time_loop(t)
+    dh.run_kernel(getter_kernel)
     total = np.sum(dh.gather_array(u.name), axis=(0, 1))
     assert np.allclose(total / np.prod(L) / F / t, 1)