Stop using default mpi_call

[simgrid.git] / docs / source / tuto_network_calibration / network_calibration_tutorial.ipynb

diff --git a/docs/source/tuto_network_calibration/network_calibration_tutorial.ipynb b/docs/source/tuto_network_calibration/network_calibration_tutorial.ipynb
index 8f7ebf24142c491087e90426388baab35d686975..7b77c0edbeee7b5c521adb1643597c9a04a3f8c5 100644 (file)
--- a/docs/source/tuto_network_calibration/network_calibration_tutorial.ipynb
+++ b/docs/source/tuto_network_calibration/network_calibration_tutorial.ipynb
@@ -6733,7 +6733,7 @@
     "import json\n",
     "\n",
     "def plot_compare(df):\n",
-    "    \"\"\" Auxiliary function to compare simgrid and reality \"\"\"\n",
+    "    \"\"\" Auxiliary function to compare SimGrid and reality \"\"\"\n",
     "    func = list(df['op'].unique())\n",
     "    assert len(func) == 1\n",
     "    func = func[0]\n",
@@ -10656,7 +10656,7 @@
     }
    ],
    "source": [
-    "# simgrid has fewer executions than real life, keep the same amount of samples for better visualization\n",
+    "# SimGrid has fewer executions than real life, keep the same amount of samples for better visualization\n",
     "N = df_smpi_send.groupby(\"msg_size\").size().iloc[0]\n",
     "\n",
     "tmp = pandas.concat([df_send.groupby(\"msg_size\").sample(N), df_smpi_send, df_isend.groupby(\"msg_size\").sample(N), df_smpi_isend, df_recv.groupby(\"msg_size\").sample(N), df_smpi_recv, df_pingpong.groupby(\"msg_size\").sample(N), df_smpi_pingpong])\n",
@@ -10948,7 +10948,7 @@
     }
    ],
    "source": [
-    "# simgrid has fewer executions than real life, keep the same amount of samples for better visualization\n",
+    "# SimGrid has fewer executions than real life, keep the same amount of samples for better visualization\n",
     "N = df_smpi_send.groupby(\"msg_size\").size().iloc[0]\n",
     "\n",
     "tmp = pandas.concat([df_send.groupby(\"msg_size\").sample(N), df_smpi_send, df_isend.groupby(\"msg_size\").sample(N), df_smpi_isend, df_recv.groupby(\"msg_size\").sample(N), df_smpi_recv, df_pingpong.groupby(\"msg_size\").sample(N), df_smpi_pingpong])\n",