From 90d03fbfbdacbd27fab6a79933c2735e2f15ab0c Mon Sep 17 00:00:00 2001
From: Ghassen Nakti <ghassen.nakti@eonerc.rwth-aachen.de>
Date: Thu, 26 Sep 2019 10:54:20 +0200
Subject: [PATCH] Update Resistive Companion Exercice Task 1
---
..._SimExample_ResistiveCompanion_Task1.ipynb | 31 ++++++++++---------
1 file changed, 17 insertions(+), 14 deletions(-)
diff --git a/exercises/03_ResistiveCompanion/Exercise_SimExample_ResistiveCompanion_Task1.ipynb b/exercises/03_ResistiveCompanion/Exercise_SimExample_ResistiveCompanion_Task1.ipynb
index 3ac22ff..5430972 100644
--- a/exercises/03_ResistiveCompanion/Exercise_SimExample_ResistiveCompanion_Task1.ipynb
+++ b/exercises/03_ResistiveCompanion/Exercise_SimExample_ResistiveCompanion_Task1.ipynb
@@ -115,7 +115,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b1174390d53c43ba84574ecbde14157f",
+ "model_id": "38d521c9d0004439890b4efa5c29f6f6",
"version_major": 2,
"version_minor": 0
},
@@ -129,12 +129,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d7851ddde8b445a1bfef2bf7cf7b9f78",
+ "model_id": "15ebb06fc03543ae9fe574ae03e53ed6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "FloatProgress(value=0.0, max=0.0002)"
+ "FloatProgress(value=0.0, max=0.01)"
]
},
"metadata": {},
@@ -147,9 +147,9 @@
"\n",
"#Assign slider values to simulation parameters\n",
"model_name = 'CS_R1L1'\n",
- "time_step = 1e-4\n",
+ "time_step = 1e-3\n",
"#Set final time to calculate first two simulation steps\n",
- "final_time = 2e-4\n",
+ "final_time = 1e-2\n",
"#Number of simulation steps\n",
"npoint = int(np.round(final_time/time_step))\n",
"\n",
@@ -221,9 +221,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "l1.i_intf: [-0.09901 -0.295069]\n",
- "n1.v: [ 1.980198 1.940986]\n",
- "r1.i_intf: [-9.90099 -9.704931]\n"
+ "l1.i_intf: [-0.909091 -2.561983 -3.91435 -5.020832 -5.926135 -6.666838 -7.272867\n",
+ " -7.76871 -8.174399 -8.506326]\n",
+ "n1.v: [ 1.818182 1.487603 1.21713 0.995834 0.814773 0.666632 0.545427\n",
+ " 0.446258 0.36512 0.298735]\n",
+ "r1.i_intf: [-9.090909 -7.438017 -6.08565 -4.979168 -4.073865 -3.333162 -2.727133\n",
+ " -2.23129 -1.825601 -1.493674]\n"
]
}
],
@@ -250,14 +253,14 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
- "<Figure size 576x432 with 1 Axes>"
+ "<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
@@ -277,10 +280,10 @@
"y_values= np.insert(y_values, 0, I_src*R1)\n",
"t = np.arange(npoint+1)*time_step\n",
"\n",
- "plt.figure(figsize=(8,6))\n",
- "plt.xlabel('Time [ms]')\n",
+ "plt.figure(figsize=(10,8))\n",
+ "plt.xlabel('Time [s]')\n",
"plt.ylabel('Voltage [V]')\n",
- "plt.axis([0, 4*time_step, 1.9, 2.05])\n",
+ "plt.axis([0, final_time+time_step , 0, 2.05])\n",
"plt.scatter(t,y_values, label='$e_{1}$(t)')\n",
"\n",
"#show corresponding values on the plot\n",
--
GitLab