diff --git a/content/Jackson_Network_Model/qt_lab_jackson_networks.ipynb b/content/Jackson_Network_Model/qt_lab_jackson_networks.ipynb index e9c0a41..f87ea70 100644 --- a/content/Jackson_Network_Model/qt_lab_jackson_networks.ipynb +++ b/content/Jackson_Network_Model/qt_lab_jackson_networks.ipynb @@ -45,13 +45,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# import piplite\n", - "# await piplite.install('scipy')\n", - "# await piplite.install('seaborn')\n", + "import piplite\n", + "await piplite.install('scipy')\n", + "await piplite.install('seaborn')\n", "\n", "import scipy.linalg as linalg\n", "import math\n", @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -133,42 +133,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -0.1, -0.4],\n", - " [-0.6, 1. , -0.4],\n", - " [-0.3, -0.3, 1. ]])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "a" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 4, 3])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "b" ] @@ -186,20 +162,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 5. , 10. , 7.5])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.dot(linalg.inv(a), b)" ] @@ -213,20 +178,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 5. , 10. , 7.5])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "arrival_rate = linalg.solve(a, b)\n", "arrival_rate" @@ -256,20 +210,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5 , 0.5 , 0.75])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "servers = np.array([1, 2, 1])\n", "service_rate = np.array([10, 10, 10])\n", @@ -305,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -437,72 +380,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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performance
$\\rho$0.5
$L_q$0.5
$L_s$1.0
$W_s$0.2
$W_q$0.1
\n", - "
" - ], - "text/plain": [ - " performance\n", - "$\\rho$ 0.5\n", - "$L_q$ 0.5\n", - "$L_s$ 1.0\n", - "$W_s$ 0.2\n", - "$W_q$ 0.1" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "queues = [MMSQueue(l, mu, s) for l, mu, s in zip(arrival_rate, service_rate, \n", " servers)]\n", @@ -512,144 +392,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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performance
$\\rho$0.50
$L_q$0.33
$L_s$1.33
$W_s$0.13
$W_q$0.03
\n", - "
" - ], - "text/plain": [ - " performance\n", - "$\\rho$ 0.50\n", - "$L_q$ 0.33\n", - "$L_s$ 1.33\n", - "$W_s$ 0.13\n", - "$W_q$ 0.03" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "queues[1].summary_frame().round(2)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
performance
$\\rho$0.75
$L_q$2.25
$L_s$3.00
$W_s$0.40
$W_q$0.30
\n", - "
" - ], - "text/plain": [ - " performance\n", - "$\\rho$ 0.75\n", - "$L_q$ 2.25\n", - "$L_s$ 3.00\n", - "$W_s$ 0.40\n", - "$W_q$ 0.30" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "queues[2].summary_frame()" ] @@ -663,17 +417,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected # of customers in queuing network = 5.33\n" - ] - } - ], + "outputs": [], "source": [ "# total in system\n", "total_in_system = sum([q.total_in_system for q in queues])\n", @@ -689,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -785,84 +531,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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node_0node_1node_2
$\\rho$0.50.500.75
$L_q$0.50.332.25
$L_s$1.01.333.00
$W_s$0.20.130.40
$W_q$0.10.030.30
\n", - "
" - ], - "text/plain": [ - " node_0 node_1 node_2\n", - "$\\rho$ 0.5 0.50 0.75\n", - "$L_q$ 0.5 0.33 2.25\n", - "$L_s$ 1.0 1.33 3.00\n", - "$W_s$ 0.2 0.13 0.40\n", - "$W_q$ 0.1 0.03 0.30" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model = JacksonNetwork(a, b, service_rate, servers)\n", "summary = model.summary_frame().round(2)\n", @@ -871,46 +542,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected # of customers in queuing network = 5.33\n" - ] - } - ], + "outputs": [], "source": [ "print(f'Expected # of customers in queuing network = {model.total_in_system:.2f}')" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def plot_jackson_network_summary(summary):\n", " plt.figure(figsize=(12,6))\n", @@ -958,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1180,114 +823,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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$W_q$0.10.030.30
\n", - "
" - ], - "text/plain": [ - " node_1 node_2 node_3\n", - "$\\rho$ 0.5 0.50 0.75\n", - "$L_q$ 0.5 0.33 2.25\n", - "$L_s$ 1.0 1.33 3.00\n", - "$W_s$ 0.2 0.13 0.40\n", - "$W_q$ 0.1 0.03 0.30" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "filepath_test = '../data/test_parameters.csv'\n", "\n", @@ -1326,114 +864,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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node_1node_2node_3
$\\rho$0.860.710.82
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