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"## Stages\n",
"Fermentation experiments are commonly broken down into stages (e.g. phases), during which the objective changes. For example, early in the fermentation growth may be optimized, while production may be optimized later in the fermentation.\n",
"\n",
"Here, let's analyze data with an aerobic growth stage and anaerobic production stage."
]
},
{
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"execution_count": 1,
"metadata": {
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"text": [
"C:\\Users\\Naveen\\Anaconda3\\lib\\site-packages\\IPython\\html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
" \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
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"C:\\Users\\Naveen\\Anaconda3\\lib\\site-packages\\cobra\\io\\__init__.py:10: UserWarning:\n",
"\n",
"cobra.io.sbml requires libsbml\n",
"\n"
]
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{
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"text": [
"IN FLUXES OUT FLUXES OBJECTIVES \n",
"o2_e -17.58 h2o_e 45.62 Ec_biomass_iJO1366_core_53p95M 0.982\n",
"nh4_e -10.61 co2_e 19.68 \n",
"glc__D_e -10.00 h_e 9.03 \n",
"pi_e -0.95 mththf_c 0.00 \n",
"so4_e -0.25 5drib_c 0.00 \n",
"k_e -0.19 4crsol_c 0.00 \n",
"fe2_e -0.02 meoh_e 0.00 \n",
"mg2_e -0.01 amob_c 0.00 \n",
"cl_e -0.01 \n",
"ca2_e -0.01 \n",
"cu2_e -0.00 \n",
"mn2_e -0.00 \n",
"zn2_e -0.00 \n",
"ni2_e -0.00 \n",
"mobd_e -0.00 \n",
"cobalt2_e -0.00 \n"
]
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/0aJFhcYwS1JmZqaGDh2q4cOHa8qUKRo0aJDCw8Md20pNTVVo6H+e9FO1alUF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\nyoVzHgBQmpiHGQAAACgBBOY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AAAAHBGYAAADAAYEZAAAAcEBgBgAAABwQmAEAAAAHBGYA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\njUcAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import impact\n",
"import cobra\n",
"import cobra.test\n",
"\n",
"# Hide some warnings to make the output cleaner\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"% matplotlib inline\n",
"\n",
"# Let's grab the iJO1366 E. coli model, cobra's test module has a copy\n",
"model = cobra.test.create_test_model(\"ecoli\")\n",
"\n",
"# Optimize the model\n",
"model.optimize()\n",
"\n",
"# Print a summary of the fluxes\n",
"model.summary()\n",
"\n",
"# Let's consider one substrate and five products\n",
"biomass_keys = ['Ec_biomass_iJO1366_core_53p95M']\n",
"substrate_keys = ['EX_glc_e']\n",
"product_keys = ['EX_for_e','EX_ac_e','EX_etoh_e','EX_succ_e']\n",
"analyte_keys = biomass_keys+substrate_keys+product_keys\n",
"# We'll use numpy to generate an arbitrary time vector\n",
"import numpy as np\n",
"\n",
"# The initial conditions (mM) [biomass, substrate, \n",
"# product1, product2, ..., product_n]\n",
"y0 = [0.05, 1000, 0, 0, 0, 0]\n",
"t_aerobic = np.linspace(0,6,600)\n",
"\n",
"# Returns a dictionary of the profiles\n",
"from impact.synthetic_data import generate_data\n",
"dFBA_profiles_aerobic = generate_data(y0, t_aerobic, model, \n",
" biomass_keys, substrate_keys, \n",
" product_keys, plot = True)\n",
"\n",
"# Let's make a new vector of initial values based on the output of the previous simulation\n",
"y0 = [dFBA_profiles_aerobic[analyte][-1] for analyte in biomass_keys+substrate_keys+product_keys]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IN FLUXES OUT FLUXES OBJECTIVES \n",
"glc__D_e -10.00 h_e 27.87 Ec_biomass_iJO1366_core_53p95M 0.242\n",
"nh4_e -2.61 for_e 17.28 \n",
"h2o_e -1.71 ac_e 8.21 \n",
"pi_e -0.23 etoh_e 8.08 \n",
"co2_e -0.09 succ_e 0.08 \n",
"so4_e -0.06 5drib_c 0.00 \n",
"k_e -0.05 glyclt_e 0.00 \n",
"mg2_e -0.00 mththf_c 0.00 \n",
"fe2_e -0.00 4crsol_c 0.00 \n",
"fe3_e -0.00 meoh_e 0.00 \n",
"ca2_e -0.00 amob_c 0.00 \n",
"cl_e -0.00 \n",
"cu2_e -0.00 \n",
"mn2_e -0.00 \n",
"zn2_e -0.00 \n",
"ni2_e -0.00 \n",
"mobd_e -0.00 \n",
"cobalt2_e -0.00 \n"
]
},
{
"data": {
"image/png": 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iTetYi0ihp8+8iIctWgT9+8Njj8Gbb0K5ct6OSMSrPLWOtWqsRURESoqTJ+HF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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Optimize the model after simulating anaerobic conditions, \n",
"# we don't get many products aerobically\n",
"model.reactions.get_by_id('EX_o2_e').lower_bound = 0\n",
"model.reactions.get_by_id('EX_o2_e').upper_bound = 0\n",
"model.optimize()\n",
"model.summary()\n",
"\n",
"t_anaerobic = np.linspace(6,10,400)\n",
"dFBA_profiles_anaerobic = generate_data(y0, t_anaerobic, model, \n",
" biomass_keys, substrate_keys, \n",
" product_keys, plot = True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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R8wxWbVpgYCAtW7Zkzpw5bNu2DT8/v7teb9CgQRQqVIi33347aV+JEiWYN28eLVq0YOzY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\nlWJXFXMdB44f50TbttwybBgVw8OZU6ECoWpzWS4xEapXh1G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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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hhBYhhOEhhA9DCO+HEH5bdv8mIYRhIYRPQwjPhxAaZr9cSZJqzpw58Mc/Jh3g\nU0+FkSMNwlJtU5ExicXAxTHGnYBOwLkhhDbApcCLMcbWwHDgsuyVKUlSzYkR/vWvJPhOnZp0hnv3\nTjrDkmqXtY5JxBinAlPLrs8JIXwMtACOBvYre9j9QAlJQJYkKW+NHw/nnw+TJ8ODD8J++639GEn5\nK6P/44YQtgaKgFFAsxjjNCgPzE2ruzhJkmrK/PnQpw907AgHHADvvGMQlgpBhU+gCyHUB4YCF5R1\niONKD1n5drm+ffuWXy8uLqa4uDizKiVJyqJnn026we3bJyG4RYu0K5JUESUlJZSUlFTpOUKMq82w\nyx4UQl3gaeC5GOPfy+77GCiOMU4LIWwOvBxjbLuKY2NFXkOSpJWF5XaxyMbfksmT4cILkzWDb7kF\nDjmk2l9CUg0KIRBjzGj7m4qOSQwAPloahMs8CZxWdv1U4IlMXliSpLQsXAjXXZd0gouKkjBsEJYK\n01o7wyGEvYCRwPskoxARuBwYDTwCtAQmAd1jjLNWcbydYUlSpVR3ZzhGeOopuPjiZOOM/v1hu+2q\n/LSSckRlOsMVGpOoCsOwJKmyqjMMf/xxMhIxeTL8/e9w8MFVrU5SrsnmmIQkSXnphx+STvC++8Jh\nhyXbKBuEJS1lGJYk1UqlpXDPPdCmDcyeDR9+mHSG69VLuzJJuaTCS6tJkpQvXn8dfvtbWHddePpp\n+OUv065IUq4yDEuSao1vv4VLLoGXX4Z+/aBnTwgZTQ9KKjSOSUiS8t68efDnP0O7dtCyJXzyCZx0\nkkFY0trZGZYk5a3SUhg4EK64Ajp1gtGjYdtt065KUj4xDEuS8tKIEckqEfXqwZAh0Llz2hVJykeG\nYUlSXvnsM/jjH+Gdd+D66+FXv3IcQlLlOTMsScoL06fDBRckHeBOnZK54B49DMKSqsbOsCQpL7Rp\nA927JzvJbbZZ2tVIqi3cjlmSlJMWLoT111/W9v3oo0jbtikWJCnnuR2zJKnWGDRoxdsGYUnZYGdY\nkpRzYoSdd4aPPgrL3effEklrZmdYklQrPPdcspWyJGWbYViSlHNuuAF+//u0q5BUCAzDkqScMno0\nTJiQrBwhSdlmGJYk5ZTrrku6wvXqpV2JpELgCXSSpJzx4YdwwAFJZ3jDDZOTYZbyb4mktfEEOklS\nXuvXL9llbsMN065EUqGwMyxJyglffgm77550hRs2TO6zMywpE3aGJUl564YboHfvZUFYkmqCnWFJ\nUuqmToVPAIQXAAAUsElEQVQdd4RPPoGmTZfdb2dYUibsDEuS8tLNN8NJJ60YhCWpJtgZliSlasYM\n2H57GDsWWrVa8XN2hiVlws6wJCnv9O8P3br9PAhLUk2wMyxJSs306bDDDvD227D11j//vJ1hSZmw\nMyxJyis33QTHH7/qICxJNcHOsCQpFd9/D61bw7hxsNVWq36MnWFJmbAzLEnKGzfcAN27rz4IS1JN\nsDMsSapx330HbdrAu+9Cy5arf5ydYUmZsDMsScoLN9wAJ5645iAsSTXBzrAkqUYt3W3uvfegRYs1\nP9bOsKRMZKUzHEK4J4QwLYTw3nL39QkhfB1CGFt2ObQyBUuSCs+f/gSnnbb2ICxJNWGtneEQwt7A\nHOCBGGO7svv6AD/GGPuv9QXsDEuSyowfDx07wiefQJMma3+8nWFJmchKZzjG+Cowc1Wvl8kLSZJ0\n1VVw4YUVC8KSVBOqcgLdeSGEd0IId4cQGlZbRZKkWmnsWBgxAi66KO1KJGmZyobh24BtY4xFwFRg\nreMSkqTCdtllcOWVsNFGaVciScvUrcxBMcb/LnfzLuCpNT2+b9++5deLi4spLi6uzMtKkvLU8OHw\nxRdw1llpVyKpNikpKaGkpKRKz1GhpdVCCFsDT8UYdym7vXmMcWrZ9YuAPWKMPVdzrCfQSVIBixH2\n3BMuvhh69MjsWE+gk5SJypxAt9bOcAhhEFAMbBpCmAz0AbqEEIqAUmAi0DvjaiVJBWHIEFiyJNl6\nWZJyjZtuSJKyZv78ZNvlBx+EfffN/Hg7w5Iy4XbMkqSc0r8/7LFH5YKwJNUEO8OSpKyYMgV22QVG\nj4Ztt63cc9gZlpSJynSGDcOSpKw480zYdFP4618r/xyGYUmZyMoJdJIkZWrsWHjmGfj007QrkaQ1\nc2ZYklStYkyWUbvmGmjo/qSScpxhWJJUrYYOhRkzkjEJScp1zgxLkqrNnDnQti0MGgT77FP153Nm\nWFImPIFOkpSqSy5JVpF44IHqeT7DsKRMGIYlSan5+ONkPeEPPoBmzarnOQ3DkjLhphuSpFTECOee\nC1dfXX1BWJJqgmFYklRlQ4bAzJlwzjlpVyJJmXFMQpJUJbNnw447wiOPQOfO1fvcjklIyoQzw5Kk\nGnf++TB/Ptx9d/U/t2FYUibcgU6SVKNefx3+9S/48MO0K5GkynFmWJJUKQsXwllnwd//DptsknY1\nklQ5hmFJUqX06wfbbQfHH592JZJUec4MS5Iy9tFHsN9+MG4ctGiRvddxZlhSJlxnWJKUdaWlyXhE\n377ZDcKSVBMMw5KkjNxyS/LRNYUl1QaOSUiSKuyzz5K1hN94A7bfPvuv55iEpEw4JiFJypolS+C0\n06BPn5oJwpJUEwzDkqQKuekmWH99OPfctCuRpOrjmIQkaa0++AC6dIExY2DrrWvudR2TkJQJxyQk\nSdXup5/g1FPhL3+p2SAsSTXBMCxJWqNrroFmzaBXr7QrkaTqVzftAiRJuWvECBgwINlcI2T0xqMk\n5Qc7w5KkVZoxA04+OQnDzZqlXY0kZYcn0EmSfiZGOP542GoruPnm9OrwBDpJmajMCXSOSUiSfubu\nu+GLL2DQoLQrkaTssjMsSVrBxx/DvvvCyJHQtm26tdgZlpQJl1aTJFXJnDlw3HFw/fXpB2FJqgl2\nhiVJQDInfNJJyS5zAwakXU3CzrCkTDgzLEmqtP/7P/jwQ3jjjbQrkaSas9YxiRDCPSGEaSGE95a7\nb5MQwrAQwqchhOdDCA2zW6YkKZvGjIE+fWDoUNhww7SrkaSaU5GZ4XuBQ1a671LgxRhja2A4cFl1\nFyZJqhkzZkD37klnePvt065GkmpWhWaGQwitgKdijO3Kbn8C7BdjnBZC2BwoiTG2Wc2xzgxLUo4q\nLYWuXaFNG7jpprSr+TlnhiVloiZXk2gaY5wGEGOcCjSt5PNIklJ05ZXJChLXX592JZKUjuo6gc7/\nrktSnhk8OLmMHg316qVdjSSlo7JheFoIodlyYxLfrenBffv2Lb9eXFxMcXFxJV9WklQd3noLfvtb\neOkl2GyztKuRpMopKSmhpKSkSs9R0ZnhrUlmhncpu90PmBFj7BdCuATYJMZ46WqOdWZYknLIlCnQ\noQP84x/QrVva1ayZM8OSMlGZmeG1huEQwiCgGNgUmAb0AR4HHgVaApOA7jHGWas53jAsSTliwQIo\nLoYjjoCrrkq7mrUzDEvKRFbCcFUZhiUpN8QIJ58MixbBkCEQMvpzkQ7DsKRMuAOdJGm1rrwSxo+H\n4cPzIwhLUk0wDEtSAbjzTnjkEXj9dXeYk6TlOSYhSbXcM89Ar17wyivwi1+kXU1mHJOQlAnHJCRJ\nK3jrLTj9dHjqqfwLwpJUEyq7A50kKcdNmABHHw133QV77pl2NZKUmwzDklQLffMNHHRQsnza0Uen\nXY0k5S7DsCTVMt9/nwThs8+G3/wm7WokKbd5Ap0k1SI//AAHHAAHHwx/+Uva1VSdJ9BJyoSbbkhS\nAZs3Dw45BHbdFf75z9qxlrBhWFImDMOSVKAWLoRjjoHNNoP77oM6tWQIzjAsKROVCcO15NelJBWu\nBQugWzeoXx8GDKg9QViSaoK/MiUpj82fn3SEN94YBg2Cuq4eL0kZMQxLUp6aPz9ZNm2TTeChh6Be\nvbQrkqT8YxiWpDw0bx507ZrMCD/4oB1hSaosw7Ak5ZnZs+GII2DLLeGBBwzCklQVhmFJyiP//S/s\nvz+0aQP33gvrrJN2RZKU3wzDkpQnJk+GffaBQw+F224zCEtSdTAMS1Ie+OSTJAj37g1//nPt2FBD\nknKBk2aSlOPGjIGjjoLrr4dTT027GkmqXewMS1IOe+wxOPxwuOMOg7AkZYOdYUnKQTHCzTfDTTfB\nc8/B7runXZEk1U6GYUnKMYsXwwUXwIgR8Prr0KpV2hVJUu1lGJakHDJ7NvTsCYsWwWuvQcOGaVck\nSbWbM8OSlCM++ww6doQWLeCZZwzCklQTDMOSlAOefRb23jsZj/i//4N69dKuSJIKg2MSkpSiGKFf\nP/jnP5OVI/baK+2KJKmwGIYlKSWzZ0OvXjBxIoweDc2bp12RJBUexyQkKQXjxsEvfwmbbgojRxqE\nJSkthmFJqkExwm23wcEHJ9sq3347rL9+2lVJUuFyTEKSasgPPyRjEePHJ+sHb7992hVJkuwMS1IN\nGDUqGYto2hTeeMMgLEm5ws6wJGXRTz/BtdfCXXfBrbfCccelXZEkaXmGYUnKko8+gpNPhs03h3fe\nST5KknJLlcYkQggTQwjvhhDGhRBGV1dRkpTPSkvhb3+D/faD3r3h6acNwpKUq6raGS4FimOMM6uj\nGEnKd599BmefDYsWJXPC222XdkWSpDWp6gl0oRqeQ5Ly3k8/wV/+Ap07w7HHwiuvGIQlKR9UtTMc\ngRdCCEuAO2OMd1VDTZKUV0aPTpZMa9EC3n4bWrVKuyJJUkVVNQzvFWOcEkLYjCQUfxxjfLU6CpOk\nXPfDD9CnDzz8MNx8M/ToASGkXZUkKRNVCsMxxillH/8bQngM6AD8LAz37du3/HpxcTHFxcVVeVlJ\nSlVpKTz4IFx2GRx+OHzwATR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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the whole profile\n",
"combined_profiles = {analyte:\n",
" dFBA_profiles_aerobic[analyte]+\n",
" dFBA_profiles_anaerobic[analyte] \n",
" for analyte in biomass_keys+substrate_keys+product_keys}\n",
"\n",
"import matplotlib.pyplot as plt\n",
"% matplotlib inline\n",
"plt.figure(figsize=[12,6])\n",
"for analyte in substrate_keys + product_keys:\n",
" plt.plot(np.linspace(0,10,1000),combined_profiles[analyte])\n",
" min,max = plt.ylim()\n",
" plt.ylim([0,max])\n",
" \n",
"plt.text(5,max*0.95,'Aerobic')\n",
"plt.text(6+.5,max*0.95,'Anaerobic')\n",
"plt.legend(substrate_keys+product_keys,loc=2)\n",
"plt.plot([6]*10,np.linspace(min,max,10),'k',linewidth=3)\n",
"\n",
"plt.figure(figsize=[12,6])\n",
"plt.plot(np.linspace(0,10,1000),combined_profiles[biomass_keys[0]])\n",
"min,max = plt.ylim()\n",
"plt.ylim([0,max])\n",
"plt.plot([6]*10,np.linspace(min,max,10),'k',linewidth=3)\n",
"plt.text(5,max*0.95,'Aerobic')\n",
"plt.text(6+.5,max*0.95,'Anaerobic')\n",
"plt.legend(['Biomass'],loc=2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Now that we have a simulated 'two-stage' fermentation data, let's try to analyze it. First let's try to curve fit and pull parameters from the overall data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Started fit\n",
"1000\n",
"Finished fit\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is the format of your plot grid:\n",
"[ (1,1) x1,y1 ] [ (1,2) x2,y2 ] [ (1,3) x3,y3 ]\n",
"\n"
]
},
{
"data": {
"image/png": 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AAAEEEEAAAQQQQAABTxOwPMDq1auXDBo0SO688858dt9//71Z+bRkyZJCTadN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\nWuD/A5S3vIr0fNnJAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# These time courses together form a single trial\n",
"single_trial = impact.SingleTrial()\n",
"for analyte in analyte_keys:\n",
" # Instantiate the timecourse\n",
" timecourse = impact.TimeCourse()\n",
" \n",
" # Define the trial identifier for the experiment\n",
" timecourse.trial_identifier.analyte_name = analyte\n",
" timecourse.trial_identifier.strain_id = 'Demo'\n",
" \n",
" if analyte in biomass_keys:\n",
" timecourse.trial_identifier.analyte_type = 'biomass'\n",
" elif analyte in substrate_keys:\n",
" timecourse.trial_identifier.analyte_type = 'substrate'\n",
" elif analyte in product_keys:\n",
" timecourse.trial_identifier.analyte_type = 'product'\n",
" else:\n",
" raise Exception('unid analyte')\n",
" \n",
" timecourse.time_vector = np.linspace(0,10,1000)\n",
" timecourse.data_vector = combined_profiles[analyte]\n",
" single_trial.add_titer(timecourse)\n",
"\n",
"# Add this to a replicate trial (even though there's one replicate)\n",
"replicate_trial = impact.ReplicateTrial()\n",
"replicate_trial.add_replicate(single_trial)\n",
"\n",
"# Add this to the experiment\n",
"experiment = impact.Experiment(info = {'experiment_title' : 'test experiment'})\n",
"experiment.add_replicate_trial(replicate_trial)\n",
"\n",
"import plotly.offline\n",
"plotly.offline.init_notebook_mode()\n",
"fileName = impact.printGenericTimeCourse_plotly(replicateTrialList=[replicate_trial], \n",
" titersToPlot=biomass_keys, output_type='image',)\n",
"\n",
"from IPython.display import Image\n",
"Image(fileName)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'A': 38.794397885882105, 'lam': 5.4900330240132948, 'growth_rate': 4.9999996572528627}\n"
]
},
{
"data": {
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QHngAli71RrMUJMydc4xcPpJHpz/Kg60e5Kl2T1E6TvMYC0IzRUUkJLKyoEcPWL/e6zMv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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"for single_trial in replicate_trial.single_trial_list:\n",
" print(replicate_trial.single_trial_list[0].analyte_dict[biomass_keys[0]].rate)\n",
" plt.plot(np.linspace(0,10,1000),combined_profiles[biomass_keys[0]])\n",
" plt.plot([0,1,2,3,4,5,6,7,8,9,10],replicate_trial.single_trial_list[0].analyte_dict[biomass_keys[0]].data_curve_fit([0,1,2,3,4,5,6,7,8,9,10]))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 600]\n",
"Started fit\n",
"601\n",
"Finished fit\n",
"[600, 1000]\n",
"Started fit\n",
"400\n",
"Finished fit\n",
"Stage: 0\n",
"{'A': 18.09275269452317, 'lam': 50.209771541246589, 'growth_rate': 3.0339841137990837}\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stage: 1\n",
"{'A': 38.794398023096186, 'lam': 4.4741197722850785, 'growth_rate': 4.9999859144920213}\n"
]
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# We can break the trials into stages\n",
"replicate_trial.calculate_stages(stage_indices=[[0,600],[600,1000]])\n",
"\n",
"for stage_number in [0,1]:\n",
" print('Stage: ',stage_number)\n",
" plt.figure()\n",
" stage = replicate_trial.stages[stage_number]\n",
" timecourse = stage.single_trial_list[0].analyte_dict[biomass_keys[0]]\n",
"\n",
" for single_trial in replicate_trial.single_trial_list:\n",
" print(timecourse.rate)\n",
" plt.plot(timecourse.time_vector,\n",
" timecourse.data_vector)\n",
" plt.plot(timecourse.time_vector,timecourse.data_curve_fit(timecourse.time_vector))\n",
" \n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}