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The first step is to select "Cell" > "Run All" from the toolbar. This will initialize all the widgets and allow you to interact with the unit operation options via the GUI controls.
from IPython.display import Image
import os
Image(os.path.join(os.path.dirname("__file__"), 'docs', 'figures', 'three_unit_flow.png'), width=800)from ipywidgets import *
from IPython.display import HTML, clear_output
import os
import numpy as np
# imports from vebio modules
from vebio.WidgetFunctions import WidgetCollection, OptimizationWidget, csv2widget_collection
from vebio.Utilities import get_host_computer
from vebio.RunFunctions import Pretreatment, Feedstock, EnzymaticHydrolysis, Bioreactor
from vebio.OptimizationFunctions import Optimization
#================================================================
# See if we're running on Eagle or on a laptop
hpc_run = get_host_computer()
#================================================================
import logging
logger = logging.getLogger()
logger.setLevel(logging.CRITICAL)Set the feedstock properties.
fs_options = csv2widget_collection("feedstock_params.csv")
fs_options.display_all_widgets()Set the options for the pretreatment operation below.
pt_options = csv2widget_collection("pretreatment_params.csv")
pt_options.display_all_widgets()Set the options for the enzymatic hydrolysis operation using either a two-phase reaction rate model or high-fidelity CFD below.
eh_options = csv2widget_collection("enzymatic_hydrolysis_params.csv")
eh_options.display_all_widgets()Set the options for the bubble column bioreaction operation below.
br_options = csv2widget_collection("bioreactor_params.csv")
br_options.display_all_widgets()obj_widget = widgets.Dropdown(
options=[('Biorector: OUR', ('br_out', 'our')),
('Enzymatic Hydrolysis: rho_g', ('eh_out', 'rho_g')),
('Enzymatic Hydrolysis: rho_x', ('eh_out', 'rho_x')),
('Enzymatic Hydrolysis: rho_sL',('eh_out', 'rho_sL')),
('Enzymatic Hydrolysis: rho_f', ('eh_out', 'rho_f')),
('Pretreatment: fis_0', ('eh_out', 'fis_0')),
('Pretreatment: X_X', ('eh_out', 'X_X')),
('Pretreatment: X_G', ('eh_out', 'X_G')),
('Pretreatment: rho_x', ('eh_out', 'rho_x')),
('Pretreatment: rho_f', ('eh_out', 'rho_f'))
],
value=('br_out', 'our'),
description='Objective:',
tooltip = 'Specifies the objective to use in optimization.'
)
display(obj_widget)#================================================================
sweep_button = widgets.Button(
description = 'Run Sweep.',
tooltip = 'Execute the model start-to-finish with the properties specified above.',
layout = {'width': '200px', 'margin': '25px 0px 100px 170px'},
button_style = 'primary'
)
#================================================================
# run_button_output = widgets.Output()
display(sweep_button)
#================================================================
# Define a function to be executed each time the run button is pressed
def sweep_button_action(b):
clear_output()
display(sweep_button)
Opt = Optimization(fs_options, pt_options, eh_options, br_options, obj_widget, hpc_run)
Opt.parameter_grid_sweep(nn=10, results_file='sweep_params.csv')
sweep_button.on_click(sweep_button_action)
#===============================================================import matplotlib.pyplot as plt
param_sweep_fn = 'sweep_params.csv'
if os.path.exists(param_sweep_fn):
with open(param_sweep_fn, 'r') as f:
firstline = f.readline().split(',')
Opt = Optimization(fs_options, pt_options, eh_options, br_options, obj_widget, hpc_run)
sweeps = np.loadtxt(param_sweep_fn, delimiter=',', skiprows=1)
bounds = Opt.var_real_bounds
extent = bounds[0][0], bounds[0][1], bounds[1][0], bounds[1][1]
nn = int(np.sqrt(len(sweeps)))
OUR = sweeps[:, 3].reshape(nn, nn)
shw = plt.imshow(OUR.T, extent=extent, aspect='auto', origin='lower')
_cs2 = plt.contour(OUR.T, levels=np.arange(65, 69, 0.5), extent=extent, origin='lower', colors='blue')
bar = plt.colorbar(shw)
bar.add_lines(_cs2)
bar.set_label(firstline[-1])
plt.xlabel(firstline[1])
plt.ylabel(firstline[2])Press the Optimize button below to launch the optimization of the start-to-finish operation using the above values as initial conditions.
This example maximizes OUR by changing user-specified pretreatment options.
logger.setLevel(logging.CRITICAL)
opt_button = widgets.Button(
description = 'Optimize.',
tooltip = 'Optimize for OUR using the conditions above as an initial guess.',
layout = {'width': '200px', 'margin': '25px 0px 25px 170px'},
button_style = 'warning'
)
#================================================================
display(opt_button)
#================================================================
# Define a function to be executed each time the run button is pressed
def opt_button_action(b):
clear_output()
display(opt_button)
params_filename = 'virteng_params_optimization.yaml'
opt_results_file = 'optimization_results.csv'
Opt = Optimization(fs_options, pt_options, eh_options, br_options, obj_widget,
hpc_run)
opt_result = Opt.scipy_minimize(Opt.objective_function, opt_results_file=opt_results_file)
print(opt_result)
opt_button.on_click(opt_button_action)
#================================================================opt_results = np.loadtxt('optimization_results.csv', delimiter=',', skiprows=1)
shw = plt.imshow(OUR.T, extent=extent, aspect='auto', origin='lower')
bar = plt.colorbar(shw)
bar.set_label('OUR')
plt.xlabel(firstline[1])
plt.ylabel(firstline[2])
plt.scatter(opt_results[:, 1], opt_results[:, 2], s=50, c='k', marker='o')
plt.plot(opt_results[:, 1], opt_results[:, 2], color='k')
plt.scatter(opt_results[-1, 1], opt_results[-1, 2], s=50, c='r', marker='o')# a = HTML('''<script>
# code_show=true;
# function code_toggle() {
# if (code_show){
# $('div.input').hide();
# } else {
# $('div.input').show();
# }
# code_show = !code_show
# }
# $( document ).ready(code_toggle);
# </script>
# <form action="javascript:code_toggle()"><input type="submit" \
# value="Toggle code visibility (hidden by default)."></form>''')
# display(a)# # reload "run functions" code if needed
# if False:
# from importlib import reload
# import vebio.RunFunctions
# reload(vebio.RunFunctions)
# from vebio.RunFunctions import run_pretreatment, run_enzymatic_hydrolysis, run_bioreactor