def test_morris_covariance_plot_grouped(): # Create a fixture to represent Si, the dictionary of output metrics Si = {'mu_star':[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 'names':['x1', 'x2', 'x3', 'x4', 'x5', 'x6'], 'mu_star_conf': [0.5, 1, 1.5, 2, 2.5, 3.0], 'sigma': [np.NAN, 0.2, 0.3, 0.5, np.NAN, 0.3 ]} fig, ax = plt.subplots(1, 1) covariance_plot(ax, Si, {}, unit=r"tCO$_2$/year")
def plot_morris(self): """Save plot as png(300 dpi) and eps (vector).""" if not self.psa_si: self.calculate_sensitivity() for i, v in enumerate(self.objnames): fig, (ax1, ax2) = plt.subplots(1, 2) horizontal_bar_plot(ax1, self.psa_si.get(i), {}, sortby='mu_star') covariance_plot(ax2, self.psa_si.get(i), {}) save_png_eps(plt, self.cfg.psa_outpath, 'mu_star_%s' % v) # close current plot in case of 'figure.max_open_warning' plt.cla() plt.clf() plt.close()
def plot_morris(self): """Save plot as png(300 dpi) and eps (vector).""" if not self.psa_si: self.calculate_sensitivity() for i, v in enumerate(self.objnames): fig, (ax1, ax2) = plt.subplots(1, 2) horizontal_bar_plot(ax1, self.psa_si.get(i), {}, sortby='mu_star') covariance_plot(ax2, self.psa_si.get(i), {}) save_png_eps(plt, self.cfg.psa_outpath, 'mu_star_%s' % v) # close current plot in case of 'figure.max_open_warning' plt.cla() plt.clf() plt.close()
def morris(): from SALib.analyze import morris from SALib.sample.morris import sample problem = { 'num_vars': 5, 'names': ['HEME', 'K1', 'MPY', 'MPYgu', 'QCC'], 'groups': None, 'bounds': [[.4, .5], [10., 30.], [30., 40.], [15., 25.], [10., 15.]] } parameter_values = sample(problem, N=10, num_levels=4, grid_jump=2, optimal_trajectories=None) parameters = assign_parameters() schedule = get_default_schedule() y_index = np.argmax(o["Curine"]) ys = [] for inputs in parameter_values: for i in range(len(problem["names"])): setattr(parameters, problem["names"][i], inputs[i]) _, __, ___, output = pbpk(parameters, schedule) ys.append(output["Curine"][y_index]) sis = morris.analyze(problem, parameter_values, np.array(ys), conf_level=0.95, print_to_console=True, num_levels=4, grid_jump=2, num_resamples=100) import matplotlib.pyplot as plt from SALib.plotting.morris import horizontal_bar_plot, covariance_plot, sample_histograms fig, (ax1, ax2) = plt.subplots(1, 2) horizontal_bar_plot(ax1, sis, {}, sortby='mu_star', unit=r"mg/g") covariance_plot(ax2, sis, {}, unit=r"mg/g") fig2 = plt.figure() sample_histograms(fig2, parameter_values, problem, {'color': 'y'}) plt.show()
# Files with a 4th column for "group name" will be detected automatically, e.g. # param_file = '../../SALib/test_functions/params/Ishigami_groups.txt' # Generate samples param_values = sample(problem, N=1000, num_levels=4, optimal_trajectories=None) # To use optimized trajectories (brute force method), # give an integer value for optimal_trajectories # Run the "model" -- this will happen offline for external models Y = Sobol_G.evaluate(param_values) # Perform the sensitivity analysis using the model output # Specify which column of the output file to analyze (zero-indexed) Si = morris.analyze(problem, param_values, Y, conf_level=0.95, print_to_console=True, num_levels=4, num_resamples=100) # Returns a dictionary with keys 'mu', 'mu_star', 'sigma', and 'mu_star_conf' # e.g. Si['mu_star'] contains the mu* value for each parameter, in the # same order as the parameter file fig, (ax1, ax2) = plt.subplots(1, 2) horizontal_bar_plot(ax1, Si, {}, sortby='mu_star', unit=r"tCO$_2$/year") covariance_plot(ax2, Si, {}, unit=r"tCO$_2$/year") fig2 = plt.figure() sample_histograms(fig2, param_values, problem, {'color': 'y'}) plt.show()
def runMorris(control_rows, output_dropdown, trajectories_text, levels_text, grid_jump_text, run_morris_button, results_output): inputs = [(control_row[0].value, control_row[1].value, control_row[2].value) for control_row in control_rows if control_row[0].value != no_selection] target_parameters = dict([(input[0], (input[1], input[2])) for input in inputs]) target_output_name = output_dropdown.value n_trajectories = trajectories_text.value num_levels = levels_text.value grid_jump = grid_jump_text.value run_morris_button.disabled = True try: problem, parameter_values, sis = hdm.run_salib_morris( target_parameters, target_output_name, n_trajectories, num_levels, grid_jump, 1 # os.cpu_count() ) except: with results_output: print("Invalid input", file=sys.stderr) return finally: run_morris_button.disabled = False from base64 import b64encode header = ','.join([name for name in target_parameters.keys()]) lines = [','.join([str(d) for d in inputs]) for inputs in parameter_values] csv = header + os.linesep + os.linesep.join(lines) payload = b64encode(csv.encode()) payload = payload.decode() fileName = "parameter_values.csv" title = "Download design" html = f'<a download="{fileName}" href="data:text/csv;charset=utf-8;base64,{payload}" target="_blank">{title}</a>' link = widgets.HTML(html) results_output.clear_output() with results_output: print("{0:<30} {1:>10} {2:>10} {3:>15} {4:>10}".format( "Parameter", "Mu_Star", "Mu", "Mu_Star_Conf", "Sigma")) num_vars = problem['num_vars'] for j in list(range(num_vars)): print("{0:30} {1:10.3f} {2:10.3f} {3:15.3f} {4:10.3f}".format( sis['names'][j], sis['mu_star'][j], sis['mu'][j], sis['mu_star_conf'][j], sis['sigma'][j])) display(link) fig1, (ax1, ax2) = plt.subplots(1, 2) horizontal_bar_plot(ax1, sis, {}, sortby='mu_star') covariance_plot(ax2, sis, {}) fig2 = plt.figure() sample_histograms(fig2, parameter_values, problem, {'color': 'y'}) fig1.tight_layout() show_inline_matplotlib_plots()
D=problem["num_vars"] #number of variables for x in [0,1]: #x=0 kgCO2, x=1 %comf_t for HVACsys in [1,2,3,4,5,6,7,8,9,10,12,13,16,17]: # Prepare data for the analysis for each system filename = 'HVACsys'+str(HVACsys) rows=data[data[:,13]==HVACsys,0:13].shape[0] #number of inputs/outputs n=divmod(rows,14)[0] model_input=data[data[:,13]==HVACsys,0:13][0:n*(D+1),:] model_output=data[data[:,13]==HVACsys,14+x:15+x][0:n*(D+1),:] print(filename) # Perform the sensitivity analysis Si = morris.analyze(problem, model_input, model_output, conf_level=0.95, print_to_console=True) #create plots fig1, (ax1, ax2) = plt.subplots(1, 2) ax1.set_title(filename) ax2.set_title(filename) if x == 0: horizontal_bar_plot(ax1, Si, {}, sortby='mu_star', unit=r"kgCO2") covariance_plot(ax2, Si, {}, unit=r"kgCO2") filename_update=filename+"_kgCO2" else: horizontal_bar_plot(ax1, Si, {}, sortby='mu_star', unit=r"%ComfTime") covariance_plot(ax2, Si, {}, unit=r"%ComfTime") filename_update=filename+"_ComfTime" #save and download plots plt.savefig(filename_update) files.download(filename_update+".png") plt.close()
[2. / 3, 1], [2. / 3, 0], [2. / 3, 2. / 3], [0, 2. / 3], [1. / 3, 1], [1, 1], [1, 1. / 3], [1. / 3, 1], [1. / 3, 1. / 3], [1, 1. / 3], [1. / 3, 2. / 3], [1. / 3, 0], [1, 0]], dtype=np.float) outputs = np.array([ 0.97, 0.71, 2.39, 0.97, 2.30, 2.39, 1.87, 2.40, 0.87, 2.15, 1.71, 1.54, 2.15, 2.17, 1.54, 2.20, 1.87, 1.0 ], dtype=np.float) salib_pb = { 'num_vars': 2, 'names': ['Test 1', 'Test 2'], 'groups': None, 'bounds': [[0.0, 1.0], [0.0, 1.0]] } name = "output" Si = ma.analyze(salib_pb, inputs, outputs, print_to_console=False) result = {k: v.tolist() for k, v in Si.items() if isinstance(v, np.ndarray)} print(json.dumps(result)) exit() fig, (ax1, ax2) = plt.subplots(1, 2) fig.suptitle('#{name} ' + 'sensitivity') mp.horizontal_bar_plot(ax1, Si, {}) mp.covariance_plot(ax2, Si, {}) plt.show()
# } # Files with a 4th column for "group name" will be detected automatically, e.g.: # param_file = '../../SALib/test_functions/params/Ishigami_groups.txt' # Generate samples param_values = sample(problem, N=1000, num_levels=4, grid_jump=2, \ optimal_trajectories=None) # To use optimized trajectories (brute force method), give an integer value for optimal_trajectories # Run the "model" -- this will happen offline for external models Y = Sobol_G.evaluate(param_values) # Perform the sensitivity analysis using the model output # Specify which column of the output file to analyze (zero-indexed) Si = morris.analyze(problem, param_values, Y, conf_level=0.95, print_to_console=True, num_levels=4, grid_jump=2, num_resamples=100) # Returns a dictionary with keys 'mu', 'mu_star', 'sigma', and 'mu_star_conf' # e.g. Si['mu_star'] contains the mu* value for each parameter, in the # same order as the parameter file fig, (ax1, ax2) = plt.subplots(1, 2) horizontal_bar_plot(ax1, Si,{}, sortby='mu_star', unit=r"tCO$_2$/year") covariance_plot(ax2, Si, {}, unit=r"tCO$_2$/year") fig2 = plt.figure() sample_histograms(fig2, param_values, problem, {'color':'y'}) plt.show()
# In[21]: Si = ma.analyze(morris_problem, sample, output, print_to_console=False) print("{:20s} {:>7s} {:>7s} {:>7s}".format("Name", "mu", "mu_star", "sigma")) for name, s1, st, mean in zip(morris_problem['names'], Si['mu'], Si['mu_star'], Si['sigma']): print("{:20s} {:=7.2f} {:=7.2f} {:=7.2f}".format(name, s1, st, mean)) # We can plot the results # In[22]: fig, (ax1, ax2) = plt.subplots(1,2) mp.horizontal_bar_plot(ax1, Si, param_dict={}) mp.covariance_plot(ax2, Si, {}) # ## Sanity check # Because this is such a trivially fast problem, we can do all 8000 simulations on laptop in a fraction of a second, so let's re-do it here in the notebook to compare with our results from above: # In[23]: def monte_carlo_large(data): y = max_vehicle_power(data[0], data[1], data[2], data[3], data[6], data[4], data[5]) return y # Run the sample through the monte carlo procedure of the power model output = monte_carlo_large(sample.T) Si = ma.analyze(morris_problem, sample, output, print_to_console=False) print("{:20s} {:>7s} {:>7s} {:>7s}".format("Name", "mu", "mu_star", "sigma")) for name, s1, st, mean in zip(morris_problem['names'], Si['mu'], Si['mu_star'], Si['sigma']):