def generate_vector_field(poly_model, pmodel, \ axr = np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), \ bxr = np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), \ make_grid = True, limit_grid_radius = 0,color = 'k'): """ Generates a field of vectors using the base color shift model. | Has the option to plot vector field. Args: :poly_model: | function handle to model :pmodel: | ndarray with model parameters. :axr: | np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), optional | Ndarray specifying the a-coordinates at which to apply the model. :bxr: | np.arange(-_VF_MAXR,_VF_MAXR+_VF_DELTAR,_VF_DELTAR), optional | Ndarray specifying the b-coordinates at which to apply the model. :make_grid: | True, optional | True: generate a 2d-grid from :axr:, :bxr:. :limit_grid_radius: | 0, optional | A value of zeros keeps grid as specified by axr,bxr. | A value > 0 only keeps (a,b) coordinates within :limit_grid_radius: :color: | 'k', optional | For plotting the vector field. | If :color: == 0, no plot will be generated. Returns: :returns: | If :color: == 0: ndarray of axt,bxt,axr,bxr | Else: handle to axes used for plotting. """ # Generate grid from axr, bxr: if make_grid == True: axr, bxr = generate_grid(ax = axr, bx = bxr, out = 'ax,bx', limit_grid_radius = limit_grid_radius) # Apply model at ref. coordinates: axt,bxt,Cxt,hxt,axr,bxr,Cxr,hxr = apply_poly_model_at_x(poly_model, pmodel,axr,bxr) # Plot vectorfield: if color is not 0: #plt.plot(axr, bxr,'ro',markersize=2) plt.quiver(axr, bxr, axt-axr, bxt-bxr, headlength=1,color = color) plt.xlabel("a'") plt.ylabel("b'") return plt.gca()#plt.show(plot1) else: return axt,bxt,axr,bxr
def plot(self, ylabel='Spectrum', *args, **kwargs): """ Make a plot of the spectral data in SPD instance. Returns: :returns: | handle to current axes. """ plt.plot(self.wl, self.value.T, *args, **kwargs) plt.xlabel('Wavelength (nm)') plt.ylabel(ylabel) return plt.gca()
def plot(self, ylabel='Spectrum', wavelength_bar=True, *args, **kwargs): """ Make a plot of the spectral data in SPD instance. Returns: :returns: | handle to current axes. """ plt.plot(self.wl, self.value.T, *args, **kwargs) if wavelength_bar == True: Smax = np.nanmax(self.value) axh = plot_spectrum_colors(spd=None, spdmax=Smax, axh=plt.gca(), wavelength_height=-0.05) plt.xlabel('Wavelength (nm)') plt.ylabel(ylabel) return plt.gca()
def plot_hue_bins(hbins = 16, start_hue = 0.0, scalef = 100, \ plot_axis_labels = False, bin_labels = '#', plot_edge_lines = True, \ plot_center_lines = False, plot_bin_colors = True, \ axtype = 'polar', ax = None, force_CVG_layout = False): """ Makes basis plot for Color Vector Graphic (CVG). Args: :hbins: | 16 or ndarray with sorted hue bin centers (°), optional :start_hue: | 0.0, optional :scalef: | 100, optional | Scale factor for graphic. :plot_axis_labels: | False, optional | Turns axis ticks on/off (True/False). :bin_labels: | None or list[str] or '#', optional | Plots labels at the bin center hues. | - None: don't plot. | - list[str]: list with str for each bin. | (len(:bin_labels:) = :nhbins:) | - '#': plots number. :plot_edge_lines: | True or False, optional | Plot grey bin edge lines with '--'. :plot_center_lines: | False or True, optional | Plot colored lines at 'center' of hue bin. :plot_bin_colors: | True, optional | Colorize hue bins. :axtype: | 'polar' or 'cart', optional | Make polar or Cartesian plot. :ax: | None or 'new' or 'same', optional | - None or 'new' creates new plot | - 'same': continue plot on same axes. | - axes handle: plot on specified axes. :force_CVG_layout: | False or True, optional | True: Force plot of basis of CVG on first encounter. Returns: :returns: | gcf(), gca(), list with rgb colors for hue bins (for use in other plotting fcns) """ # Setup hbincenters and hsv_hues: if isinstance(hbins, float) | isinstance(hbins, int): nhbins = hbins dhbins = 360 / (nhbins) # hue bin width hbincenters = np.arange(start_hue + dhbins / 2, 360, dhbins) hbincenters = np.sort(hbincenters) else: hbincenters = hbins idx = np.argsort(hbincenters) if isinstance(bin_labels, list) | isinstance(bin_labels, np.ndarray): bin_labels = bin_labels[idx] hbincenters = hbincenters[idx] nhbins = hbincenters.shape[0] hbincenters = hbincenters * np.pi / 180 # Setup hbin labels: if bin_labels is '#': bin_labels = ['#{:1.0f}'.format(i + 1) for i in range(nhbins)] # initializing the figure cmap = None if (ax == None) or (ax == 'new'): fig = plt.figure() newfig = True else: newfig = False rect = [0.1, 0.1, 0.8, 0.8] # setting the axis limits in [left, bottom, width, height] if axtype == 'polar': # the polar axis: if newfig == True: ax = fig.add_axes(rect, polar=True, frameon=False) else: #cartesian axis: if newfig == True: ax = fig.add_axes(rect) if (newfig == True) | (force_CVG_layout == True): # Calculate hue-bin boundaries: r = np.vstack( (np.zeros(hbincenters.shape), scalef * np.ones(hbincenters.shape))) theta = np.vstack((np.zeros(hbincenters.shape), hbincenters)) #t = hbincenters.copy() dU = np.roll(hbincenters.copy(), -1) dL = np.roll(hbincenters.copy(), 1) dtU = dU - hbincenters dtL = hbincenters - dL dtU[dtU < 0] = dtU[dtU < 0] + 2 * np.pi dtL[dtL < 0] = dtL[dtL < 0] + 2 * np.pi dL = hbincenters - dtL / 2 dU = hbincenters + dtU / 2 dt = (dU - dL) dM = dL + dt / 2 # Setup color for plotting hue bins: hsv_hues = hbincenters - 30 * np.pi / 180 hsv_hues = hsv_hues / hsv_hues.max() edges = np.vstack( (np.zeros(hbincenters.shape), dL)) # setup hue bin edges array if axtype == 'cart': if plot_center_lines == True: hx = r * np.cos(theta) hy = r * np.sin(theta) if bin_labels is not None: hxv = np.vstack((np.zeros(hbincenters.shape), 1.3 * scalef * np.cos(hbincenters))) hyv = np.vstack((np.zeros(hbincenters.shape), 1.3 * scalef * np.sin(hbincenters))) if plot_edge_lines == True: hxe = np.vstack( (np.zeros(hbincenters.shape), 1.2 * scalef * np.cos(dL))) hye = np.vstack( (np.zeros(hbincenters.shape), 1.2 * scalef * np.sin(dL))) # Plot hue-bins: for i in range(nhbins): # Create color from hue angle: c = np.abs(np.array(colorsys.hsv_to_rgb(hsv_hues[i], 0.84, 0.9))) #c = [abs(c[0]),abs(c[1]),abs(c[2])] # ensure all positive elements if i == 0: cmap = [c] else: cmap.append(c) if axtype == 'polar': if plot_edge_lines == True: ax.plot(edges[:, i], r[:, i] * 1.2, color='grey', marker='None', linestyle=':', linewidth=3, markersize=2) if plot_center_lines == True: if np.mod(i, 2) == 1: ax.plot(theta[:, i], r[:, i], color=c, marker=None, linestyle='--', linewidth=2) else: ax.plot(theta[:, i], r[:, i], color=c, marker='o', linestyle='-', linewidth=3, markersize=10) if plot_bin_colors == True: bar = ax.bar(dM[i], r[1, i], width=dt[i], color=c, alpha=0.15) if bin_labels is not None: ax.text(hbincenters[i], 1.3 * scalef, bin_labels[i], fontsize=12, horizontalalignment='center', verticalalignment='center', color=np.array([1, 1, 1]) * 0.3) if plot_axis_labels == False: ax.set_xticklabels([]) ax.set_yticklabels([]) else: if plot_edge_lines == True: ax.plot(hxe[:, i], hye[:, i], color='grey', marker='None', linestyle=':', linewidth=3, markersize=2) if plot_center_lines == True: if np.mod(i, 2) == 1: ax.plot(hx[:, i], hy[:, i], color=c, marker=None, linestyle='--', linewidth=2) else: ax.plot(hx[:, i], hy[:, i], color=c, marker='o', linestyle='-', linewidth=3, markersize=10) if bin_labels is not None: ax.text(hxv[1, i], hyv[1, i], bin_labels[i], fontsize=12, horizontalalignment='center', verticalalignment='center', color=np.array([1, 1, 1]) * 0.3) ax.axis(1.1 * np.array( [hxv.min(), hxv.max(), hyv.min(), hyv.max()])) if plot_axis_labels == False: ax.set_xticklabels([]) ax.set_yticklabels([]) else: plt.xlabel("a'") plt.ylabel("b'") plt.plot(0, 0, color='k', marker='o', linestyle=None) return plt.gcf(), plt.gca(), cmap
def plot_ColorVectorGraphic(jabt, jabr, hbins = 16, start_hue = 0.0, scalef = 100, \ plot_axis_labels = False, bin_labels = None, \ plot_edge_lines = True, plot_center_lines = False, \ plot_bin_colors = True, axtype = 'polar', ax = None,\ force_CVG_layout = False): """ Plot Color Vector Graphic (CVG). Args: :jabt: | ndarray with jab data under test SPD :jabr: | ndarray with jab data under reference SPD :hbins: | 16 or ndarray with sorted hue bin centers (°), optional :start_hue: | 0.0, optional :scalef: | 100, optional | Scale factor for graphic. :plot_axis_labels: | False, optional | Turns axis ticks on/off (True/False). :bin_labels: | None or list[str] or '#', optional | Plots labels at the bin center hues. | - None: don't plot. | - list[str]: list with str for each bin. | (len(:bin_labels:) = :nhbins:) | - '#': plots number. :plot_edge_lines: | True or False, optional | Plot grey bin edge lines with '--'. :plot_center_lines: | False or True, optional | Plot colored lines at 'center' of hue bin. :plot_bin_colors: | True, optional | Colorize hue-bins. :axtype: | 'polar' or 'cart', optional | Make polar or Cartesian plot. :ax: | None or 'new' or 'same', optional | - None or 'new' creates new plot | - 'same': continue plot on same axes. | - axes handle: plot on specified axes. :force_CVG_layout: | False or True, optional | True: Force plot of basis of CVG. Returns: :returns: | gcf(), gca(), list with rgb colors for hue bins (for use in other plotting fcns) """ # Plot basis of CVG: figCVG, ax, cmap = plot_hue_bins(hbins=hbins, start_hue=start_hue, scalef=scalef, axtype=axtype, ax=ax, plot_center_lines=plot_center_lines, plot_edge_lines=plot_edge_lines, force_CVG_layout=force_CVG_layout, bin_labels=bin_labels, plot_bin_colors=plot_bin_colors) if cmap == []: cmap = ['k' for i in range(hbins)] if axtype == 'polar': jabr_theta, jabr_r = math.cart2pol(jabr[..., 1:3], htype='rad') jabt_theta, jabt_r = math.cart2pol(jabt[..., 1:3], htype='rad') #ax.quiver(jabrtheta,jabr_r,jabt[...,1]-jabr[...,1], jabt[...,2]-jabr_binned[...,2], color = 'k', headlength=3, angles='uv', scale_units='y', scale = 2,linewidth = 0.5) ax.plot(jabt_theta, jabt_r, color='grey', linewidth=2) for j in range(hbins): c = cmap[j] ax.quiver(jabr_theta[j], jabr_r[j], jabt[j, 1] - jabr[j, 1], jabt[j, 2] - jabr[j, 2], edgecolor='k', facecolor=c, headlength=3, angles='uv', scale_units='y', scale=2, linewidth=0.5) else: #ax.quiver(jabr[...,1],jabr[...,2],jabt[...,1]-jabr[...,1], jabt[...,2]-jabr[...,2], color = 'k', headlength=3, angles='uv', scale_units='xy', scale = 1,linewidth = 0.5) ax.plot(jabt, jabt, color='grey', linewidth=2) for j in range(hbins): ax.quiver(jabr[j, 1], jabr[j, 2], jabt[j, 1] - jabr[j, 1], jabt[j, 2] - jabr[j, 2], color=cmap[j], headlength=3, angles='uv', scale_units='xy', scale=1, linewidth=0.5) if axtype == 'cart': plt.xlabel("a'") plt.ylabel("b'") return plt.gcf(), plt.gca(), cmap
def plot_chromaticity_diagram_colors(diagram_samples = 256, diagram_opacity = 1.0, diagram_lightness = 0.25,\ cieobs = _CIEOBS, cspace = 'Yxy', cspace_pars = {},\ show = True, axh = None,\ show_grid = True, label_fontname = 'Times New Roman', label_fontsize = 12,\ **kwargs): """ Plot the chromaticity diagram colors. Args: :diagram_samples: | 256, optional | Sampling resolution of color space. :diagram_opacity: | 1.0, optional | Sets opacity of chromaticity diagram :diagram_lightness: | 0.25, optional | Sets lightness of chromaticity diagram :axh: | None or axes handle, optional | Determines axes to plot data in. | None: make new figure. :show: | True or False, optional | Invoke matplotlib.pyplot.show() right after plotting :cieobs: | luxpy._CIEOBS or str, optional | Determines CMF set to calculate spectrum locus or other. :cspace: | luxpy._CSPACE or str, optional | Determines color space / chromaticity diagram to plot data in. | Note that data is expected to be in specified :cspace: :cspace_pars: | {} or dict, optional | Dict with parameters required by color space specified in :cspace: | (for use with luxpy.colortf()) :show_grid: | True, optional | Show grid (True) or not (False) :label_fontname: | 'Times New Roman', optional | Sets font type of axis labels. :label_fontsize: | 12, optional | Sets font size of axis labels. :kwargs: | additional keyword arguments for use with matplotlib.pyplot. Returns: """ offset = _EPS ii, jj = np.meshgrid(np.linspace(offset, 1 + offset, diagram_samples), np.linspace(1+offset, offset, diagram_samples)) ij = np.dstack((ii, jj)) SL = _CMF[cieobs]['bar'][1:4].T SL = np.vstack((SL,SL[0])) SL = 100.0*SL/SL[:,1,None] SL = colortf(SL, tf = cspace, tfa0 = cspace_pars) Y,x,y = asplit(SL) SL = np.vstack((x,y)).T ij2D = ij.reshape((diagram_samples**2,2)) ij2D = np.hstack((diagram_lightness*100*np.ones((ij2D.shape[0],1)), ij2D)) xyz = colortf(ij2D, tf = cspace + '>xyz', tfa0 = cspace_pars) xyz[xyz < 0] = 0 xyz[np.isinf(xyz.sum(axis=1)),:] = np.nan xyz[np.isnan(xyz.sum(axis=1)),:] = offset srgb = xyz_to_srgb(xyz) srgb = srgb/srgb.max() srgb = srgb.reshape((diagram_samples,diagram_samples,3)) if show == True: if axh is None: fig = plt.figure() axh = fig.add_subplot(111) polygon = Polygon(SL, facecolor='none', edgecolor='none') axh.add_patch(polygon) image = axh.imshow( srgb, interpolation='bilinear', extent = (0.0, 1, -0.05, 1), clip_path=None, alpha=diagram_opacity) image.set_clip_path(polygon) plt.plot(x,y, color = 'darkgray') if cspace == 'Yxy': plt.xlim([0,1]) plt.ylim([0,1]) elif cspace == 'Yuv': plt.xlim([0,0.6]) plt.ylim([0,0.6]) if (cspace is not None): xlabel = _CSPACE_AXES[cspace][1] ylabel = _CSPACE_AXES[cspace][2] if (label_fontname is not None) & (label_fontsize is not None): plt.xlabel(xlabel, fontname = label_fontname, fontsize = label_fontsize) plt.ylabel(ylabel, fontname = label_fontname, fontsize = label_fontsize) if show_grid == True: plt.grid() #plt.show() return axh else: return None
def plot_color_data(x,y,z=None, axh=None, show = True, cieobs =_CIEOBS, \ cspace = _CSPACE, formatstr = 'k-', **kwargs): """ Plot color data from x,y [,z]. Args: :x: | float or ndarray with x-coordinate data :y: | float or ndarray with y-coordinate data :z: | None or float or ndarray with Z-coordinate data, optional | If None: make 2d plot. :axh: | None or axes handle, optional | Determines axes to plot data in. | None: make new figure. :show: | True or False, optional | Invoke matplotlib.pyplot.show() right after plotting :cieobs: | luxpy._CIEOBS or str, optional | Determines CMF set to calculate spectrum locus or other. :cspace: | luxpy._CSPACE or str, optional | Determines color space / chromaticity diagram to plot data in. | Note that data is expected to be in specified :cspace: :formatstr: | 'k-' or str, optional | Format str for plotting (see ?matplotlib.pyplot.plot) :kwargs: | additional keyword arguments for use with matplotlib.pyplot. Returns: :returns: | None (:show: == True) | or | handle to current axes (:show: == False) """ x = np.atleast_1d(x) y = np.atleast_1d(y) if 'grid' in kwargs.keys(): plt.grid(kwargs['grid']);kwargs.pop('grid') if z is not None: z = np.atleast_1d(z) if axh is None: fig = plt.figure() axh = plt.axes(projection='3d') axh.plot3D(x,y,z,formatstr, linewidth = 2,**kwargs) plt.zlabel(_CSPACE_AXES[cspace][0], kwargs) else: plt.plot(x,y,formatstr,linewidth = 2,**kwargs) plt.xlabel(_CSPACE_AXES[cspace][1], kwargs) plt.ylabel(_CSPACE_AXES[cspace][2], kwargs) if 'label' in kwargs.keys(): plt.legend() if show == True: plt.show() else: return plt.gca()
def plotellipse(v, cspace_in = 'Yxy', cspace_out = None, nsamples = 100, \ show = True, axh = None, \ line_color = 'darkgray', line_style = ':', line_width = 1, line_marker = '', line_markersize = 4,\ plot_center = False, center_marker = 'o', center_color = 'darkgray', center_markersize = 4,\ show_grid = True, label_fontname = 'Times New Roman', label_fontsize = 12,\ out = None): """ Plot ellipse(s) given in v-format [Rmax,Rmin,xc,yc,theta]. Args: :v: | (Nx5) ndarray | ellipse parameters [Rmax,Rmin,xc,yc,theta] :cspace_in: | 'Yxy', optional | Color space of v. | If None: no color space assumed. Axis labels assumed ('x','y'). :cspace_out: | None, optional | Color space to plot ellipse(s) in. | If None: plot in cspace_in. :nsamples: | 100 or int, optional | Number of points (samples) in ellipse boundary :show: | True or boolean, optional | Plot ellipse(s) (True) or not (False) :axh: | None, optional | Ax-handle to plot ellipse(s) in. | If None: create new figure with axes. :line_color: | 'darkgray', optional | Color to plot ellipse(s) in. :line_style: | ':', optional | Linestyle of ellipse(s). :line_width': | 1, optional | Width of ellipse boundary line. :line_marker: | 'none', optional | Marker for ellipse boundary. :line_markersize: | 4, optional | Size of markers in ellipse boundary. :plot_center: | False, optional | Plot center of ellipse: yes (True) or no (False) :center_color: | 'darkgray', optional | Color to plot ellipse center in. :center_marker: | 'o', optional | Marker for ellipse center. :center_markersize: | 4, optional | Size of marker of ellipse center. :show_grid: | True, optional | Show grid (True) or not (False) :label_fontname: | 'Times New Roman', optional | Sets font type of axis labels. :label_fontsize: | 12, optional | Sets font size of axis labels. :out: | None, optional | Output of function | If None: returns None. Can be used to output axh of newly created | figure axes or to return Yxys an ndarray with coordinates of | ellipse boundaries in cspace_out (shape = (nsamples,3,N)) Returns: :returns: None, or whatever set by :out:. """ Yxys = np.zeros((nsamples,3,v.shape[0])) ellipse_vs = np.zeros((v.shape[0],5)) for i,vi in enumerate(v): # Set sample density of ellipse boundary: t = np.linspace(0, 2*np.pi, nsamples) a = vi[0] # major axis b = vi[1] # minor axis xyc = vi[2:4,None] # center theta = vi[-1] # rotation angle # define rotation matrix: R = np.hstack(( np.vstack((np.cos(theta), np.sin(theta))), np.vstack((-np.sin(theta), np.cos(theta))))) # Calculate ellipses: Yxyc = np.vstack((1, xyc)).T Yxy = np.vstack((np.ones((1,nsamples)), xyc + np.dot(R, np.vstack((a*np.cos(t), b*np.sin(t))) ))).T Yxys[:,:,i] = Yxy # Convert to requested color space: if (cspace_out is not None) & (cspace_in is not None): Yxy = colortf(Yxy, cspace_in + '>' + cspace_out) Yxyc = colortf(Yxyc, cspace_in + '>' + cspace_out) Yxys[:,:,i] = Yxy # get ellipse parameters in requested color space: ellipse_vs[i,:] = math.fit_ellipse(Yxy[:,1:]) #de = np.sqrt((Yxy[:,1]-Yxyc[:,1])**2 + (Yxy[:,2]-Yxyc[:,2])**2) #ellipse_vs[i,:] = np.hstack((de.max(),de.min(),Yxyc[:,1],Yxyc[:,2],np.nan)) # nan because orientation is xy, but request is some other color space. Change later to actual angle when fitellipse() has been implemented # plot ellipses: if show == True: if (axh is None) & (i == 0): fig = plt.figure() axh = fig.add_subplot(111) if (cspace_in is None): xlabel = 'x' ylabel = 'y' else: xlabel = _CSPACE_AXES[cspace_in][1] ylabel = _CSPACE_AXES[cspace_in][2] if (cspace_out is not None): xlabel = _CSPACE_AXES[cspace_out][1] ylabel = _CSPACE_AXES[cspace_out][2] if plot_center == True: plt.plot(Yxyc[:,1],Yxyc[:,2],color = center_color, linestyle = 'none', marker = center_marker, markersize = center_markersize) plt.plot(Yxy[:,1],Yxy[:,2],color = line_color, linestyle = line_style, linewidth = line_width, marker = line_marker, markersize = line_markersize) plt.xlabel(xlabel, fontname = label_fontname, fontsize = label_fontsize) plt.ylabel(ylabel, fontname = label_fontname, fontsize = label_fontsize) if show_grid == True: plt.grid() #plt.show() Yxys = np.transpose(Yxys,axes=(0,2,1)) if out is not None: return eval(out) else: return None
def demo_opt(f, args=(), xrange=None, options={}): """ DEMO_OPT: Multi-objective optimization using the DEMO This function uses the Differential Evolution for Multi-objective Optimization (a.k.a. DEMO) to solve a multi-objective problem. The result is a set of nondominated points that are (hopefully) very close to the true Pareto front. Args: :f: | handle to objective function. | The output must be, for each point, a column vector of size m x 1, | with m > 1 the number of objectives. :args: (), optional | Input arguments required for f. :xrange: None, optional | ndarray with lower and upperbounds. | If n is the dimension, it will be a n x 2 matrix, such that the | first column contains the lower bounds, | and the second, the upper bounds. | None defaults to no bounds ( [-Inf, Inf] ndarray). :options: | None, optional | dict with internal parameters of the algorithm. | None initializes default values. | keys: | - 'F': the scale factor to be used in the mutation (default: 0.5); | - 'CR': the crossover factor used in the recombination (def.: 0.3); | - 'mu': the population size (number of individuals) (def.: 100); | - 'kmax': maximum number of iterations (def.: 300); | - 'display': 'on' to display the population while the algorithm is | being executed, and 'off' to not (default: 'off'); | If any of the parameters is not set, the default ones are used | instead. Returns: fopt, xopt :fopt: the m x mu_opt ndarray with the mu_opt best objectives :xopt: the n x mu_opt ndarray with the mu_opt best individuals """ # Initialize the parameters of the algorithm with values contained in dict: options = init_options(options=options) # Initial considerations n = xrange.shape[0] #dimension of the problem P = {'x': np.random.rand(n, options['mu'])} #initial decision variables P['f'] = fobjeval(f, P['x'], args, xrange) #evaluates the initial population m = P['f'].shape[0] #number of objectives k = 0 #iterations counter # Beginning of the main loop Pfirst = P.copy() axh = None while (k <= options['kmax']): # Plot the current population (if desired): if options['display'] == True: if (k == 0) & (m < 4): fig = plt.gcf() fig.show() fig.canvas.draw() if m == 2: # fig = plt.figure() axh = plt.axes() plt.plot(P['f'][0], P['f'][1], 'o') plt.title('Objective values during the execution') plt.xlabel('f_1') plt.ylabel('f_2') fig.canvas.draw() del axh.lines[0] elif m == 3: # fig = plt.figure() axh = plt.axes(projection='3d') # print(P['f']) axh.plot3D(P['f'][0], P['f'][1], P['f'][2], 'o') plt.title('Objective values during the execution') axh.set_xlabel('f_1') axh.set_ylabel('f_2') axh.set_zlabel('f_3') fig.canvas.draw() plt.pause(0.01) del axh.lines[0] # Perform the variation operation (mutation and recombination): O = {'x': mutation(P['x'], options)} #mutation O['x'] = recombination(P['x'].copy(), O['x'], options) #recombination O['x'] = repair( O['x']) #assure the offspring do not cross the search limits O['f'] = fobjeval(f, O['x'], args, xrange) #compute objective functions # Selection and updates P = selection(P, O, options) print('Iteration #{:1.0f} of {:1.0f}'.format(k, options['kmax'])) k += 1 # Return the final population: # First, unnormalize it Xmin = xrange[:, 0][:, None] # * np.ones(options['mu']) #replicate lower bound Xmax = xrange[:, 1][:, None] # * np.ones(options['mu']) #replicate upper limit Xun = (Xmax - Xmin) * P['x'] + Xmin # Then, return the nondominated front: ispar = ndset(P['f']) fopt = P['f'][:, ispar] xopt = Xun[:, ispar] return fopt, xopt