def plotSpectrum(paths): if type(paths) == str: paths = [paths] fig = plt.figure() fig.patch.set_alpha(0) ax1 = SubplotHost(fig, 111) fig.add_subplot(ax1) for p in paths: data = np.loadtxt(p, skiprows=9) ax1.plot(eV_To_nm / data[:, 0], data[:, 1]) ax1.set_ylabel('Intensity (arb. units)') ax2 = ax1.twin() ax1.set_xlabel('Energy (eV)') # ax2 is responsible for "top" axis and "right" axis # ticks = ax1.get_xticks() tticks = np.round(eV_To_nm / ax1.get_xticks(), 2) tticks = np.array(tticks, np.int) ax2.set_xticks([eV_To_nm / t for t in tticks]) ax2.set_xticklabels(tticks) #ax2.axis["top"].label.set_visible(True) ax1.ticklabel_format(axis='y', style='sci', scilimits=(0, 0)) ax2.set_xlabel('Wavelength (nm)') ax2.set_yticks([]) #def main(): # path = input("Enter the path of your file: ") # path=path.replace('"','') # path=path.replace("'",'') ## path = r'C:/Users/sylvain.finot/Documents/data/2019-03-11 - T2597 - 5K/Fil3/TRCL-cw455nm/TRCL.dat' # plotSpectrum(path) # #if __name__ == '__main__': # main()
def plotComponentStress(r, sigmaR, sigmaTheta, sigmaZ, sigmaEq, filename, i, loc): a = r[0,0]; b = r[0,-1] trX = Q_(1, 'inch').to('mm').magnitude trY = Q_(1, 'ksi').to('MPa').magnitude trans = mtransforms.Affine2D().scale(trX,trY) fig = plt.figure(figsize=(4, 3.5)) ax = SubplotHost(fig, 1, 1, 1) axa = ax.twin(trans) axa.set_viewlim_mode("transform") axa.axis["top"].set_label(r'\textsc{radius}, $r$ (in.)') axa.axis["top"].label.set_visible(True) axa.axis["right"].set_label(r'\textsc{stress component}, $\sigma$ (ksi)') axa.axis["right"].label.set_visible(True) ax = fig.add_subplot(ax) ax.plot(r[i,:]*1e3, sigmaR[i,:]*1e-6, '^-', label='$\sigma_r$') ax.plot(r[i,:]*1e3, sigmaTheta[i,:]*1e-6, 'o-', label=r'$\sigma_\theta$') ax.plot(r[i,:]*1e3, sigmaZ[i,:]*1e-6, 'v-', label='$\sigma_z$') ax.plot(r[i,:]*1e3, sigmaEq[i,:]*1e-6, 's-', label='$\sigma_\mathrm{eq}$') ax.set_xlabel(r'\textsc{radius}, $r$ (mm)') ax.set_xlim((a*1e3)-0.1,(b*1e3)+0.1) ax.set_ylabel(r'\textsc{stress component}, $\sigma$ (MPa)') ax.legend(loc=loc) #labels = ax.get_xticklabels() #plt.setp(labels, rotation=30) fig.tight_layout() fig.savefig(filename, transparent=True) plt.close(fig)
def diffusion1D(length_microns, log10D_m2s, time_seconds, init=1., fin=0., erf_or_sum='erf', show_plot=True, style=styles.style_blue, infinity=100, points=100, centered=True, axes=None, symmetric=True, maximum_value=1.): """ Simplest implementation for 1D diffusion. Takes required inputs length, diffusivity, and time and plots diffusion curve on new or specified figure. Optional inputs are unit initial value and final values. Defaults assume diffusion out, so init=1. and fin=0. Reverse these for diffusion in. Change scale of y-values with maximum_value keyword. Returns figure, axis, x vector in microns, and model y data. """ if symmetric is True: params = params_setup1D(length_microns, log10D_m2s, time_seconds, init=init, fin=fin) x_diffusion, y_diffusion = diffusion1D_params(params, points=points) if centered is False: a_length = (max(x_diffusion) - min(x_diffusion)) / 2 x_diffusion = x_diffusion + a_length else: # multiply length by two params = params_setup1D(length_microns*2, log10D_m2s, time_seconds, init=init, fin=fin) x_diffusion, y_diffusion = diffusion1D_params(params, points=points) # divide elongated profile in half x_diffusion = x_diffusion[int(points/2):] y_diffusion = y_diffusion[int(points/2):] if centered is True: a_length = (max(x_diffusion) - min(x_diffusion)) / 2 x_diffusion = x_diffusion - a_length if show_plot is True: if axes is None: fig = plt.figure() ax = SubplotHost(fig, 1,1,1) ax.grid() ax.set_ylim(0, maximum_value) ax.set_xlabel('position ($\mu$m)') ax.set_xlim(min(x_diffusion), max(x_diffusion)) ax.plot(x_diffusion, y_diffusion*maximum_value, **style) ax.set_ylabel('Unit concentration or final/initial') fig.add_subplot(ax) else: axes.plot(x_diffusion, y_diffusion*maximum_value, **style) fig = None ax = None else: fig = None ax = None return fig, ax, x_diffusion, y_diffusion
def plotNACA(r, sigma, fea, i, filename, loc, ylabel): a = r[0,0]; b = r[0,-1] trX = Q_(1, 'inch').to('mm').magnitude trY = Q_(1, 'ksi').to('MPa').magnitude trans = mtransforms.Affine2D().scale(trX,trY) fig = plt.figure(figsize=(4, 3.5)) ax = SubplotHost(fig, 1, 1, 1) axa = ax.twin(trans) axa.set_viewlim_mode("transform") axa.axis["top"].set_label(r'\textsc{radius}, $r$ (in.)') axa.axis["top"].label.set_visible(True) axa.axis["right"].set_label(ylabel+' (ksi)') axa.axis["right"].label.set_visible(True) ax = fig.add_subplot(ax) ax.plot(r[0,:]*1e3, sigma[0,:]*1e-6, '-', color='C0',label=r'$\theta=0^\circ$') ax.plot((a+fea[0][:,0])*1e3, fea[0][:,i]*1e-6, 'o', color='C0', markevery=1) ax.plot(r[0,:]*1e3, sigma[20,:]*1e-6, '-', color='C1', label=r'$\theta=60^\circ$') ax.plot((a+fea[1][:,0])*1e3, fea[1][:,i]*1e-6, '^', color='C1', markevery=1) ax.plot(r[0,:]*1e3, sigma[40,:]*1e-6, '-', color='C2', label=r'$\theta=120^\circ$') ax.plot((a+fea[2][:,0])*1e3, fea[2][:,i]*1e-6, 'v', color='C2', markevery=1) ax.plot(r[0,:]*1e3, sigma[60,:]*1e-6, '-', color='C3', label=r'$\theta=180^\circ$') ax.plot((a+fea[3][:,0])*1e3, fea[3][:,i]*1e-6, 's', color='C3', markevery=1) ax.set_xlabel(r'\textsc{radius}, $r$ (mm)') ax.set_xlim((a*1e3)-10,(b*1e3)+10) ax.set_ylabel(ylabel+' (MPa)') #ax.set_ylim(-400, 400) c0line = Line2D([], [], color='C0', marker='o', label=r'$\theta=0^\circ$') c1line = Line2D([], [], color='C1', marker='^', label=r'$\theta=60^\circ$') c2line = Line2D([], [], color='C2', marker='v', label=r'$\theta=120^\circ$') c3line = Line2D([], [], color='C3', marker='s', label=r'$\theta=180^\circ$') handles=[c0line, c1line, c2line, c3line] labels = [h.get_label() for h in handles] ax.legend([handle for i,handle in enumerate(handles)], [label for i,label in enumerate(labels)], loc=loc) fig.tight_layout() fig.savefig(filename, transparent=True) plt.close(fig)
def plot_diffusion1D(x_microns, model, initial_value=None, fighandle=None, axishandle=None, top=1.2, style=None, fitting=False, show_km_scale=False, show_initial=True): """Takes x and y diffusion data and plots 1D diffusion profile input""" a_microns = (max(x_microns) - min(x_microns)) / 2. a_meters = a_microns / 1e3 if fighandle is None and axishandle is not None: print 'Remember to pass in handles for both figure and axis' if fighandle is None or axishandle is None: fig = plt.figure() ax = SubplotHost(fig, 1, 1, 1) ax.grid() ax.set_ylim(0, top) else: fig = fighandle ax = axishandle if style is None: if fitting is True: style = {'linestyle': 'none', 'marker': 'o'} else: style = styles.style_lightgreen if show_km_scale is True: ax.set_xlabel('Distance (km)') ax.set_xlim(0., 2. * a_meters / 1e3) x_km = x_microns / 1e6 ax.plot((x_km) + a_meters / 1e3, model, **style) else: ax.set_xlabel('position ($\mu$m)') ax.set_xlim(-a_microns, a_microns) ax.plot(x_microns, model, **style) if initial_value is not None and show_initial is True: ax.plot(ax.get_xlim(), [initial_value, initial_value], '--k') ax.set_ylabel('Unit concentration or final/initial') fig.add_subplot(ax) return fig, ax
def plotTIMO(r, s, feaCmp, feaEq, filename): a = r[0,0]; b = r[0,-1] trX = Q_(1, 'inch').to('mm').magnitude trY = Q_(1, 'ksi').to('MPa').magnitude trans = mtransforms.Affine2D().scale(trX,trY) fig = plt.figure(figsize=(4, 3.5)) ax = SubplotHost(fig, 1, 1, 1) axa = ax.twin(trans) axa.set_viewlim_mode("transform") axa.axis["top"].set_label(r'\textsc{radius}, $r$ (in.)') axa.axis["top"].label.set_visible(True) axa.axis["right"].set_label(r'\textsc{stress component}, $\sigma$ (ksi)') axa.axis["right"].label.set_visible(True) ax = fig.add_subplot(ax) ax.plot(r[0,:]*1e3, s.sigmaTheta[0,:]*1e-6, '-', color='C0') ax.plot((a+feaCmp[:,0])*1e3, feaCmp[:,4]*1e-6, 'o', color='C0') ax.plot(r[0,:]*1e3, s.sigmaR[0,:]*1e-6, '-', color='C1') ax.plot((a+feaCmp[:,0])*1e3, feaCmp[:,5]*1e-6, '^', color='C1') ax.plot(r[0,:]*1e3, s.sigmaZ[0,:]*1e-6, '-', color='C2') ax.plot((a+feaCmp[:,0])*1e3, feaCmp[:,6]*1e-6, 'v', color='C2') ax.plot(r[0,:]*1e3, s.sigmaEq[0,:]*1e-6, '-', color='C3') ax.plot((a+feaEq[:,0])*1e3, feaEq[:,1]*1e-6, 's', color='C3') ax.plot(r[0,:]*1e3, s.sigmaRTheta[0,:]*1e-6, '-', color='C4') ax.plot((a+feaCmp[:,0])*1e3, feaCmp[:,7]*1e-6, '+', color='C4') ax.set_xlabel(r'\textsc{radius}, $r$ (mm)') ax.set_xlim((a*1e3)-10,(b*1e3)+10) ax.set_ylabel(r'\textsc{stress component}, $\sigma$ (MPa)') #ax.set_ylim(-400, 400) c0line = Line2D([], [], color='C0', marker='o', label=r'$\sigma_\theta$') c1line = Line2D([], [], color='C1', marker='^', label=r'$\sigma_r$') c2line = Line2D([], [], color='C2', marker='v', label=r'$\sigma_z$') c3line = Line2D([], [], color='C3', marker='s', label=r'$\sigma_\mathrm{eq}$') c4line = Line2D([], [], color='C4', marker='+', label=r'$\tau_{r\theta}$') handles=[c0line, c1line, c2line, c4line, c3line] labels = [h.get_label() for h in handles] ax.legend([handle for i,handle in enumerate(handles)], [label for i,label in enumerate(labels)], loc='best') fig.tight_layout() fig.savefig(filename, transparent=True) plt.close(fig)
def plot_area_profile_outline(centered=True, peakwn=None, set_size=(6.5, 4), ytop=1.2, wholeblock=False, heights_instead=False, show_water_ppm=True): """ Set up area profile outline and style defaults. Default is for 0 to be the middle of the profile (centered=True). """ fig = plt.figure(figsize=set_size) ax = SubplotHost(fig, 1,1,1) fig.add_subplot(ax) ax_ppm = ax.twinx() ax_ppm.axis["top"].major_ticklabels.set_visible(False) if show_water_ppm is True: pass else: ax_ppm.axis["right"].major_ticklabels.set_visible(False) ax.set_xlabel('Position ($\mu$m)') # Set y-label if wholeblock is True: if heights_instead is False: ax.set_ylabel('Area/Area$_0$') else: ax.set_ylabel('Height/Height$_0$') else: if heights_instead is False: ax.set_ylabel('Area (cm$^{-2}$)') else: ax.set_ylabel('Height (cm$^{-1}$)') ax.set_ylim(0, ytop) ax.grid() return fig, ax, ax_ppm
gs = gridspec.GridSpec(1, 1) #ax = plt.subplot(gs[0, 0]) ax = SubplotHost(fig, 1, 1, 1) fig.add_subplot(ax) ax.set_ylim(-17, -10) ax_Fe = ax.twin() Fe_labels = list(np.arange(0.0, 0.1, 0.02)) + list(np.arange(0.1, 0.9, 0.1)) parasite_tick_locations = np.log10(Fe_labels) ax_Fe.set_xticks(parasite_tick_locations) ax_Fe.set_xticklabels(Fe_labels) ax_Fe.axis["top"].set_label("Fe (a.p.f.u.)") ax_Fe.axis["top"].label.set_visible(True) ax_Fe.axis["right"].major_ticklabels.set_visible(False) ax.set_ylabel('log$_{10}$ diffusivity$_{H}$ $(m^2/s)$ at 800 $\degree$C') ax.set_xlabel('log$_{10}$ Fe (a.p.f.u.)') label = ['name'] * 10 #for idx in range(len(Names)): for idx in [0, 1, 2, 3, 4, 6, 7]: ax.plot( x[idx], bulk[idx], #label=Names[idx], clip_on=False, **style[idx]) # individual labels xyloc = (x[idx], bulk[idx]) if np.isnan(FeOwt[idx]):
gs = gridspec.GridSpec(1,1) ax = SubplotHost(fig, 1,1,1) fig.add_subplot(ax) ax.set_ylim(-12.5, -10.5) ax_Al = ax.twin() #Al_labels = list(np.arange(0.0, 0.1, 0.02)) + list(np.arange(0.1, 0.9, 0.1)) Al_labels = [0.01, 0.02, 0.05, 0.1, 0.2] parasite_tick_locations = np.log10(Al_labels) ax_Al.set_xticks(parasite_tick_locations) ax_Al.set_xticklabels(Al_labels) ax_Al.axis["top"].set_label("IV-Al (a.p.f.u.)") ax_Al.axis["top"].label.set_visible(True) ax_Al.axis["right"].major_ticklabels.set_visible(False) ax.set_ylabel('log$_{10}$ diffusivity$_{H}$ $(m^{-2}/s)$ at 800 $\degree$C') ax.set_xlabel('log$_{10}$ IV-Al (a.p.f.u.)') #ax.set_xlim(-2.0, -0.4) ax.set_xlim(-1.7, -0.5) Names = ['Jaipur diopside\n(|| a, c*)', unicode('N\374shan cpx', 'latin-1'), 'augite PMR-53', 'Fuego\nphenocryst', 'Jaipur diopside\n(|| b)', ] # original convention from normalization spreadsheet from SERC website # http://serc.carleton.edu/research_education/equilibria/mineralformulaerecalculation.html Al = np.array([ 0.016, # Jaipur
def plot_em(ifile, lf_file, cmd_file, age, z, track): print 'Plotting', ifile logL, logTe, mbol, j, k, mcore, co, dmdt = np.loadtxt(ifile, usecols=(4, 5, 10, 11, 13, 14, 15, 18), unpack=True) nAGB = (mcore == 0) cAGB = ((co >= 1) & (dmdt <= -5)) oAGB = ((co <= 1) & (logL >= 3.3) & (dmdt < -5)) jk = j - k bins = np.arange(-10, 20, 0.1) ###### HRD fig = plt.figure() ax = fig.add_axes([.1, .1, .8, .8]) ax.plot(logTe[nAGB], logL[nAGB], '.k') ax.plot(logTe[cAGB], logL[cAGB], 'o', mfc='None', ms=5, mew=1, mec=colorC, alpha=0.3) ax.plot(logTe[oAGB], logL[oAGB], 'o', mfc='None', ms=5, mew=1, mec=colorO, alpha=0.3) ax.annotate('Age=%.2e' % age, (.7, .1), va='center', xycoords='axes fraction') ax.annotate('Z=%.2e' % z, (.7, .15), va='center', xycoords='axes fraction') ax.annotate('[M/H]=%.2f' % ztomh(z), (.7, .2), va='center', xycoords='axes fraction') ax.annotate(r'$%s$' % track.replace('_', '\ '), (.1, .9), va='center', xycoords='axes fraction') ax.set_xlim(ax.get_xlim()[::-1]) #ax.set_ylim(-3.1, -9.5) ax.set_xlabel(r'$\log\ T_{\\eff}$') ax.set_ylabel(r'$\log L$') plt.savefig(cmd_file.replace('cmd', 'hrd')) ###### CMD fig = plt.figure() ax = fig.add_axes([.1, .1, .8, .8]) ax.plot(jk[nAGB], k[nAGB], '.k') ax.plot(jk[cAGB], k[cAGB], 'o', mfc='None', ms=5, mew=1, mec=colorC, alpha=0.3) ax.plot(jk[oAGB], k[oAGB], 'o', mfc='None', ms=5, mew=1, mec=colorO, alpha=0.3) ax.annotate('Age=%.2e' % age, (.7, .1), va='center', xycoords='axes fraction') ax.annotate('Z=%.2e' % z, (.7, .15), va='center', xycoords='axes fraction') ax.annotate('[M/H]=%.2f' % ztomh(z), (.7, .2), va='center', xycoords='axes fraction') ax.annotate(r'$%s$' % track.replace('_', '\ '), (.1, .9), va='center', xycoords='axes fraction') ax.set_xlim(.1, 2.4) ax.set_ylim(-3.1, -9.5) ax.set_xlabel(r'$J-K$') ax.set_ylabel(r'$K$') plt.savefig(cmd_file) plt.close() ###### LF fig = plt.figure() ax1 = SubplotHost(fig, 2, 1, 1) ax2 = SubplotHost(fig, 2, 1, 2) fig.add_subplot(ax1) fig.add_subplot(ax2) aux_trans = mtransforms.Affine2D().scale(-2.5, 1.).translate(4.77, 0) ax_logl = ax1.twin(aux_trans) ax_logl.set_viewlim_mode('transform') if sum(cAGB): pdf, bins, patches = ax1.hist(mbol[cAGB], bins, histtype='stepfilled', color=colorC) ax1.set_ylim(0.01, pdf.max()*1.1) if sum(oAGB): pdf, bins, patches = ax2.hist(mbol[oAGB], bins, histtype='stepfilled', color=colorO) ax2.set_ylim(0.01, pdf.max() * 1.1) ax1.annotate('Age=%.2e' % age, (.7, .1), va='center', xycoords='axes fraction') ax1.annotate('Z=%.2e' % z, (.7, .2), va='center', xycoords='axes fraction') ax1.annotate('[M/H]=%.2f' % ztomh(z), (.7, .3), va='center', xycoords='axes fraction') ax1.annotate('C-rich', (.7, .8), va='center', xycoords='axes fraction') ax2.annotate('O-rich', (.7, .8), va='center', xycoords='axes fraction') ax2.annotate(r'$%s$' % track.replace('_', '\ '), (.1, .8), va='center', xycoords='axes fraction') for ax in [ax1, ax2]: ax.set_xlim(0.2, -7.2) ax.axis['left'].set_label('N') #ax1.set_xlabel('Log L/L_sun') ax1.set_ylabel(r'$N$') ax2.set_ylabel(r'$N$') ax_logl.axis['right'].major_ticklabels.set_visible(False) ax1.axis['bottom'].major_ticklabels.set_visible(False) ax2.axis['bottom'].set_label('M$_\mathsf{bol}$') ax_logl.annotate(r'$\log L/L_\mathsf{sun}$', (.5, 1.1), xycoords='axes fraction') #ax_logl.axis['top'].set_label('log L/L$_{\odot}$') fig.subplots_adjust(left=.1, bottom=None, right=0.98, top=None, wspace=0, hspace=0) plt.savefig(lf_file) plt.close()
for j in range (shape_z): mu[j] = cosmo.dist_modulus(z[j],Omega_m,(1.-Omega_m),h) + error[j] #generating the fitting function for j in range (shape_z_ana): mu_ana[j] = cosmo.dist_modulus(z_ana[j],Omega_m,(1.-Omega_m),h) #--------------------- #Plotting the analytical models and the data #------------------- fig = pl.figure() host = SubplotHost(fig, 1,1,1) host.set_xlabel('$z$',fontsize=21) host.set_ylabel('$\mu$',fontsize=21) fig.add_subplot(host) p1 = host.plot(z_ana,mu_ana,'r-',lw=1.5,label="$\Omega_m = 0.3$") p2 = host.errorbar(z,mu,yerr=0.1,fmt='o',color='k',lw=1.5,label="SN data") leg = pl.legend(loc=4,fontsize=18) #host.set_ylim(0,48) #pl.xticks(visible=False) #pl.yticks(visible=False) #host.yaxis.get_label().set_color(p1.get_color()) #leg.texts[0].set_color(p1.get_color()) #host.yaxis.get_label().set_color(p2.get_color())
ax_wn.tick_params(axis='x', direction = 'in', labelsize=11) ax_wn.tick_params(axis='y', left=False, labelleft=False) ax_wn.xaxis.set_label_position('top') ax_mn.xaxis.tick_bottom() ax_mn.xaxis.set_label_position('bottom') print('load data') datat=np.loadtxt('dpt_files/Chrysene/p1_08_chry_v2.dpt', delimiter = ',') data2=nrmlze(datat, 0) ax_wn.plot(datat[:,0], data2 + offset, 'k-', lw=0.5, linestyle='-') offset=offset+1.15 ax_wn.set_ylim([-.5, offset+0.5]) ax_wn.set_ylabel('intensity, a.u.', fontsize=11) ax_mn.set_xlabel('wavelength, $\mu$m', fontsize=11) ax_wn.set_xlabel('wavenumber, cm$^{-1}$', fontsize=11) #fig.suptitle('wavenumber, cm$^{-1}$', fontsize=11) #plt.subplots_adjust(top=0.88, #bottom=0.11, #left=0.035, #right=0.965, #hspace=0.2, #wspace=0.12) plt.draw() plt.show()
def plot(self, r1=None, r2=None, nav_im=None, norm='log', scroll_step=1, alpha=0.3, cmap=None, pct=0.1, mradpp=None, widget=None): ''' Interactive plotting of the virtual aperture images. The sliders control the parameters and may be clicked, dragged or scrolled. Clicking on inner (r1) and outer (r2) slider labels sets the radii values to the minimum and maximum, respectively. Parameters ---------- r1 : scalar Inner radius of aperture in pixels. r2 : scalar Inner radius of aperture in pixels. nav_im : None or ndarray Image used for the navigation plot. If None, a blank image is used. norm : None or string: If not None and norm='log', a logarithmic cmap normalisation is used. scroll_step : int Step in pixels used for each scroll event. alpha : float Alpha for aperture plot in [0, 1]. cmap : None or a matplotlib colormap If not None, the colormap used for both plots. pct : scalar Slice image percentile in [0, 50). mradpp : None or scalar mrad per pixel. widget : Pop_Up_Widget A custom class consisting of mutliple widgets ''' from matplotlib.widgets import Slider self._scroll_step = max([1, int(scroll_step)]) self._pct = pct if norm is not None: if norm.lower() == 'log': from matplotlib.colors import LogNorm norm = LogNorm() # condition rs if r1 is not None: self.r1 = r1 else: if self.r1 is None: self.r1 = 0 if r2 is not None: self.r2 = r2 else: if self.r2 is None: self.r2 = int((self.data_shape[-2:] / 4).mean()) self.rc = (self.r2 + self.r1) / 2.0 if nav_im is None: nav_im = np.zeros(self.data_shape[-2:]) # calculate data virtual_image = self.annular_slice(self.r1, self.r2) print("MRADPP", mradpp) # prepare plots if mradpp is None: if widget is not None: print("True") docked = widget.setup_docking("Virtual Annular", "Bottom", figsize=(8.4, 4.8)) fig = docked.get_fig() fig.clf() (ax_nav, ax_cntrst) = fig.subplots(1, 2) self._f_nav = fig else: self._f_nav, (ax_nav, ax_cntrst) = plt.subplots(1, 2, figsize=(8.4, 4.8)) else: # add 2nd x-axis # https://matplotlib.org/examples/axes_grid/parasite_simple2.html from mpl_toolkits.axes_grid1.parasite_axes import SubplotHost import matplotlib.transforms as mtransforms if widget is not None: print("False") docked = widget.setup_docking("Virtual Annular", "Bottom", figsize=(8.4, 4.8)) self._f_nav = docked.get_fig() self._f_nav.clf() else: self._f_nav = plt.figure(figsize=(8.4, 4.8)) ax_nav = SubplotHost(self._f_nav, 1, 2, 1) ax_cntrst = SubplotHost(self._f_nav, 1, 2, 2) aux_trans = mtransforms.Affine2D().scale(1.0 / mradpp, 1.0) ax_mrad = ax_cntrst.twin(aux_trans) ax_mrad.set_viewlim_mode("transform") self._f_nav.add_subplot(ax_nav) self._f_nav.add_subplot(ax_cntrst) ax_mrad.axis["top"].set_label('mrad') ax_mrad.axis["top"].label.set_visible(True) ax_mrad.axis["right"].major_ticklabels.set_visible(False) self._f_nav.subplots_adjust(bottom=0.3, wspace=0.3) if widget is not None: axr1 = fig.add_axes([0.10, 0.05, 0.80, 0.03]) axr2 = fig.add_axes([0.10, 0.10, 0.80, 0.03]) axr3 = fig.add_axes([0.10, 0.15, 0.80, 0.03]) else: axr1 = plt.axes([0.10, 0.05, 0.80, 0.03]) axr2 = plt.axes([0.10, 0.10, 0.80, 0.03]) axr3 = plt.axes([0.10, 0.15, 0.80, 0.03]) val_max = self.r_pix.max() try: self._sr1 = Slider(axr1, 'r1', 0, val_max - 1, valinit=self.r1, valfmt='%0.0f', valstep=1) self._sr2 = Slider(axr2, 'r2', 1, val_max, valinit=self.r2, valfmt='%0.0f', valstep=1) except AttributeError: self._sr1 = Slider(axr1, 'r1', 0, val_max - 1, valinit=self.r1, valfmt='%0.0f') self._sr2 = Slider(axr2, 'r2', 1, val_max, valinit=self.r2, valfmt='%0.0f') self._sr3 = Slider(axr3, 'rc', 1, val_max, valinit=self.rc, valfmt='%0.1f') # these don't seem to work #self._sr1.slider_max = self._sr2 #self._sr2.slider_min = self._sr1 self._sr1.on_changed(self._update_r_from_slider) self._sr2.on_changed(self._update_r_from_slider) self._sr3.on_changed(self._update_rc_from_slider) ax_nav.imshow(nav_im, norm=norm, cmap=cmap) ax_nav.set_xlabel('Detector X (pixels)') ax_nav.set_ylabel('Detector Y (pixels)') # line plot r_cntrst_max = int(np.abs(self.data_shape[-2:] - self.cyx).max()) dw = 1 rs = np.arange(dw, r_cntrst_max) r1, r2 = self.r1, self.r2 sls = np.array([self.annular_slice(r - dw, r) for r in rs]) self.r1, self.r2 = r1, r2 self._contrast_y = np.std(sls, (1, 2))**2 / np.mean(sls, (1, 2)) self._contrast_x = rs - dw / 2.0 ax_cntrst.plot(self._contrast_x, self._contrast_y) ax_cntrst.minorticks_on() ax_cntrst.set_xlabel('Radius (pixels)') ax_cntrst.set_ylabel('Contrast (std^2/mean)') self._span = ax_cntrst.axvspan(self.r1, self.r2, color=[1, 0, 0, 0.1], ec='r') # wedges fc = [0, 0, 0, alpha] ec = 'r' from matplotlib.patches import Wedge self._rmax = val_max + 1 self._w2 = Wedge(self.cyx[::-1], self._rmax, 0, 360, width=self._rmax - self.r2, fc=fc, ec=ec) self._w1 = Wedge(self.cyx[::-1], self.r1, 0, 360, width=self.r1, fc=fc, ec=ec) ax_nav.add_artist(self._w2) ax_nav.add_artist(self._w1) if widget is not None: docked = widget.setup_docking("Virtual Annular", "Bottom", figsize=(8.4, 4.8)) fig = docked.get_fig() fig.clf() ax_im = fig.subplots(1, 1) self._f_im = fig else: self._f_im, ax_im = plt.subplots(1, 1) vmin, vmax = np.percentile(virtual_image, [self._pct, 100 - self._pct]) self._vim = ax_im.imshow(virtual_image, cmap=cmap, vmin=vmin, vmax=vmax) if widget is not None: self._cb = fig.colorbar(self._vim) else: self._cb = plt.colorbar(self._vim) self._cb.set_label('Counts') ax_im.set_xlabel('Scan X (pixels)') ax_im.set_ylabel('Scan Y (pixels)') cid = self._f_nav.canvas.mpl_connect('scroll_event', self._onscroll) self._sr1.label.set_picker(True) self._sr2.label.set_picker(True) cid_pick = self._f_nav.canvas.mpl_connect('pick_event', self._onpick)
figs = [fig1] figs_N = 2 fig1_y = 1 fig2_y = 2 plt.subplots_adjust(hspace=0.0) axprops = dict() if plot_V: ax_V_Eh = SubplotHost(fig1, figs_N, 1, fig1_y, **axprops) ax_V_Eh_to_Volt = mtransforms.Affine2D().scale(1.0, cst.eV_to_Eh) ax_V_Volt = ax_V_Eh.twin(ax_V_Eh_to_Volt) ax_V_Volt.set_viewlim_mode("transform") ax_V_Eh.grid(True) ax_V_Volt.set_ylabel("Potential (Volt)") ax_V_Eh.set_ylabel("Potential energy of a 1+ (Hartree)") axprops["sharex"] = ax_V_Volt plt.setp(ax_V_Volt.get_xticklabels(), visible=False) # ax_V_Eh.set_title(r"Potential") if plot_U: ax_U_Eh = SubplotHost(fig2, figs_N, 1, fig2_y, **axprops) ax_U_Eh_to_eV = mtransforms.Affine2D().scale(1.0, cst.eV_to_Eh) ax_U_eV = ax_U_Eh.twin(ax_U_Eh_to_eV) ax_U_eV.set_viewlim_mode("transform") ax_U_Eh.grid(True) ax_U_Eh.set_xlabel("Position (Bohr)")