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 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 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 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 make_3DWB_water_profile(final_profile, water_ppmH2O_initial=None, initial_profile=None, initial_area_list=None, initial_area_positions_microns=None, show_plot=True, top=1.2, fig_ax=None): """Take a profile and initial water content. Returns the whole-block water concentration profile based on the profile's attribute wb_areas. If wb_areas have not been made, some initial profile information and various options are passed to make_3DWB_area_profile(). Default makes a plot showing A/Ao and water on parasite y-axis """ fin = final_profile init = initial_profile # Set initial water if water_ppmH2O_initial is not None: w0 = water_ppmH2O_initial else: if fin.sample is not None: if fin.sample.initial_water is not None: w0 = fin.sample.initial_water elif init is not None: if init.sample is not None: if init.sample.initial_water is not None: w0 = init.sample.initial_water else: print 'Need initial water content.' return False # Set whole-block areas if (fin.wb_areas is not None) and (len(fin.wb_areas) > 0): wb_areas = fin.wb_areas else: wb_areas = make_3DWB_area_profile(fin, initial_profile, initial_area_list, initial_area_positions_microns) water = wb_areas * w0 if show_plot is True: # Use a parasite y-axis to show water content fig = plt.figure() ax_areas = SubplotHost(fig, 1, 1, 1) fig.add_subplot(ax_areas) area_tick_marks = np.arange(0, 100, 0.2) ax_areas.set_yticks(area_tick_marks) ax_water = ax_areas.twin() ax_water.set_yticks(area_tick_marks) if isinstance(w0, uncertainties.Variable): ax_water.set_yticklabels(area_tick_marks * w0.n) else: ax_water.set_yticklabels(area_tick_marks * w0) ax_areas.axis["bottom"].set_label('Position ($\mu$m)') ax_areas.axis["left"].set_label('Final area / Initial area') ax_water.axis["right"].set_label('ppm H$_2$O') ax_water.axis["top"].major_ticklabels.set_visible(False) ax_water.axis["right"].major_ticklabels.set_visible(True) ax_areas.grid() ax_areas.set_ylim(0, 1.2) if fin.len_microns is not None: leng = fin.len_microns else: leng = fin.set_len() ax_areas.set_xlim(-leng / 2.0, leng / 2.0) style = fin.choose_marker_style() ax_areas.plot([-leng / 2.0, leng / 2.0], [1, 1], **style_1) ax_areas.plot(fin.positions_microns - leng / 2.0, wb_areas, **style) return water, fig, ax_areas else: return water
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)
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()) #leg.texts[1].set_color(p2.get_color()) #host.yaxis.get_label().set_color(p3.get_color()) #leg.texts[2].set_color(p3.get_color()) #host.yaxis.get_label().set_color(p4.get_color())
ax2_eV_to_Eh = mtransforms.Affine2D().scale(cst.eV_to_Eh, cst.Eh_to_eV) ax2_Eh = ax2_eV.twin(ax2_eV_to_Eh) ax2_Eh .set_viewlim_mode("transform") fig2.add_subplot(ax2_eV) # Plot NumericalHeating as a function of dt for every potential depth dbase_potentials = (base_potentials[-1] - base_potentials[0]) / float(max_plot-1) # -1 since we want the number of intervals c = 0 for j in xrange(len(potential_shapes)): for base_potentials_close in np.arange(base_potentials[-1]+dbase_potentials/2.0, base_potentials[0], -dbase_potentials): nothing, index0 = find_nearest(base_potentials, base_potentials_close) nothing, index1 = find_nearest(potential_shapes, potential_shapes[j]) #nothing, index2 = find_nearest(dts, dts[i]) ax1_eV.plot(dts, NumericalHeating[index0, index1, :], colors_and_symbols.symb_col(c), label = r"" + str(base_potentials[index0]) + " Eh") #label = r"Potential depth: " + str(base_potentials[index0]) + " Eh (" + potential_shapes[j] + " )") # When the NumericalHeating is negative, we have cooling, which would not appear on the log scale plot. cooling_indices = np.where(NumericalHeating[index0, index1, :] <= 0.0) if (len(cooling_indices[0]) >= 1): ax1_eV.plot(dts[cooling_indices], abs(NumericalHeating[index0, index1, :][cooling_indices]), '.' + colors_and_symbols.symb_col(c)) c += 1 # Plot NumericalHeating as a function of potential depth for every dt ddt = (dts[-1] - dts[0]) / float(max_plot-1+1) # -1 since we want the number of intervals # +1 so the skipped dt = 0.005 as does not reduce the number of curves c = 0 for j in xrange(len(potential_shapes)): for dt_close in np.arange(dts[-1]+ddt/2.0, dts[0], -ddt):
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)
# ax_wn.set_xlim(675, 1700) ax_wn.invert_xaxis() # x_wn=np.array([800, 1000, 1200, 1400, 1600]) # ax_wn.set_xticks(x_wn) ax_wn.xaxis.tick_top() 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,
os.chdir(basedir) print(dirlist) offset = 0 x_minimum1 = 675 x_maximum1 = 1700 for i in dirlist: if fnmatch.fnmatch(i, '*.dpt'): datat = np.loadtxt(i, delimiter=',') data2 = nrmlze(datat, 0) mask1 = ((datat[:, 0] >= x_minimum1) & (datat[:, 0] <= x_maximum1)) ax_wn.plot(datat[mask1, 0], data2[mask1] + offset, 'k-', lw=0.5, linestyle='-') offset = offset + 1.15 else: continue os.chdir('../../') ax_wn.set_ylim([-.5, offset + 0.5]) if n == 1: ax_wn.set_ylabel('intensity, a.u.', fontsize=11) if n == 2: ax_mn.set_xlabel('wavelength, $\mu$m', fontsize=11)
#mu_data = np.array(data.mu(), dtype=dtype) #z_data = data[:]['z'] #sigma_data = data[:]['sigma'] #--------------------- #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,mu[0,:],'r-',lw=1.5,label="$\Omega_m = 0.2$") p2 = host.plot(z,mu[1,:],'b--',lw=1.5,label="$\Omega_m = 0.3$") p3 = host.plot(z,mu[2,:],'k-.',lw=1.5,label="$\Omega_m = 0.4$") p4 = host.plot(z,mu[3,:],'m:',lw=1.5,label="$\Omega_m = 0.5$") p5 = host.errorbar(z_data,mu_data,yerr=sigma_data,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()) #leg.texts[1].set_color(p2.get_color()) #host.yaxis.get_label().set_color(p3.get_color())
for i in xrange(nb_particles): if all_particles[i].cs >= 0: str_elem = "i" str_cs = "+" if i == impacting_electron.nearest: symbol = "rs" else: symbol = "ro" else: str_elem = "e" symbol = "bo" str_cs = "" # Potential if plot_V: ax_V_Eh.plot(all_particles[i].pos, all_particles[i].V, symbol) ax_V_Eh.text( all_particles[i].pos, all_particles[i].V, r" $" + str_elem + "_{" + str(i) + "}\ (" + str(all_particles[i].cs) + str_cs + ")$", horizontalalignment="left", ) # Energy # if (plot_U): # ax_U_Eh.plot(all_particles[i].pos, all_particles[i].U(), symbol) # ax_U_Eh.text(all_particles[i].pos, all_particles[i].U(), r' $' + str_elem + '_{' + str(i) + '}\ (' + str(all_particles[i].cs) + str_cs + ')$', horizontalalignment = "center") # ****************************************************************************** # Potential plot # ******************************************************************************
-11.02, -11.1, Jaipur.whatIsD(Celsius, orient='y') ]) # Jaipur diopside fast direction #ax.plot(np.log10(0.016), Jaipur.whatIsD(Celsius, orient='x'), **Jaipur.basestyle) #ax.text(-1.55, -10.8, 'Jaipur diopside\nfast direction', ha='center') # augite PMR-53 #ax.plot(np.log10(0.026), -11.02, **PMR.basestyle) #ax.text(-1.75, -11.3, 'augite\nPMR-53',) # Nushan cpx #ax.plot(np.log10(0.204), Nushan.whatIsD(Celsius, orient='z'), **Nushan.basestyle) ax.plot(np.log10(0.154), Nushan.whatIsD(Celsius, orient='z'), **Nushan.basestyle) ax.text(-0.7, -12.35, unicode('N\374shan\ncpx', 'latin-1'), ha='center') # Fuego cpx #FuegoLoc = (np.log10(0.133), (Jaipur.whatIsD(Celsius, orient='x')+Jaipur.whatIsD(Celsius, orient='y'))/2.) FuegoLoc = (np.log10(0.066), (Jaipur.whatIsD(Celsius, orient='x')+Jaipur.whatIsD(Celsius, orient='y'))/2.) ax.add_artist(Ellipse(FuegoLoc, 0.06, 1., facecolor='none', edgecolor='k')) #ax.text(-0.95, -11.5, 'Fuego\nphenocryst', ha='right') ax.text(-1.225, -10.85, 'Fuego phenocryst', ha='right', rotation=90) # Xenoliths #xenoMin = np.log10(0.11 - 0.035) #xenoMax = np.log10(0.11 + 0.035) xenoMin = np.log10(0.07 - 0.045) xenoMax = np.log10(0.07 + 0.045) xenoDrange = 1.
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)
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]): label[idx] = ''.join( (Names[idx], '\n[Mg# ', '{:.1f}'.format(MgNumber[idx]), ']')) elif 'Fuego' in Names[idx]: label[idx] = ''.join( (Names[idx], '\n[Mg# ', '{:.1f}'.format(MgNumber[idx]), ',\n', '{:.2f}'.format(FeOwt[idx]), '% FeO]')) else: label[idx] = ''.join( (Names[idx], '\n[Mg# ', '{:.1f}'.format(MgNumber[idx]), ', ',
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]): label[idx] = ''.join((Names[idx], '\n[Mg# ', '{:.1f}'.format(MgNumber[idx]), ']')) elif 'Fuego' in Names[idx]: label[idx] = ''.join((Names[idx], '\n[Mg# ', '{:.1f}'.format(MgNumber[idx]), ',\n', '{:.2f}'.format(FeOwt[idx]), '% FeO]')) else: label[idx] = ''.join((Names[idx], '\n[Mg# ', '{:.1f}'.format(MgNumber[idx]), ', ', '{:.2f}'.format(FeOwt[idx]), '% FeO]')) # label = ''.join((Names[idx], '\nMg# ', '{:.1f}'.format(MgNumber[idx]), # '\nFeO wt%: ',