On Sunday, April 3, 2016 at 2:35:58 PM UTC-7, Oscar Benjamin wrote: > On 3 Apr 2016 22:21, "Muhammad Ali" <muhammadaliask...@gmail.com> wrote: > > > > How do I convert/change/modify python script so that my data could be > extracted according to python script and at the end it generates another > single extracted data file instead of displaying/showing some graph? So > that, I can manually plot the newly generated file (after data extraction) > by some other software like origin. > > It depends what you're computing and what format origin expects the data to > be in. Presumably it can use CSV files so take a look at the CSV module > which can write these. > > (You'll get better answers to a question like this if you show us some code > and ask a specific question about how to change it.) > > -- > Oscar
How could the python script be modified to generate data file rather than display a plot by using matplotlib? def make_plot(plot): indent = plot.plot_options.indent args = plot.plot_options.args # Creating the plot print ('Generating the plot...') fig = plt.figure(figsize=(plot.fig_width_inches,plot.fig_height_inches)) ax = fig.add_subplot(111) # Defining the color schemes. print (indent + '>>> Using the "' + plot.cmap_name + '" colormap.') if(plot.plot_options.using_default_cmap and not args.running_from_GUI): print (2 * indent + 'Tip: You can try different colormaps by either:') print (2 * indent + ' * Running the plot tool with the option -icmap n, ' \ 'with n in the range from 0 to', len(plot.plot_options.cmaps) - 1) print (2 * indent + ' * Running the plot tool with the option "-cmap cmap_name".') print (2 * indent + '> Take a look at') print (4 * indent + '<http://matplotlib.org/examples/color/colormaps_reference.html>') print (2 * indent + ' for a list of colormaps, or run') print (4 * indent + '"./plot_unfolded_EBS_BandUP.py --help".') # Building the countour plot from the read data # Defining the (ki,Ej) grid. if(args.interpolation is not None): ki = np.linspace(plot.kmin, plot.kmax, 2 * len(set(plot.KptsCoords)) + 1, endpoint=True) Ei = np.arange(plot.emin, plot.emax + plot.dE_for_hist2d, plot.dE_for_hist2d) # Interpolating grid_freq = griddata((plot.KptsCoords, plot.energies), plot.delta_Ns, (ki[None,:], Ei[:,None]), method=args.interpolation, fill_value=0.0) else: ki = np.unique(np.clip(plot.KptsCoords, plot.kmin, plot.kmax)) Ei = np.unique(np.clip(plot.energies, plot.emin, plot.emax)) grid_freq = griddata((plot.KptsCoords, plot.energies), plot.delta_Ns, (ki[None,:], Ei[:,None]), method='nearest', fill_value=0.0) if(not args.skip_grid_freq_clip): grid_freq = grid_freq.clip(0.0) # Values smaller than zero are just noise. # Normalizing and building the countour plot manually_normalize_colorbar_min_and_maxval = False if((args.maxval_for_colorbar is not None) or (args.minval_for_colorbar is not None)): manually_normalize_colorbar_min_and_maxval = True args.disable_auto_round_vmin_and_vmax = True maxval_for_colorbar = args.maxval_for_colorbar minval_for_colorbar = args.minval_for_colorbar else: if not args.disable_auto_round_vmin_and_vmax: minval_for_colorbar = float(round(np.min(grid_freq))) maxval_for_colorbar = float(round(np.max(grid_freq))) args.round_cb = 0 if(manually_normalize_colorbar_min_and_maxval or not args.disable_auto_round_vmin_and_vmax): modified_vmin_or_vmax = False if not args.disable_auto_round_vmin_and_vmax and not args.running_from_GUI: print (plot.indent + '* Automatically renormalizing color scale '\ '(you can disable this with the option --disable_auto_round_vmin_and_vmax):') if manually_normalize_colorbar_min_and_maxval: print (plot.indent + '* Manually renormalizing color scale') if(minval_for_colorbar is not None): previous_vmin = np.min(grid_freq) if(abs(previous_vmin - minval_for_colorbar) >= 0.1): modified_vmin_or_vmax = True print (2 * indent + 'Previous vmin = %.1f, new vmin = %.1f' % (previous_vmin, minval_for_colorbar)) else: minval_for_colorbar = np.min(grid_freq) if(maxval_for_colorbar is not None): previous_vmax = np.max(grid_freq) if(abs(previous_vmax - maxval_for_colorbar) >= 0.1): modified_vmin_or_vmax = True print (2 * indent + 'Previous vmax = %.1f, new vmax = %.1f' % (previous_vmax, maxval_for_colorbar)) else: maxval_for_colorbar = np.max(grid_freq) if(modified_vmin_or_vmax): print (2 * indent + 'The previous vmin and vmax might be slightly different from ' 'the min and max delta_Ns ' 'due to the interpolation scheme used for the plot.') # values > vmax will be set to vmax, and #<vmin will be set to vmin grid_freq = grid_freq.clip(minval_for_colorbar, maxval_for_colorbar) v = np.linspace(minval_for_colorbar, maxval_for_colorbar, args.n_levels, endpoint=True) else: v = np.linspace(np.min(grid_freq), np.max(grid_freq), args.n_levels, endpoint=True) print (indent + '* Drawing contour plot...') print (2 * indent + '> Using %i color levels. Use the option "--n_levels" to choose a different number.' %args.n_levels) image = ax.contourf(ki, Ei, grid_freq, levels=v, cmap=plot.cmap) plot_spin_proj_requested = args.plot_spin_perp or args.plot_spin_para or args.plot_sigma_x or args.plot_sigma_y or args.plot_sigma_z if(plot_spin_proj_requested and plot.spin_projections is not None): print (indent + '* Drawing spin projection info') cmap_for_spin_plot = [plt.cm.bwr, plt.cm.RdBu, plt.cm.seismic_r][0] if(args.clip_spin is None): vmin_spin = np.min(plot.spin_projections) vmax_spin = np.max(plot.spin_projections) else: vmax_spin = abs(args.clip_spin) vmin_spin = -1.0 * abs(args.clip_spin) print (2 * indent + '* New maxval for spin: %.2f' % vmax_spin) print (2 * indent + '* New minval for spin: %.2f' % vmin_spin) spin_projections = np.clip(plot.spin_projections, vmin_spin, vmax_spin) grid_freq_spin = griddata((plot.KptsCoords, plot.energies), spin_projections, (ki[None,:], Ei[:,None]), method='nearest', fill_value=0.0) k_for_scatter = [] E_for_scatter = [] spin_projections_for_scatter = [] for iener in range(len(Ei)): for ikpt in range(len(ki)): if(abs(grid_freq_spin[iener, ikpt]) > 1E-3): k_for_scatter.append(ki[ikpt]) E_for_scatter.append(Ei[iener]) spin_projections_for_scatter.append(grid_freq_spin[iener, ikpt]) if(spin_projections_for_scatter): if(args.spin_marker=='o'): image2 = ax.scatter(k_for_scatter, E_for_scatter, marker='o', s=[10.0 * abs(item) for item in spin_projections_for_scatter], c=spin_projections_for_scatter, cmap=cmap_for_spin_plot) else: image2 = ax.scatter(k_for_scatter, E_for_scatter, marker='_', s=[500.0 * (ki[1] - ki[0]) for item in spin_projections_for_scatter], linewidth=[100.0 * plot.dE_for_hist2d * (item ** 2) for item in spin_projections_for_scatter], c=spin_projections_for_scatter, cmap=cmap_for_spin_plot) else: print (2 * indent + '* The abs values of the spin projections were all < 1E-3.') #Preparing the plot ax.set_xlim(plot.kmin, plot.kmax) ax.set_ylim(plot.emin, plot.emax) ax.set_title(plot.title, fontsize=plot.title_size) ax.set_ylabel(plot.y_axis_label, fontsize=plot.yaxis_labels_size) plt.yticks(fontsize=plot.tick_marks_size) # Fermi energy line show_E_f = not args.no_ef if(show_E_f and plot.E_f >= plot.emin and plot.E_f <= plot.emax): E_f_line = plt.axhline(y=plot.E_f, c=plot.color_E_f_line(image), linestyle=plot.line_style_E_f, lw=plot.line_width_E_f) # High symmetry points lines if(plot.pos_high_symm_points): x_tiks_positions = [kx for kx in plot.pos_high_symm_points if kx - plot.kmax <= 1E-2 and kx >= plot.kmin] if(args.no_symm_labels): x_tiks_labels = [] else: x_tiks_labels = [plot.labels_high_symm_lines[i] for i in range(len(plot.labels_high_symm_lines)) if plot.pos_high_symm_points[i] in x_tiks_positions] x_tiks_labels = [xlabel for xlabel in x_tiks_labels if xlabel] if x_tiks_labels: print (indent + '* K-point labels read from the "' + args.kpoints_file + '" file:') for ilabel in range(len(x_tiks_labels)): print(2 * indent + "k = {:9.5f}".format(x_tiks_positions[ilabel]) + ', label =',\ x_tiks_labels[ilabel]) plt.xticks(x_tiks_positions, x_tiks_labels, fontsize=plot.tick_marks_size) else: plot.x_axis_label = '$k \hspace{0.25} (\AA^{-1})$' plt.locator_params(axis = 'x', nbins = 5) ax.set_xlabel(plot.x_axis_label, fontsize=plot.xaxis_labels_size) plt.xticks(fontsize=plot.tick_marks_size) ax.tick_params(axis='x', pad=10) # Drawing vertical lines at the positions of the high-symmetry points if(not args.no_symm_lines): for line_position in [pos for pos in plot.pos_high_symm_points if float(round(pos, 3)) > float(round(plot.kmin, 3)) and float(round(pos, 3)) < float(round(plot.kmax, 3))]: hs_lines = plt.axvline(x=line_position, c=plot.color_high_symm_lines(image), linestyle=plot.line_style_high_symm_points, lw=plot.line_width_high_symm_points) # Color bar show_colorbar = not args.no_cb if show_colorbar: if plot.cb_orientation=='vertical': cb_pad=0.005 else: cb_pad=0.06 if(not x_tiks_labels): cb_pad += 0.08 # To prevent the cb from overlapping with the numbers. cb_yticks = np.arange(int(image.norm.vmin), int(image.norm.vmax) + 1, 1) cb_ytick_labels = [round(item,abs(args.round_cb)) for item in cb_yticks] cb = plt.colorbar(image, ax=ax, ticks=cb_yticks, orientation=plot.cb_orientation, pad=cb_pad) cb.set_ticklabels(cb_ytick_labels) cb.ax.tick_params(labelsize=plot.colorbar_tick_marks_size) color_bar_label = None if args.cb_label: color_bar_label = ('$Color scale: \hspace{0.5} \delta N(\\vec{k}; ' + '\hspace{0.25} \epsilon)$ ') if args.cb_label_full: color_bar_label = ('$Colors cale: \hspace{0.5} \delta N(\\vec{k}; ' + '\hspace{0.25} \epsilon);$ '+ '$\delta\epsilon=' + round(1000.0*plot.dE_for_hist2d,0) + '\\hspace{0.25} meV.$') if plot.cb_orientation=='vertical': cb_label_rotation = 90 else: cb_label_rotation = 0 if color_bar_label: cb.ax.text(plot.offset_x_text_colorbar, plot.offset_y_text_colorbar, color_bar_label, rotation=cb_label_rotation, ha='center', va='center', fontsize=plot.colorbar_label_size) # Saving/showing the results plt.tick_params(which='both', bottom='off', top='off', left='off', right='off', labelbottom='on') default_out_basename = "_".join([splitext(basename(args.input_file))[0], 'E_from', str(plot.emin), 'to', str(plot.emax), 'eV_dE', str(plot.dE_for_hist2d), 'eV']) if(args.save): if(args.output_file is None): args.output_file = abspath(default_out_basename + '.' + args.file_format) print ('Savig figure to file "%s" ...' % args.output_file) if(args.fig_resolution[0].upper() == 'H'): print (indent + '* High-resolution figure (600 dpi).') fig_resolution_in_dpi = 600 elif (args.fig_resolution[0].upper() == 'M'): print (indent + '* Medium-resolution figure (300 dpi).') fig_resolution_in_dpi = 300 elif (args.fig_resolution[0].upper() == 'L'): print (indent + '* Low-resolution figure (100 dpi).') fig_resolution_in_dpi = 100 else: print (indent + 'Assuming medium-resolution (300 dpi) for the figure.') fig_resolution_in_dpi = 300 plt.savefig(args.output_file, dpi=fig_resolution_in_dpi, bbox_inches='tight') print (indent + '* Done saving figure (%s).' % args.output_file) if args.saveshow: print ('Opening saved figure (%s)...' % default_out_basename) # 'xdg-open' might fail to find the defualt program in some systems # For such cases, one can try to use other alternatives (just add more to the list below) image_viewer_list = ['xdg-open', 'eog'] for image_viewer in image_viewer_list: open_saved_fig = Popen([image_viewer, args.output_file], stdout=PIPE, stderr=PIPE) std_out, std_err = open_saved_fig.communicate() success_opening_file = std_err.strip() == '' if(success_opening_file): break if(not success_opening_file): print (indent + '* Failed (%s): no image viewer detected.' % default_out_basename) if args.show: print ('Showing figure (%s)...' % default_out_basename) plt.show() print (indent + '* Done showing figure (%s).' % default_out_basename) if __name__ == '__main__': print_opening_message() plot_options = BandUpPlotOptions() plot = BandUpPlot(plot_options) make_plot(plot) sys.exit(0) -- https://mail.python.org/mailman/listinfo/python-list