On Sunday, April 3, 2016 at 5:19:15 PM UTC-7, MRAB wrote:
> On 2016-04-04 01:04, Muhammad Ali wrote:
> > 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)
> >
> Look at the line "if(args.save):". That decides whether to save.
>
> Where does args come from? It comes from "args =
> plot.plot_options.args". "plot" is passed into "make_plot".
>
> Where does that object come from? It comes from "plot =
> BandUpPlot(plot_options)".
>
> Where does "plot_options" come from? It comes from "plot_options =
> BandUpPlotOptions()".
>
> What's "BandUpPlotOptions()"? I have no idea. It's not part of what
> you've posted.
>
> Now, over to you to do the rest. Maybe it's mentioned in matplotlib's docs.
I have posted the whole python script, please guide me which lines need to
modify to get a single data file instead of plot using matplotlib.
Thank you.
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