Plot/Graph
Muhammad Ali
muhammadaliaskari at gmail.com
Sun Apr 3 20:04:51 EDT 2016
On Sunday, April 3, 2016 at 2:35:58 PM UTC-7, Oscar Benjamin wrote:
> On 3 Apr 2016 22:21, "Muhammad Ali" <muhammadaliaskari at 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)
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