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Groups > comp.lang.python > #106392 > unrolled thread
| Started by | Muhammad Ali <muhammadaliaskari@gmail.com> |
|---|---|
| First post | 2016-04-03 12:01 -0700 |
| Last post | 2016-04-03 20:16 -0400 |
| Articles | 12 — 6 participants |
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Plot/Graph Muhammad Ali <muhammadaliaskari@gmail.com> - 2016-04-03 12:01 -0700
Re: Plot/Graph Michael Selik <michael.selik@gmail.com> - 2016-04-03 21:04 +0000
Re: Plot/Graph Muhammad Ali <muhammadaliaskari@gmail.com> - 2016-04-03 14:17 -0700
Re: Plot/Graph Oscar Benjamin <oscar.j.benjamin@gmail.com> - 2016-04-03 22:35 +0100
Re: Plot/Graph Muhammad Ali <muhammadaliaskari@gmail.com> - 2016-04-03 17:04 -0700
Re: Plot/Graph MRAB <python@mrabarnett.plus.com> - 2016-04-04 01:18 +0100
Re: Plot/Graph Muhammad Ali <muhammadaliaskari@gmail.com> - 2016-04-04 15:35 -0700
Re: Plot/Graph MRAB <python@mrabarnett.plus.com> - 2016-04-05 01:05 +0100
Re: Plot/Graph Steven D'Aprano <steve@pearwood.info> - 2016-04-05 11:56 +1000
Re: Plot/Graph Muhammad Ali <muhammadaliaskari@gmail.com> - 2016-04-04 17:16 -0700
Re: Plot/Graph Muhammad Ali <muhammadaliaskari@gmail.com> - 2016-04-04 18:17 -0700
Re: Plot/Graph Dennis Lee Bieber <wlfraed@ix.netcom.com> - 2016-04-03 20:16 -0400
| From | Muhammad Ali <muhammadaliaskari@gmail.com> |
|---|---|
| Date | 2016-04-03 12:01 -0700 |
| Subject | Plot/Graph |
| Message-ID | <768f1e2e-baab-4ce3-ae4e-aab2d8ac5bf8@googlegroups.com> |
Hi, Could anybody tell me that how can I plot graphs by matplotlib and get expertise in a short time? I have to plot 2D plots just like origin software. Secondly, how could we draw some horizontal reference line at zero when the vertical scale is from -3 to 3? Looking for your posts, please. Thank you. p.s: Is there any short and to the point text book/manual/pdf to learn 2D plotting with matplotlib?
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| From | Michael Selik <michael.selik@gmail.com> |
|---|---|
| Date | 2016-04-03 21:04 +0000 |
| Message-ID | <mailman.414.1459717472.28225.python-list@python.org> |
| In reply to | #106392 |
Indeed there is. Every example in the gallery shows the code to produce it. http://matplotlib.org/gallery.html On Sun, Apr 3, 2016, 8:05 PM Muhammad Ali <muhammadaliaskari@gmail.com> wrote: > > Hi, > > Could anybody tell me that how can I plot graphs by matplotlib and get > expertise in a short time? I have to plot 2D plots just like origin > software. > > Secondly, how could we draw some horizontal reference line at zero when > the vertical scale is from -3 to 3? > > Looking for your posts, please. > > Thank you. > > p.s: Is there any short and to the point text book/manual/pdf to learn 2D > plotting with matplotlib? > -- > https://mail.python.org/mailman/listinfo/python-list >
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| From | Muhammad Ali <muhammadaliaskari@gmail.com> |
|---|---|
| Date | 2016-04-03 14:17 -0700 |
| Message-ID | <20f8e8fb-0bb8-4daa-8d68-d0cc3699a6fc@googlegroups.com> |
| In reply to | #106408 |
On Sunday, April 3, 2016 at 2:04:45 PM UTC-7, Michael Selik wrote: > Indeed there is. Every example in the gallery shows the code to produce it. > > http://matplotlib.org/gallery.html > > On Sun, Apr 3, 2016, 8:05 PM Muhammad Ali <muhammadaliaskari@gmail.com> > wrote: > > > > > Hi, > > > > Could anybody tell me that how can I plot graphs by matplotlib and get > > expertise in a short time? I have to plot 2D plots just like origin > > software. > > > > Secondly, how could we draw some horizontal reference line at zero when > > the vertical scale is from -3 to 3? > > > > Looking for your posts, please. > > > > Thank you. > > > > p.s: Is there any short and to the point text book/manual/pdf to learn 2D > > plotting with matplotlib? > > -- > > https://mail.python.org/mailman/listinfo/python-list > > Thank you for your post. 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.
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| From | Oscar Benjamin <oscar.j.benjamin@gmail.com> |
|---|---|
| Date | 2016-04-03 22:35 +0100 |
| Message-ID | <mailman.416.1459719344.28225.python-list@python.org> |
| In reply to | #106411 |
On 3 Apr 2016 22:21, "Muhammad Ali" <muhammadaliaskari@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
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| From | Muhammad Ali <muhammadaliaskari@gmail.com> |
|---|---|
| Date | 2016-04-03 17:04 -0700 |
| Message-ID | <10a26eef-d271-4202-902e-751eb5495a80@googlegroups.com> |
| In reply to | #106413 |
On Sunday, April 3, 2016 at 2:35:58 PM UTC-7, Oscar Benjamin wrote:
> On 3 Apr 2016 22:21, "Muhammad Ali" <muhammadaliaskari@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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| From | MRAB <python@mrabarnett.plus.com> |
|---|---|
| Date | 2016-04-04 01:18 +0100 |
| Message-ID | <mailman.420.1459729142.28225.python-list@python.org> |
| In reply to | #106417 |
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" <muhammadaliaskari@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.
[toc] | [prev] | [next] | [standalone]
| From | Muhammad Ali <muhammadaliaskari@gmail.com> |
|---|---|
| Date | 2016-04-04 15:35 -0700 |
| Message-ID | <c2368d5c-eafb-4fe5-a233-f25b82f5521b@googlegroups.com> |
| In reply to | #106419 |
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" <muhammadaliaskari@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.
[toc] | [prev] | [next] | [standalone]
| From | MRAB <python@mrabarnett.plus.com> |
|---|---|
| Date | 2016-04-05 01:05 +0100 |
| Message-ID | <mailman.46.1459814755.32530.python-list@python.org> |
| In reply to | #106472 |
On 2016-04-04 23:35, Muhammad Ali wrote:
> 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" <muhammadaliaskari@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.
>
If you can run it, you should be able to set the args to make it save.
It should be explained somewhere in the docs.
That script refers to things that must be coming from elsewhere, things
that are not being imported explicitly by the script.
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| From | Steven D'Aprano <steve@pearwood.info> |
|---|---|
| Date | 2016-04-05 11:56 +1000 |
| Message-ID | <57031b5e$0$1587$c3e8da3$5496439d@news.astraweb.com> |
| In reply to | #106419 |
On Tue, 5 Apr 2016 08:33 am, Muhammad Ali wrote: [couple of hundred lines of quoted text] [followed by about a thousand lines of code] Seriously? Do you really expect people to read through hundreds of lines of quotes, going at least three levels > > > deep, and then read a thousand lines of code, to do your work for you? For free? We try to be friendly and helpful, but there are limits. Please read this before replying: http://www.sscce.org/ Thank you. -- Steven
[toc] | [prev] | [next] | [standalone]
| From | Muhammad Ali <muhammadaliaskari@gmail.com> |
|---|---|
| Date | 2016-04-04 17:16 -0700 |
| Message-ID | <99a3f77c-7069-4e81-942e-06f6e4445291@googlegroups.com> |
| In reply to | #106413 |
On Sunday, April 3, 2016 at 2:35:58 PM UTC-7, Oscar Benjamin wrote: > On 3 Apr 2016 22:21, "Muhammad Ali" <muhammadaliaskari@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 Yes, it is complete script and it works well with matplotlib.
[toc] | [prev] | [next] | [standalone]
| From | Muhammad Ali <muhammadaliaskari@gmail.com> |
|---|---|
| Date | 2016-04-04 18:17 -0700 |
| Message-ID | <dbd37709-4764-404c-9e01-e925dbcb5d18@googlegroups.com> |
| In reply to | #106478 |
On Tuesday, April 5, 2016 at 8:16:22 AM UTC+8, 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" <muhammadaliaskari@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 > > Yes, it is complete script and it works well with matplotlib. But I have to modify it to extract data into a single .dat file instead of directly plotting it by using matplotlib. I want to plot the data file in some other software.
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| From | Dennis Lee Bieber <wlfraed@ix.netcom.com> |
|---|---|
| Date | 2016-04-03 20:16 -0400 |
| Message-ID | <mailman.419.1459729023.28225.python-list@python.org> |
| In reply to | #106392 |
On Sun, 3 Apr 2016 12:01:48 -0700 (PDT), Muhammad Ali
<muhammadaliaskari@gmail.com> declaimed the following:
>
>p.s: Is there any short and to the point text book/manual/pdf to learn 2D plotting with matplotlib?
http://www.amazon.com/Matplotlib-Python-Developers-Sandro-Tosi/dp/1847197906
http://www.amazon.com/Matplotlib-Plotting-Cookbook-Alexandre-Devert/dp/1849513260
--
Wulfraed Dennis Lee Bieber AF6VN
wlfraed@ix.netcom.com HTTP://wlfraed.home.netcom.com/
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