From 401373907ac8ba2cbcc04149348f929e279517d8 Mon Sep 17 00:00:00 2001 From: "Kieran B. Spooner" Date: Wed, 4 Jan 2023 17:36:28 +0000 Subject: [PATCH 1/2] update website --- docs/.doctrees/avg-rates.doctree | Bin 0 -> 12054 bytes docs/.doctrees/cumkappa.doctree | Bin 0 -> 15871 bytes docs/.doctrees/dos.doctree | Bin 0 -> 9710 bytes docs/.doctrees/environment.pickle | Bin 431724 -> 441839 bytes docs/.doctrees/gallery.doctree | Bin 21667 -> 22848 bytes docs/.doctrees/heatmaps.doctree | Bin 0 -> 18837 bytes docs/.doctrees/phonons.doctree | Bin 0 -> 12818 bytes docs/.doctrees/prophon.doctree | Bin 0 -> 10664 bytes docs/.doctrees/tp.axes.doctree | Bin 191331 -> 196390 bytes docs/.doctrees/tp.cli.doctree | Bin 42568 -> 55335 bytes docs/.doctrees/tp.data.doctree | Bin 186974 -> 190554 bytes docs/.doctrees/tp.doctree | Bin 174797 -> 174521 bytes docs/.doctrees/tp.plot.doctree | Bin 723148 -> 723433 bytes docs/.doctrees/tp.setup.doctree | Bin 35724 -> 35751 bytes docs/.doctrees/tutorial-00.doctree | Bin 7463 -> 8477 bytes docs/.doctrees/tutorial-01.doctree | Bin 17709 -> 18757 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  2. Tutorial-03: Compound Axes
  3. @@ -196,6 +197,19 @@

    Plot (lines 27-28) +

    Colourmaps¶

    +

    Some functions, including heatmaps and projected dispersions such as +the wideband plot above, accept single colours which are passed to +ThermoPlotter’s colourmap generators. These can also be accessed +directly, allowing greater control of the output or use on non-tp +applications. The colourmap used here could be generated with:

    +

    linear interpolates linearly between the colours, and is used for +projected dispersions. elbow provides two linear sections with a +midpoint you specify, and uniform calculates the midpoint position +based on the relative compositions of the colours, which is used for +heatmaps.

    + diff --git a/docs/tutorial-05.html b/docs/tutorial-05.html index 3721d67..91d7940 100644 --- a/docs/tutorial-05.html +++ b/docs/tutorial-05.html @@ -100,21 +100,130 @@

    Tutorial-05: Helper Functions
    tp plot transport ../data/basno3/transport_75x75x75.json -k ../data/basno3/kappa-m363636.hdf5 -q conductivity -q seebeck -q lattice_thermal_conductivity -n 1e18 -n 1e19 -n 1e20 -n 1e21 --tmin 0 --location 3
     
    -

    The full script is long and mostly irrelevant, but can be found here.

    +

    In Python, it is:

    +
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    #!/usr/bin/env python3
    +
    +import tp
    +from matplotlib.gridspec import GridSpec
    +import matplotlib.pyplot as plt
    +
    +afile = '../data/basno3/transport_75x75x75.json'
    +kfile = '../data/basno3/kappa-m363636.hdf5'
    +
    +doping = [-1e18, -1e19, -1e20, -1e21]
    +direction = 'avg'
    +quantities = ['conductivity', 'seebeck']
    +scale = ['log', 'linear', 'linear']
    +
    +from os import path
    +if not path.isfile(kfile) or (path.getsize(kfile) < 1024*1024*100):
    +    raise Exception('File not found, please use get-data.sh')
    +
    +# Axes
    +plt.style.use('tp')
    +fig = plt.figure(figsize=(27/2.54, 8.3/2.54))
    +grid = GridSpec(1, 14)
    +ax = [fig.add_subplot(grid[0, :4]),
    +      fig.add_subplot(grid[0, 5:9]),
    +      fig.add_subplot(grid[0, 10:])]
    +plt.subplots_adjust(left=0.06, right=0.98,
    +                    bottom=0.12, top=0.95)
    +
    +# Load
    +adata = tp.data.load.amset(afile)
    +kdata = tp.data.load.phono3py(kfile)
    +
    +# Plot
    +for i, q in enumerate(quantities):
    +    for d in doping:
    +        data = tp.data.utilities.resolve(adata, q, direction=direction, doping=d)
    +        ax[i].plot(data['temperature'], data[q],
    +                   label="{:.2e}".format(data['meta']['doping']))
    +
    +q = 'lattice_thermal_conductivity'
    +data = tp.data.utilities.resolve(kdata, q, direction=direction)
    +ax[2].plot(data['temperature'], data[q])
    +
    +# Formatting
    +axlabels = tp.settings.labels()
    +for i, q in enumerate([*quantities, q]):
    +    ax[i].set_xlabel(axlabels['temperature'])
    +    ax[i].set_ylabel(axlabels[q])
    +    tp.plot.utilities.set_locators(ax[i], x='linear', y=scale[i])
    +
    +handles, labels = tp.axes.legend.consolidate(ax)
    +ax[2].legend(loc='best', title=axlabels['doping'], handles=handles,
    +             labels=labels)
    +tp.axes.legend.alphabetise(ax, preset='roman', suffix=')', x=-0.12)
    +
    +# Save
    +fig.savefig('tutorial-05.png')
    +
    +

    Axes (line 20)¶

    -
    plt.style.use('tp')
    -
    -

    ThermoPlotter comes with two matplotlib styles, tp and tp-large, for small and large axes respectively, which are accessible any time using plt.style.use.

    Resolve (lines 36 and 41)¶

    -
    tp.data.utilities.resolve(adata, q, direction=direction, doping=d)
    -
    -

    tp.data.utilities.resolve resolves a data array by dependent properties. As arguments, it takes a data dictionary, an array of the names of the quantities to be resolved, and the dependent variable @@ -138,13 +247,6 @@

    Resolve (lines 36 and 41)

    Locators, Ticks and Labels (lines 45-49)¶

    -
    axlabels = tp.settings.labels()
    -for i, q in enumerate([*quantities, q]):
    -    ax[i].set_xlabel(axlabels['temperature'])
    -    ax[i].set_ylabel(axlabels[q])
    -    tp.plot.utilities.set_locators(ax[i], x='linear', y=scale[i])
    -
    -

    There are several functions to aid in formatting axes. tp.settings.labels, and its variations large_, long_, medium_ and short_labels return a dictionary of axes labels @@ -155,12 +257,6 @@

    Locators, Ticks and Labels (lines 45-49)

    Legends (lines 51-54)¶

    -
    handles, labels = tp.axes.legend.consolidate(ax)
    -ax[2].legend(loc='best', title=axlabels['doping'], handles=handles,
    -             labels=labels)
    -tp.axes.legend.alphabetise(ax, preset='roman', suffix=')', x=-0.12)
    -
    -

    tp.axes.legend.consolidate consolidates the legends of a list of axes into one, ensuring no duplicates, and returns the handles and labels, which can then be passed to ax.legend.

    diff --git a/docs/tutorials.html b/docs/tutorials.html index cf5efbd..24bfc60 100644 --- a/docs/tutorials.html +++ b/docs/tutorials.html @@ -17,7 +17,7 @@ - + @@ -139,7 +139,7 @@

    Tutorials - +

    diff --git a/docs/waterfalls.html b/docs/waterfalls.html new file mode 100644 index 0000000..de0d0c6 --- /dev/null +++ b/docs/waterfalls.html @@ -0,0 +1,312 @@ + + + + + + Waterfall — ThermoPlotter 2.0.0 documentation + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    Waterfall¶

    +Waterfall plot of mean free path against frequency with lattice thermal conductivity projected. +

    This shows a plot of mean free path against frequency with lattice +thermal conductivity projected on the colour axis, as well as a density +of states (DoS). It shows the connections between phonon scattering and +the constituent elements, for example there seems to be a big drop in +phonon mean free path in the frequencies with high amounts of Ba +character.

    +

    While plots cannot be layered at the command line like this, the +waterfall section can be plotted with:

    +

    And in python:

    +
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    #!/usr/bin/env python3
    +
    +import tp
    +
    +kfile = '../data/basno3/kappa-m363636.hdf5'
    +dfile = '../data/basno3/projected_dos.dat'
    +poscar = '../data/basno3/POSCAR'
    +
    +# <ignore>
    +from os import path
    +if not path.isfile(kfile) or (path.getsize(kfile) < 1024*1024*100):
    +    raise Exception('File not found, please use get-data.sh')
    +# </ignore>
    +
    +direction = 'avg'
    +temperature = 300
    +waterfall = 'mean_free_path'
    +projected = 'mode_kappa'
    +# Note for cumkappa and waterfall plots, mode_kappa and not kappa is required
    +quantities = ['waterfall', waterfall, projected]
    +# waterfall is an alias for frequency
    +
    +colours = {'Ba': '#ffcf06',
    +           'Sn': '#59c605',
    +           'O':  '#00b1f7'}
    +colour = 'viridis'
    +
    +# Axes
    +fig, ax, add_legend = tp.axes.small.one_colourbar()
    +
    +# Load
    +data = tp.data.load.phono3py(kfile, quantities=quantities)
    +dos = tp.data.load.phonopy_dos(dfile, poscar=poscar)
    +
    +# Add
    +tp.plot.frequency.format_waterfall(ax, data, waterfall, direction=direction,
    +                                   temperature=temperature)
    +tp.plot.frequency.add_dos(ax, dos, colour=colours, scale=True, main=False,
    +                          alpha=0.6, line=False)
    +cbar = tp.plot.frequency.add_projected_waterfall(ax, data, waterfall,
    +                                                 projected, colour=colour,
    +                                                 temperature=temperature,
    +                                                 direction=direction)
    +add_legend()
    +
    +# Save
    +
    +fig.savefig('waterfall.pdf')
    +fig.savefig('waterfall.png')
    +
    +
    +

    This example highlights a complication: in order for the +waterfall to be on top of the DoS, so as not to obscure the projected +colour, an additional command, format_waterfall, must be used so +the DoS can be scaled correctly but plotted first (line 36-37).

    +

    Replacing add_projected_waterfall with simply add_waterfall +removes the projection, and colours it with a single colour or by band +index as you prefer. This is achieved by removing the --projected +tag at the command line.

    +Density waterfall plot of mean free path against frequency. +

    This is an alternative of the waterfall plot, where the colour darkness +shows the density of the points, which is often clearer than just +adjusting the alpha parameter on the regular waterfall plot.

    +

    It can be plotted at the command line with:

    +

    Or in python with:

    +
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    #!/usr/bin/env python3
    +
    +import tp
    +
    +kfile = '../data/basno3/kappa-m363636.hdf5'
    +# <ignore>
    +from os import path
    +if not path.isfile(kfile) or (path.getsize(kfile) < 1024*1024*100):
    +    raise Exception('File not found, please use get-data.sh')
    +# </ignore>
    +
    +direction = 'avg'
    +temperature = 300
    +waterfall = 'mean_free_path'
    +quantities = ['waterfall', waterfall]
    +# waterfall is an alias for frequency
    +
    +colour = 'Blues'
    +
    +# Axes
    +fig, ax, add_legend = tp.axes.small.one()
    +
    +# Load
    +data = tp.data.load.phono3py(kfile, quantities=quantities)
    +
    +# Add
    +tp.plot.frequency.add_density(ax, data, waterfall, colour=colour,
    +                              temperature=temperature, direction=direction)
    +
    +# Save
    +
    +fig.savefig('density.pdf')
    +fig.savefig('density.png')
    +
    +
    +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file From 0cd64e19dd37994a36f0b3e1cc3f26f29cd5e167 Mon Sep 17 00:00:00 2001 From: "Kieran B. Spooner" Date: Thu, 5 Jan 2023 10:11:09 +0000 Subject: [PATCH 2/2] actually updated the website this time :upside_down_face: --- docs/.doctrees/avg-rates.doctree | Bin 12054 -> 12090 bytes docs/.doctrees/cumkappa.doctree | Bin 15871 -> 15906 bytes docs/.doctrees/dos.doctree | Bin 9710 -> 8232 bytes docs/.doctrees/environment.pickle | Bin 441839 -> 443024 bytes docs/.doctrees/gallery.doctree | Bin 22848 -> 23972 bytes docs/.doctrees/heatmaps.doctree | Bin 18837 -> 17499 bytes docs/.doctrees/phonons.doctree | Bin 12818 -> 12852 bytes docs/.doctrees/prophon.doctree | Bin 10664 -> 10708 bytes docs/.doctrees/tutorial-02.doctree | Bin 16636 -> 16247 bytes docs/.doctrees/waterfalls.doctree | Bin 14151 -> 14187 bytes docs/_sources/avg-rates.rst.txt | 41 +-------- docs/_sources/cumkappa.rst.txt | 55 +----------- docs/_sources/dos.rst.txt | 47 +--------- docs/_sources/gallery.rst.txt | 3 + docs/_sources/heatmaps.rst.txt | 85 +----------------- docs/_sources/phonons.rst.txt | 56 +----------- docs/_sources/prophon.rst.txt | 42 +-------- docs/_sources/waterfalls.rst.txt | 55 +----------- docs/avg-rates.html | 5 +- docs/cumkappa.html | 1 - docs/dos.html | 6 ++ docs/heatmaps.html | 62 +++++++++++++ docs/phonons.html | 1 - docs/searchindex.js | 2 +- docs/src/avg-rates.rst | 41 +-------- docs/src/cumkappa.rst | 55 +----------- docs/src/dos.rst | 47 +--------- docs/src/gallery.rst | 3 + docs/src/heatmaps.rst | 85 +----------------- docs/src/phonons.rst | 56 +----------- docs/src/plot-dos.py | 25 ++++++ docs/src/prophon.rst | 42 +-------- docs/src/waterfalls.rst | 55 +----------- docs/waterfalls.html | 5 +- .../02-individual-customisation/README.rst | 2 +- examples/dos/README.rst | 4 +- examples/heatmaps/README.rst | 11 ++- 37 files changed, 128 insertions(+), 764 deletions(-) create mode 100644 docs/src/plot-dos.py diff --git a/docs/.doctrees/avg-rates.doctree b/docs/.doctrees/avg-rates.doctree index 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a/docs/.doctrees/waterfalls.doctree b/docs/.doctrees/waterfalls.doctree index 5c351436649224e7e774e94cce6b5a6ef9b09f44..07d5c3cb22ce86d3cf94e1fe26a2ce642065137b 100644 GIT binary patch delta 363 zcmX?}_d1WIfpu!aMixCrSp_{k{nU!Y+=86cV*T>OlGLKK#GD-cAXi5hU)RkUj7QWa z8#3B#*3>surW(zodmM&1qFK8it|g0 zl2fN-Xq9GYW*wh=z(9=g#N>-64U8|4R6m{EVECNz8%RVvqb8#ks5T=jLn`Yj(CiFp zpx&g^$q!j2C-)n0PCj6yA#ofeE|tNZ!I;4elFyLw7AZ~2Iy@~9LOCk zQd#^Uj=pKuBcP5RmYmGI)G6M&=&r&vO&??$$eB#KaMOUk(wbal`j^QXEQI1vEhbEd VUNGZi;$fOR-$aV>_~dV9fdJlFc2ocW delta 283 zcmaEzcRY`!fpx0QMixCrM$5_PgtaF-Xz*_iVcf6Al%ciRU*i)aW9DWjEfYo%w_5uH z3)5-F$rDZFCikR~I+FD*(=osywdnxUC> zjFEvMLn?zigE50SLpnn%L&jUEG%4%&L4l7 goIKC;FQXM$1Z0oq-1 K-1, -which will be recorded in ``data['meta']['kappa_source']`` (other -numbers can be specified). The above is an alternative, which plots the -lattice thermal conductivity required to reach a specified ZT. We hope -this is a valuable tool to help decide wether to run expensive lattice -thermal conductivity calculations on a material. The CLI is: - -.. codeblock:: - tp plot kappa-target ../data/zno/boltztrap.hdf5 -c '#008080' -d x - -and python: - -.. literalinclude:: plot-kappa-target.py - :language: python - :linenos: - :emphasize-lines: 18,19 - -.. image:: ztdiff.png - :alt: Difference between two ZTs against carrier concentration and temperature. - -The final heatmap is the ztdiff, which compares the ZTs of two -materials at a range of carrier concentrations and temperatures. This -can be done with any two materials, from completely different (e.g. -BaSnO3 and ZnO here) to the same material under different -conditions (e.g. different degrees of nanostructuring), although this -method is most appropriate for closely related materials with similar -dopabilities. At the command line: - -.. codeblock:: - tp plot ztdiff ../data/zno/boltztrap.hdf5 ../data/basno3/boltztrap.hdf5 -k ../data/zno/kappa-m404021.hdf5 ../data/basno3/kappa-m363636.hdf5 -l ZnO -l 'BaSnO$_3$' - -and in python: - -.. literalinclude:: plot-ztdiff.py - :language: python - :linenos: - :emphasize-lines: 19,20,23 - -Note how list comprehensions and expansions (lines 19-20 and 23 -respectively) can make the script tidier. On top of the colourbar (here -thrown away with ``_``), this function returns the handles and labels -which must be put into the legend manually if you want it. If you are -using an axes template with multiple sets of axes, the ``custom`` -argument must be used in ``add_legend`` to enable manual manipulation -of the handles and labels. ``add_ztdiff`` and its power factor -equivalent, ``add_pfdiff``, also make sure the colour scale is centred -on zero (i.e. where both are equal). \ No newline at end of file +.. include:: ../../examples/heatmaps/README.rst diff --git a/docs/_sources/phonons.rst.txt b/docs/_sources/phonons.rst.txt index 86ce9c1..12c8035 100644 --- a/docs/_sources/phonons.rst.txt +++ b/docs/_sources/phonons.rst.txt @@ -1,55 +1 @@ -------- -Phonons -------- - -.. image:: phonons.png - :alt: Phonon dispersion and density of states. - -This shows a phonon dispersion and density of states (DoS). This is the -only combined plot currenty possible at the command line, with: - -.. codeblock:: - tp plot phonons ../data/zno/band.yaml --dos ../data/zno/projected_dos.dat --poscar ../data/zno/POSCAR -doscolour '#d46ef9' --doscolour '#7b8eff' -c '#f0901f' - -and in python: - -.. literalinclude:: plot-phonons.py - :language: python - :linenos: - :emphasize-lines: 22,24 - -All plot-types in ``tp.plot.frequency`` have an invert argument to plot -them side-on by a phonon dispersion, which also shortens their x-axis -labels and removes their y-axis labels and tick labels (line 22). -Currently the axes of the two plots have to be aligned manually, but -this is simple (line 24). - -.. image:: multiphon.png - :alt: Phonon dispersions for different supercell sizes. - -This shows phonons dispersions for various supercell sizes for -convergence: - -.. codeblock:: - tp plot phonons ../data/basno3/band-*.yaml --legend_title "Supercell Size" -l "2 x 2 x 2" -l "3 x 3 x 3" -l "4 x 4 x 4" -l "5 x 5 x 5" - -and in python: - -.. literalinclude:: plot-multiphon.py - :language: python - :linenos: - :emphasize-lines: 17,18 - -At the command line, the same command is used for both, but in python -a separate command, ``tp.plot.phonons.add_multi`` is used. While this -could be done by looping over ``tp.plot.phonons.add_dispersion``, it -is bundled into a one-liner for convenience. As phonon dispersions do -not always have the same x-scale (at least in phonopy), this rescales -the dispersions so they always match, which is also useful to compare -different materials (so long as they are at least in the same space -group and are preferably closely related), or materials under expansion -or compression. This also demonstrates the large axes style, more -appropriate for presentations or posters than the default style, which -is better for papers; and the add_legend function, which adds a -pre-positioned legend, and accepts all the other usual arguments -including ``title`` (line 18). +.. include:: ../../examples/phonons/README.rst diff --git a/docs/_sources/prophon.rst.txt b/docs/_sources/prophon.rst.txt index 6707d43..b7ddf28 100644 --- a/docs/_sources/prophon.rst.txt +++ b/docs/_sources/prophon.rst.txt @@ -1,41 +1 @@ ------------------ -Projected Phonons ------------------ - -.. image:: wideband.png - :alt: Phonon dispersion with broadened bands. - -This shows a phonon dispersion with bands broadened to indicate -scattering (the widest bands scatter the most). Via the command-line: - -.. codeblock:: - tp plot wideband ../data/zno/band.yaml ../data/zno/kappa-m404021.hdf5 -c black -c red -s dark_background -p POSCAR - -and in python: - -.. literalinclude:: plot-wideband.py - :language: python - :linenos: - :emphasize-lines: 16,19,23,24 - -This also demonstrates the custom colourmaps which can be generated by -supplying a single hex, rgb (array) or named colour as the colour -input, or in this case two (the default background is normally white, -but to match the ``dark_background`` style sheet, black has been used -here). In this case, ``tp.plot.colour.linear`` has been used. It also -shows the large style, more suitable to presentations or posters than -the default one which is more suited to papers. Instead of the actual -quantities you want to load, ``wideband`` can select all necessary for -this plot. - -.. image:: prophon.png - :alt: Phonon dispersion with phonon lifetime projected. - -This shows a phonon dispersion with phonon lifetime projected on the -colour axis. It is a more quantitative version of the above wideband -plot. It can be plotted in python with: - -.. literalinclude:: plot-projected-phonons.py - :language: python - :linenos: - :emphasize-lines: 25-27 +.. include:: ../../examples/projected-phonons/README.rst diff --git a/docs/_sources/waterfalls.rst.txt b/docs/_sources/waterfalls.rst.txt index 2834faf..d0cfc7d 100644 --- a/docs/_sources/waterfalls.rst.txt +++ b/docs/_sources/waterfalls.rst.txt @@ -1,54 +1 @@ ---------- -Waterfall ---------- - -.. image:: waterfall.png - :alt: Waterfall plot of mean free path against frequency with lattice thermal conductivity projected. - -This shows a plot of mean free path against frequency with lattice -thermal conductivity projected on the colour axis, as well as a density -of states (DoS). It shows the connections between phonon scattering and -the constituent elements, for example there seems to be a big drop in -phonon mean free path in the frequencies with high amounts of Ba -character. - -While plots cannot be layered at the command line like this, the -waterfall section can be plotted with: - -.. codeblock:: - tp plot waterfall ../data/basno3/kappa-m363636.hdf5 -y mfp --projected kappa - -And in python: - -.. literalinclude:: plot-waterfall.py - :language: python - :linenos: - :emphasize-lines: 36,37 - -This example highlights a complication: in order for the -waterfall to be on top of the DoS, so as not to obscure the projected -colour, an additional command, ``format_waterfall``, must be used so -the DoS can be scaled correctly but plotted first (line 36-37). - -Replacing ``add_projected_waterfall`` with simply ``add_waterfall`` -removes the projection, and colours it with a single colour or by band -index as you prefer. This is achieved by removing the ``--projected`` -tag at the command line. - -.. image:: density.png - :alt: Density waterfall plot of mean free path against frequency. - -This is an alternative of the waterfall plot, where the colour darkness -shows the density of the points, which is often clearer than just -adjusting the alpha parameter on the regular waterfall plot. - -It can be plotted at the command line with: - -.. codeblock:: - tp plot waterfall ../data/basno3/kappa-m363636.hdf5 -y mfp --projected density -c Blues - -Or in python with: - -.. literalinclude:: plot-density.py - :language: python - :linenos: +.. include:: ../../examples/waterfall/README.rst diff --git a/docs/avg-rates.html b/docs/avg-rates.html index 78f9d06..3bd006a 100644 --- a/docs/avg-rates.html +++ b/docs/avg-rates.html @@ -17,7 +17,7 @@ - + @@ -42,7 +42,6 @@
  4. Density of States (DoS)
  5. Cumulative Kappa
  6. Waterfalls
  7. -
  8. Projected Phonons
  9. Average Scattering Rates
  10. Reference
  11. @@ -206,7 +205,7 @@

    Average Rates - + diff --git a/docs/cumkappa.html b/docs/cumkappa.html index b239ce7..9c808f7 100644 --- a/docs/cumkappa.html +++ b/docs/cumkappa.html @@ -42,7 +42,6 @@
  12. Density of States (DoS)
  13. Cumulative Kappa
  14. Waterfalls
  15. -
  16. Projected Phonons
  17. Average Scattering Rates
  18. Reference
  19. diff --git a/docs/dos.html b/docs/dos.html index 143bd32..e2c7e49 100644 --- a/docs/dos.html +++ b/docs/dos.html @@ -90,7 +90,13 @@

    Density of States (DoS)This shows a two densities of states (DoSs), which show the density of phonon modes at particular frequencies.

    The left plot can be generated by:

    +
    tp plot dos ../data/basno3/projected_dos.dat --total --nolegend -c pink -c cyan -c red -p ../data/basno3/POSCAR
    +
    +

    And the right plot by:

    +
    tp plot dos ../data/basno3/projected_dos.dat --atoms "Ba Sn O O_2 2" --sigma 0.2 --location 1 -c magenta -c cyan -c red -c orange
    +
    +

    Or the whole thing in python with:

    The most foolproof way of specifying colours is through a dictionary (lines 8-11), but colours, linestyles etc. as well as atoms can also be diff --git a/docs/heatmaps.html b/docs/heatmaps.html index 0652cc4..3a04535 100644 --- a/docs/heatmaps.html +++ b/docs/heatmaps.html @@ -89,7 +89,63 @@

    Heatmaps

    This shows the classic ZT against carrier concentration and temperature heatmap. This can be plotted at the command line with:

    +
    tp plot ztmap ../data/zno/boltztrap.hdf5 -k ../data/zno/kappa-m404021.hdf5 -c '#800080' -d x
    +
    +

    and in python with:

    +
     1
    + 2
    + 3
    + 4
    + 5
    + 6
    + 7
    + 8
    + 9
    +10
    +11
    +12
    +13
    +14
    +15
    +16
    +17
    +18
    +19
    +20
    +21
    +22
    +23
    +24
    +25
    +26
    #!/usr/bin/env python3
    +
    +from os import path
    +import tp
    +
    +bfile = '../data/zno/boltztrap.hdf5'
    +kfile = '../data/zno/kappa-m404021.hdf5'
    +if not path.isfile(kfile) or (path.getsize(kfile) < 1024*1024*100):
    +    raise Exception('File not found, please use get-data.sh')
    +
    +direction = 'x'
    +colour = '#800080'
    +
    +# Axes
    +fig, ax, add_legend = tp.axes.small.one_colourbar()
    +
    +# Load
    +adata = tp.data.load.boltztrap(bfile)
    +kdata = tp.data.load.phono3py(kfile)
    +
    +# Add
    +tp.plot.heatmap.add_ztmap(ax, adata, kdata, direction=direction, colour=colour)
    +
    +# Save
    +fig.savefig('ztmap.pdf')
    +fig.savefig('ztmap.png')
    +
    +

    This uses the tp.plot.heatmap.add_ztmap function (line 22), which calculates the ZT and sets the axes labels. All the other functionality is wrapped within the tp.plot.heatmap.add_heatmap function, which @@ -110,6 +166,9 @@

    Heatmaps
    tp plot kappa-target ../data/zno/boltztrap.hdf5 -c '#008080' -d x
    +
    +

    and python:

    and in python:

     1
      2
    diff --git a/docs/phonons.html b/docs/phonons.html
    index 2de3b75..c1c2d59 100644
    --- a/docs/phonons.html
    +++ b/docs/phonons.html
    @@ -42,7 +42,6 @@
     
  20. Density of States (DoS)
  21. Cumulative Kappa
  22. Waterfalls
  23. -
  24. Projected Phonons
  25. Average Scattering Rates
  26. Reference
  27. diff --git a/docs/searchindex.js b/docs/searchindex.js index 6f8d44a..b8ce1bd 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/docs/src/avg-rates.rst b/docs/src/avg-rates.rst index 2b29404..4eb78d3 100644 --- a/docs/src/avg-rates.rst +++ b/docs/src/avg-rates.rst @@ -1,40 +1 @@ -------------- -Average Rates -------------- - -.. image:: avg-rates.png - :alt: A plot of weighted average scattering rates against temperature and carrier concnetration. - -This shows the average scattering rates against temperature, averaged -across k-points and weighted by the derivative of the Fermi-Dirac -distribution, as they are when calculating the conductivity, thereby -giving a representative image of the effect the scattering processes -play in the material. This can be plotted at the command line with: - -.. codeblock:: - tp plot avg-rates ../data/basno3/mesh_75x75x75.h5 -t 1000 -n -1e19 --location 2 --large -c red -c blue -c magenta - -If rates are included in the file but are very low, you may want to -``--exclude`` them; ``--exclude PIE`` may be particularly popular. -While ThermoPlotter does not currently have python functions to plot -line graphs, it does calculate the weighted rates and the ancillary -functions should make this relatively straightforward: - -.. literalinclude:: plot-avg-rates.py - :language: python - :linenos: - :emphasize-lines: 7,24,26,27 - -The weighted rates can be calculated directly via the -``tp.data.load.amset_mesh`` function, by specifying ``weighted_rates`` -as the ``quantity`` (lines 7, 24). Selecting which data to show is then -as simple as using the resolve function (lines 26/27). To plot the -graph matplotlib's normal ``plot`` function is used, and the axes, -labels, ticks and legend are all sorted out by tp functions (lines 21, -34-38. 39/40 and 42 respectively, see also `Tutorial-05`_). - -.. _Tutorial-05: https://smtg-ucl.github.io/ThermoPlotter/tutorial-05.html - -This graph can instead show weighted mean free paths, by adding the -``--mfp`` tag at the command line or changing ``q`` to equal -``weighted_mfp`` in python. +.. include:: ../../examples/avg-rates/README.rst diff --git a/docs/src/cumkappa.rst b/docs/src/cumkappa.rst index 6e1debd..3d231b0 100644 --- a/docs/src/cumkappa.rst +++ b/docs/src/cumkappa.rst @@ -1,54 +1 @@ ---------------------------------------- -Cumulative Lattice Thermal Conductivity ---------------------------------------- - -.. image:: cumkappa.png - :alt: Cumulative lattice thermal conductivity against frequency and mean free path. - -This shows two plots of cumulative lattice thermal conductivity. The -first one is against frequency, and particularly in conjunction with a -`density of states`_ (DoS) or a phonon band structure, this can show the -relative contributions of the constituent atoms to the lattice thermal -conductivity. The second is against mean free path, and can be used to -approximate the effect of nanostructuring, for which reason the ability -to add markers has been included, e.g. here you can see nanostructuring -to 100 nanometers may reduce the lattice thermal conductivity by around -40 %. - -.. _density of states: https://github.com/SMTG-UCL/ThermoPlotter/blob/master/examples/dos/ - -The right plot can be plotted at the command line with: - -.. codeblock:: - tp plot cumkappa ../data/zno/kappa-m404021.hdf5 --mfp --percent -d x -d y -c '#59c605' -c '#ffcf06' --nofill --xmarkers 1e-7 -l x -l y --location in - -And while the layering of the DoS plot is not yet supported at the -command line, the cumkappa part of the left graph can be plotted by -removing ``--mfp --percent --xmarkers 1e-7`` from the above. - -The python version is as follows: - -.. literalinclude:: plot-cumkappa.py - :language: python - :linenos: - :emphasize-lines: 33-35,37 - -In order to combine frequency plots, there are two important tags, ``main``, -which controls the setting of axis limits and labels, and shouls be ``False`` -for all but the main plot; and ``scale``, which scales the data to the axes, -allowing diverse plots to share :rainbow: (line 35). An alternative usage of -these is if both are ``True``, it scales to percent (line 33). This is done -with the ``--percent`` tag at the command line. - -The markers are added with the ``xmarkers`` tag (line 34), and have a -counterpart, ``ymarkers``. - -This also demonstrates use of the ``add_legend`` function supplied with -``tp.axes`` functions (line 37), which combines the legends of all plots and -places itself in one of several pre-programmed position, which you can -select with the ``location`` argument. Numbers will place the legend -in one of the axes with ``loc='best'``, and there are also several -descriptive positions, such as ``above`` and ``right``. It also accepts -most ``ax.legend`` arguements such as ``title`` and ``ncol``. If you -want to use custom handles and labels in a multi-axes figure (including -DoS axes), you must specify ``custom=True``. +.. include:: ../../examples/cumkappa/README.rst diff --git a/docs/src/dos.rst b/docs/src/dos.rst index 81a6cea..befa416 100644 --- a/docs/src/dos.rst +++ b/docs/src/dos.rst @@ -1,46 +1 @@ ------------------------ -Density of States (DoS) ------------------------ - -.. image:: dos.png - :alt: Two densities of states demonstrating customisablility options. - -This shows a two densities of states (DoSs), which show the density of phonon -modes at particular frequencies. - -The left plot can be generated by: - -.. codeblock:: - - tp plot dos ../data/basno3/projected_dos.dat --total --nolegend -c pink -c cyan -c red -p ../data/basno3/POSCAR - -And the right plot by: - -.. codeblock:: - - tp plot dos ../data/basno3/projected_dos.dat --atoms "Ba Sn O O_2 2" --sigma 0.2 --location 1 -c magenta -c cyan -c red -c orange - -Or the whole thing in python with: - -.. literalinclude:: plot-dos.py - :language: python - :linenos: - :emphasize-lines: 8-11,17,20,21 - -The most foolproof way of specifying colours is through a dictionary -(lines 8-11), but colours, linestyles etc. as well as atoms can also be -specified in POSCAR order. - -Manually specifying ``atoms`` (line 20) rather than reading from -the POSCAR enables the separation of different environments of the same atom, -and in this instance no POSCAR is required. The default for the ``poscar`` -argument is POSCAR, so often this will not need to be specified anyway. - -If desired, the atom-projected dos can be disabled entirely by setting -``projected`` to ``False``, and in the same way the ``total`` can be enabled -or disabled (line 17). - -To recreated experimental data more closely, Gaussian smearing can be applied -with ``sigma`` (line 21). This option may be quicker and easier than rerunning -phonopy to compare, but beware it does not detect if smearing is already -applied. A good starting value is 0.2, as used here. +.. include:: ../../examples/dos/README.rst diff --git a/docs/src/gallery.rst b/docs/src/gallery.rst index 15b5727..6df6576 100644 --- a/docs/src/gallery.rst +++ b/docs/src/gallery.rst @@ -140,6 +140,9 @@ but is easily scripted or plotted at the command line: :alt: A plot of weighted average scattering rates against temperature and carrier concnetration. :target: https://smtg-ucl.github.io/ThermoPlotter/avg-rates.html +.. codeblock:: + tp plot avg-rates ../data/basno3/mesh_75x75x75.h5 -t 1000 -n -1e19 --location 2 --large -c red -c blue -c magenta + It shows the k-point and Fermi-Dirac weighted scattering rates against temperature and carrier concentration, to give an accurate picture of their effect on the electrical conductivity. This can also be done for the electron diff --git a/docs/src/heatmaps.rst b/docs/src/heatmaps.rst index 891d1f2..7d9d9ad 100644 --- a/docs/src/heatmaps.rst +++ b/docs/src/heatmaps.rst @@ -1,84 +1 @@ --------- -Heatmaps --------- - -.. image:: ztmap.png - :alt: ZT against carrier concentration and temperature. - -This shows the classic ZT against carrier concentration and temperature -heatmap. This can be plotted at the command line with: - -.. codeblock:: - tp plot ztmap ../data/zno/boltztrap.hdf5 -k ../data/zno/kappa-m404021.hdf5 -c '#800080' -d x - -and in python with: - -.. literalinclude:: plot-ztmap.py - :language: python - :linenos: - :emphasize-lines: 22 - -This uses the ``tp.plot.heatmap.add_ztmap`` function (line 22), which -calculates the ZT and sets the axes labels. All the other functionality -is wrapped within the ``tp.plot.heatmap.add_heatmap`` function, which -enhances ``pcolourmesh`` in ways such as automatic rescaling of axes to -represent all data, automatic extension of colourbars if applicable and -custom colourmaps, which are generated dynamically with the input of a -single colour, in hex, rgb (array) or named colour format. In this -case, the ``tp.plot.colour.uniform`` colourmap generator has been used, -which calculates a uniform colourmap between white, a supplied -highlight colour and black, or three colours of your choosing. There -are several other heatmap functions in Thermoplotter, including the -equivalent for power factor, ``add_pfmap``, and those below... - -.. image:: kappa-target.png - :alt: Lattice thermal conductivity required to reach a given ZT against carrier concentration and temperature. - -It is possible to plot a ztmap without lattice thermal conductivity -data, in which case it will be set to 1 W m-1 K-1, -which will be recorded in ``data['meta']['kappa_source']`` (other -numbers can be specified). The above is an alternative, which plots the -lattice thermal conductivity required to reach a specified ZT. We hope -this is a valuable tool to help decide wether to run expensive lattice -thermal conductivity calculations on a material. The CLI is: - -.. codeblock:: - tp plot kappa-target ../data/zno/boltztrap.hdf5 -c '#008080' -d x - -and python: - -.. literalinclude:: plot-kappa-target.py - :language: python - :linenos: - :emphasize-lines: 18,19 - -.. image:: ztdiff.png - :alt: Difference between two ZTs against carrier concentration and temperature. - -The final heatmap is the ztdiff, which compares the ZTs of two -materials at a range of carrier concentrations and temperatures. This -can be done with any two materials, from completely different (e.g. -BaSnO3 and ZnO here) to the same material under different -conditions (e.g. different degrees of nanostructuring), although this -method is most appropriate for closely related materials with similar -dopabilities. At the command line: - -.. codeblock:: - tp plot ztdiff ../data/zno/boltztrap.hdf5 ../data/basno3/boltztrap.hdf5 -k ../data/zno/kappa-m404021.hdf5 ../data/basno3/kappa-m363636.hdf5 -l ZnO -l 'BaSnO$_3$' - -and in python: - -.. literalinclude:: plot-ztdiff.py - :language: python - :linenos: - :emphasize-lines: 19,20,23 - -Note how list comprehensions and expansions (lines 19-20 and 23 -respectively) can make the script tidier. On top of the colourbar (here -thrown away with ``_``), this function returns the handles and labels -which must be put into the legend manually if you want it. If you are -using an axes template with multiple sets of axes, the ``custom`` -argument must be used in ``add_legend`` to enable manual manipulation -of the handles and labels. ``add_ztdiff`` and its power factor -equivalent, ``add_pfdiff``, also make sure the colour scale is centred -on zero (i.e. where both are equal). \ No newline at end of file +.. include:: ../../examples/heatmaps/README.rst diff --git a/docs/src/phonons.rst b/docs/src/phonons.rst index 86ce9c1..12c8035 100644 --- a/docs/src/phonons.rst +++ b/docs/src/phonons.rst @@ -1,55 +1 @@ -------- -Phonons -------- - -.. image:: phonons.png - :alt: Phonon dispersion and density of states. - -This shows a phonon dispersion and density of states (DoS). This is the -only combined plot currenty possible at the command line, with: - -.. codeblock:: - tp plot phonons ../data/zno/band.yaml --dos ../data/zno/projected_dos.dat --poscar ../data/zno/POSCAR -doscolour '#d46ef9' --doscolour '#7b8eff' -c '#f0901f' - -and in python: - -.. literalinclude:: plot-phonons.py - :language: python - :linenos: - :emphasize-lines: 22,24 - -All plot-types in ``tp.plot.frequency`` have an invert argument to plot -them side-on by a phonon dispersion, which also shortens their x-axis -labels and removes their y-axis labels and tick labels (line 22). -Currently the axes of the two plots have to be aligned manually, but -this is simple (line 24). - -.. image:: multiphon.png - :alt: Phonon dispersions for different supercell sizes. - -This shows phonons dispersions for various supercell sizes for -convergence: - -.. codeblock:: - tp plot phonons ../data/basno3/band-*.yaml --legend_title "Supercell Size" -l "2 x 2 x 2" -l "3 x 3 x 3" -l "4 x 4 x 4" -l "5 x 5 x 5" - -and in python: - -.. literalinclude:: plot-multiphon.py - :language: python - :linenos: - :emphasize-lines: 17,18 - -At the command line, the same command is used for both, but in python -a separate command, ``tp.plot.phonons.add_multi`` is used. While this -could be done by looping over ``tp.plot.phonons.add_dispersion``, it -is bundled into a one-liner for convenience. As phonon dispersions do -not always have the same x-scale (at least in phonopy), this rescales -the dispersions so they always match, which is also useful to compare -different materials (so long as they are at least in the same space -group and are preferably closely related), or materials under expansion -or compression. This also demonstrates the large axes style, more -appropriate for presentations or posters than the default style, which -is better for papers; and the add_legend function, which adds a -pre-positioned legend, and accepts all the other usual arguments -including ``title`` (line 18). +.. include:: ../../examples/phonons/README.rst diff --git a/docs/src/plot-dos.py b/docs/src/plot-dos.py new file mode 100644 index 0000000..7e69d35 --- /dev/null +++ b/docs/src/plot-dos.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python3 + +import tp + +dosfile = '../data/basno3/projected_dos.dat' +poscar = '../data/basno3/POSCAR' + +colours = {'Ba': '#ff00ff', + 'Sn': '#00ffff', + 'O': '#ff0000', + 'O_2': '#ff8000'} + +fig, ax, add_legend = tp.axes.small.two_h() + +# Unsmeared, read from POSCAR with total +dos1 = tp.data.load.phonopy_dos(dosfile, poscar=poscar) +tp.plot.frequency.add_dos(ax[0], dos1, total=True, colour=colours) + +# Smeared (sigma=0.2), custom atoms with no total +dos2 = tp.data.load.phonopy_dos(dosfile, atoms='Ba Sn O O_2 2') +tp.plot.frequency.add_dos(ax[1], dos2, sigma=0.2, colour=colours) + +add_legend(title='$BaSnO_3$', location=2, ncol=1) + +fig.savefig('dos.png') \ No newline at end of file diff --git a/docs/src/prophon.rst b/docs/src/prophon.rst index 6707d43..b7ddf28 100644 --- a/docs/src/prophon.rst +++ b/docs/src/prophon.rst @@ -1,41 +1 @@ ------------------ -Projected Phonons ------------------ - -.. image:: wideband.png - :alt: Phonon dispersion with broadened bands. - -This shows a phonon dispersion with bands broadened to indicate -scattering (the widest bands scatter the most). Via the command-line: - -.. codeblock:: - tp plot wideband ../data/zno/band.yaml ../data/zno/kappa-m404021.hdf5 -c black -c red -s dark_background -p POSCAR - -and in python: - -.. literalinclude:: plot-wideband.py - :language: python - :linenos: - :emphasize-lines: 16,19,23,24 - -This also demonstrates the custom colourmaps which can be generated by -supplying a single hex, rgb (array) or named colour as the colour -input, or in this case two (the default background is normally white, -but to match the ``dark_background`` style sheet, black has been used -here). In this case, ``tp.plot.colour.linear`` has been used. It also -shows the large style, more suitable to presentations or posters than -the default one which is more suited to papers. Instead of the actual -quantities you want to load, ``wideband`` can select all necessary for -this plot. - -.. image:: prophon.png - :alt: Phonon dispersion with phonon lifetime projected. - -This shows a phonon dispersion with phonon lifetime projected on the -colour axis. It is a more quantitative version of the above wideband -plot. It can be plotted in python with: - -.. literalinclude:: plot-projected-phonons.py - :language: python - :linenos: - :emphasize-lines: 25-27 +.. include:: ../../examples/projected-phonons/README.rst diff --git a/docs/src/waterfalls.rst b/docs/src/waterfalls.rst index 2834faf..d0cfc7d 100644 --- a/docs/src/waterfalls.rst +++ b/docs/src/waterfalls.rst @@ -1,54 +1 @@ ---------- -Waterfall ---------- - -.. image:: waterfall.png - :alt: Waterfall plot of mean free path against frequency with lattice thermal conductivity projected. - -This shows a plot of mean free path against frequency with lattice -thermal conductivity projected on the colour axis, as well as a density -of states (DoS). It shows the connections between phonon scattering and -the constituent elements, for example there seems to be a big drop in -phonon mean free path in the frequencies with high amounts of Ba -character. - -While plots cannot be layered at the command line like this, the -waterfall section can be plotted with: - -.. codeblock:: - tp plot waterfall ../data/basno3/kappa-m363636.hdf5 -y mfp --projected kappa - -And in python: - -.. literalinclude:: plot-waterfall.py - :language: python - :linenos: - :emphasize-lines: 36,37 - -This example highlights a complication: in order for the -waterfall to be on top of the DoS, so as not to obscure the projected -colour, an additional command, ``format_waterfall``, must be used so -the DoS can be scaled correctly but plotted first (line 36-37). - -Replacing ``add_projected_waterfall`` with simply ``add_waterfall`` -removes the projection, and colours it with a single colour or by band -index as you prefer. This is achieved by removing the ``--projected`` -tag at the command line. - -.. image:: density.png - :alt: Density waterfall plot of mean free path against frequency. - -This is an alternative of the waterfall plot, where the colour darkness -shows the density of the points, which is often clearer than just -adjusting the alpha parameter on the regular waterfall plot. - -It can be plotted at the command line with: - -.. codeblock:: - tp plot waterfall ../data/basno3/kappa-m363636.hdf5 -y mfp --projected density -c Blues - -Or in python with: - -.. literalinclude:: plot-density.py - :language: python - :linenos: +.. include:: ../../examples/waterfall/README.rst diff --git a/docs/waterfalls.html b/docs/waterfalls.html index de0d0c6..c3a77f7 100644 --- a/docs/waterfalls.html +++ b/docs/waterfalls.html @@ -16,7 +16,7 @@ - + @@ -42,7 +42,6 @@
  28. Density of States (DoS)
  29. Cumulative Kappa
  30. Waterfalls
  31. -
  32. Projected Phonons
  33. Average Scattering Rates
  34. Reference
  35. @@ -283,7 +282,7 @@

    Waterfall - +


    diff --git a/examples/02-individual-customisation/README.rst b/examples/02-individual-customisation/README.rst index e75eeed..83ddcce 100644 --- a/examples/02-individual-customisation/README.rst +++ b/examples/02-individual-customisation/README.rst @@ -67,7 +67,7 @@ ThermoPlotter's `colourmap generators`_. These can also be accessed directly, allowing greater control of the output or use on non-tp applications. The colourmap used here could be generated with: -.. codeblock:: python +.. code-block:: python tp.plot.colour.linear(cmin='black', cmax='red') ``linear`` interpolates linearly between the colours, and is used for diff --git a/examples/dos/README.rst b/examples/dos/README.rst index 81a6cea..e6e9f00 100644 --- a/examples/dos/README.rst +++ b/examples/dos/README.rst @@ -10,13 +10,13 @@ modes at particular frequencies. The left plot can be generated by: -.. codeblock:: +.. code-block:: tp plot dos ../data/basno3/projected_dos.dat --total --nolegend -c pink -c cyan -c red -p ../data/basno3/POSCAR And the right plot by: -.. codeblock:: +.. code-block:: tp plot dos ../data/basno3/projected_dos.dat --atoms "Ba Sn O O_2 2" --sigma 0.2 --location 1 -c magenta -c cyan -c red -c orange diff --git a/examples/heatmaps/README.rst b/examples/heatmaps/README.rst index 588736e..70d38c2 100644 --- a/examples/heatmaps/README.rst +++ b/examples/heatmaps/README.rst @@ -8,12 +8,13 @@ Heatmaps This shows the classic ZT against carrier concentration and temperature heatmap. This can be plotted at the command line with: -.. codeblock:: +.. code-block:: + tp plot ztmap ../data/zno/boltztrap.hdf5 -k ../data/zno/kappa-m404021.hdf5 -c '#800080' -d x and in python with: -.. literalinclude:: plot-ztmap.py +.. literalinclude:: plot-zt.py :language: python :linenos: :emphasize-lines: 22 @@ -42,7 +43,8 @@ lattice thermal conductivity required to reach a specified ZT. We hope this is a valuable tool to help decide wether to run expensive lattice thermal conductivity calculations on a material. The CLI is: -.. codeblock:: +.. code-block:: + tp plot kappa-target ../data/zno/boltztrap.hdf5 -c '#008080' -d x and python: @@ -63,7 +65,8 @@ conditions (e.g. different degrees of nanostructuring), although this method is most appropriate for closely related materials with similar dopabilities. At the command line: -.. codeblock:: +.. code-block:: + tp plot ztdiff ../data/zno/boltztrap.hdf5 ../data/basno3/boltztrap.hdf5 -k ../data/zno/kappa-m404021.hdf5 ../data/basno3/kappa-m363636.hdf5 -l ZnO -l 'BaSnO$_3$' and in python:

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    ThermoPlotter offers ways to do the first three (the last is trivial), diff --git a/docs/tutorial-02.html b/docs/tutorial-02.html index a7c11ca..6f65f81 100644 --- a/docs/tutorial-02.html +++ b/docs/tutorial-02.html @@ -45,6 +45,7 @@

  37. Axes (line 20)
  38. Load (lines 23-24)
  39. Plot (lines 27-28)
  40. +
  41. Colourmaps
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