diff --git a/doc/source/images/1-quantum_wire.py.diff b/doc/source/images/1-quantum_wire.py.diff
index ed74c0973b9cad8cf2ae73067f22a21422d0c192..30d2dc9975651db99a185152b25d3d3bf38d1713 100644
--- a/doc/source/images/1-quantum_wire.py.diff
+++ b/doc/source/images/1-quantum_wire.py.diff
@@ -1,26 +1,29 @@
--- original
+++ modified
-@@ -9,6 +9,7 @@
- # - Using the simple sparse solver for computing Landauer conductance
+@@ -10,6 +10,8 @@
+ from matplotlib import pyplot
import kwant
-+import latex, html
++import latex
++import html
# First, define the tight-binding system
-@@ -73,7 +74,8 @@
+@@ -73,8 +75,9 @@
+ sys.attach_lead(lead1)
# Plot it, to make sure it's OK
-
+-
-kwant.plot(sys)
-+kwant.plot(sys, "1-quantum_wire_sys.pdf", width=latex.figwidth_pt)
-+kwant.plot(sys, "1-quantum_wire_sys.png", width=html.figwidth_px)
++size = (latex.figwidth_in, 0.3 * latex.figwidth_in)
++kwant.plot(sys, file="1-quantum_wire_sys.pdf", fig_size=size, dpi=html.dpi)
++kwant.plot(sys, file="1-quantum_wire_sys.png", fig_size=size, dpi=html.dpi)
# Finalize the system
-@@ -98,8 +100,14 @@
+@@ -98,8 +101,13 @@
+ # Use matplotlib to write output
# We should see conductance steps
- from matplotlib import pyplot
-pyplot.figure()
+fig = pyplot.figure()
@@ -32,8 +35,7 @@
+pyplot.ylabel("conductance [in units of e^2/h]", fontsize=latex.mpl_label_size)
+pyplot.setp(fig.get_axes()[0].get_xticklabels(), fontsize=latex.mpl_tick_size)
+pyplot.setp(fig.get_axes()[0].get_yticklabels(), fontsize=latex.mpl_tick_size)
-+fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+fig.savefig("1-quantum_wire_result.pdf")
-+fig.savefig("1-quantum_wire_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++fig.savefig("1-quantum_wire_result.png", dpi=html.dpi)
diff --git a/doc/source/images/2-ab_ring.py.diff b/doc/source/images/2-ab_ring.py.diff
index 29bd464fd4dc9b4573c7871ec43b923edf93589c..1075a5ce81fad6d59ee4e43ce3a4cb182f4a4109 100644
--- a/doc/source/images/2-ab_ring.py.diff
+++ b/doc/source/images/2-ab_ring.py.diff
@@ -81,11 +81,10 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("2-ab_ring_result.pdf")
-+ fig.savefig("2-ab_ring_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("2-ab_ring_result.png", dpi=html.dpi)
def main():
@@ -93,8 +92,9 @@
# Check that the system looks as intended.
- kwant.plot(sys)
-+ kwant.plot(sys, "2-ab_ring_sys.pdf", width=latex.figwidth_pt)
-+ kwant.plot(sys, "2-ab_ring_sys.png", width=html.figwidth_px)
++ size = (latex.figwidth_in, latex.figwidth_in)
++ kwant.plot(sys, file="2-ab_ring_sys.pdf", fig_size=size, dpi=html.dpi)
++ kwant.plot(sys, file="2-ab_ring_sys.png", fig_size=size, dpi=html.dpi)
# Finalize the system.
sys = sys.finalized()
@@ -104,11 +104,11 @@
+ # Finally, some plots needed for the notes
+ sys = make_system_note1()
-+ kwant.plot(sys, "2-ab_ring_note1.pdf", width=latex.figwidth_small_pt)
-+ kwant.plot(sys, "2-ab_ring_note1.png", width=html.figwidth_small_px)
++ kwant.plot(sys, file="2-ab_ring_note1.pdf", fig_size=size, dpi=html.dpi)
++ kwant.plot(sys, file="2-ab_ring_note1.png", fig_size=size, dpi=html.dpi)
+ sys = make_system_note2()
-+ kwant.plot(sys, "2-ab_ring_note2.pdf", width=latex.figwidth_small_pt)
-+ kwant.plot(sys, "2-ab_ring_note2.png", width=html.figwidth_small_px)
++ kwant.plot(sys, file="2-ab_ring_note2.pdf", fig_size=size, dpi=html.dpi)
++ kwant.plot(sys, file="2-ab_ring_note2.png", fig_size=size, dpi=html.dpi)
+
+
# Call the main function if the script gets executed (as opposed to imported).
diff --git a/doc/source/images/2-quantum_well.py.diff b/doc/source/images/2-quantum_well.py.diff
index b3e158e4e955fc36decf046d494e248248e9a358..579ba29460feff9a3f1db0827a4ee8b72589b022 100644
--- a/doc/source/images/2-quantum_well.py.diff
+++ b/doc/source/images/2-quantum_well.py.diff
@@ -8,7 +8,7 @@
# global variable governing the behavior of potential() in
# make_system()
-@@ -76,19 +77,26 @@
+@@ -76,19 +77,25 @@
smatrix = kwant.solve(sys, energy)
data.append(smatrix.transmission(1, 0))
@@ -26,11 +26,10 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("2-quantum_well_result.pdf")
-+ fig.savefig("2-quantum_well_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("2-quantum_well_result.png", dpi=html.dpi)
def main():
diff --git a/doc/source/images/2-spin_orbit.py.diff b/doc/source/images/2-spin_orbit.py.diff
index ec1bbd826eb7a7a28a453ed43d7d4ccde9afe649..d935aa9411b4f4d7c86871e931c7c6228d3508f0 100644
--- a/doc/source/images/2-spin_orbit.py.diff
+++ b/doc/source/images/2-spin_orbit.py.diff
@@ -8,7 +8,7 @@
# define Pauli-matrices for convenience
sigma_0 = numpy.eye(2)
-@@ -73,19 +74,25 @@
+@@ -73,19 +74,24 @@
smatrix = kwant.solve(sys, energy)
data.append(smatrix.transmission(1, 0))
@@ -25,11 +25,10 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("2-spin_orbit_result.pdf")
-+ fig.savefig("2-spin_orbit_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("2-spin_orbit_result.png", dpi=html.dpi)
def main():
diff --git a/doc/source/images/3-band_structure.py.diff b/doc/source/images/3-band_structure.py.diff
index 0ca2e45df01c597c3b76c782b6fe0eb5608c110c..277316381a5e55a82570c587951ce8244073e03f 100644
--- a/doc/source/images/3-band_structure.py.diff
+++ b/doc/source/images/3-band_structure.py.diff
@@ -8,7 +8,7 @@
def make_lead(a=1, t=1.0, W=10):
-@@ -39,11 +40,20 @@
+@@ -39,11 +40,19 @@
# the bandstructure
energy_list = [lead.energies(k) for k in momenta]
@@ -25,11 +25,10 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("3-band_structure_result.pdf")
-+ fig.savefig("3-band_structure_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("3-band_structure_result.png", dpi=html.dpi)
def main():
diff --git a/doc/source/images/3-closed_system.py.diff b/doc/source/images/3-closed_system.py.diff
index 0bb1a9c1ffcfbac10fa1d83d1ac133ed3c6b9507..7394098d154243250ba640a67d32ab87c65fd58f 100644
--- a/doc/source/images/3-closed_system.py.diff
+++ b/doc/source/images/3-closed_system.py.diff
@@ -8,7 +8,7 @@
def make_system(a=1, t=1.0, r=10):
-@@ -69,18 +70,30 @@
+@@ -69,19 +70,24 @@
# we only plot the 15 lowest eigenvalues
energies.append(ev[:15])
@@ -25,22 +25,18 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("3-closed_system_result.pdf")
-+ fig.savefig("3-closed_system_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("3-closed_system_result.png", dpi=html.dpi)
def main():
sys = make_system()
- # Check that the system looks as intended.
+- # Check that the system looks as intended.
- kwant.plot(sys)
-+ kwant.plot(sys, filename="3-closed_system_sys.pdf",
-+ width=latex.figwidth_pt)
-+ kwant.plot(sys, filename="3-closed_system_sys.png",
-+ width=html.figwidth_px)
-
+-
# Finalize the system.
sys = sys.finalized()
+
diff --git a/doc/source/images/4-graphene.py.diff b/doc/source/images/4-graphene.py.diff
index c86c7b55e1f331c641221f8af55639a3b553d57f..09401251ca6840faed6ca03f25b42622906423ff 100644
--- a/doc/source/images/4-graphene.py.diff
+++ b/doc/source/images/4-graphene.py.diff
@@ -1,14 +1,15 @@
--- original
+++ modified
-@@ -17,6 +17,7 @@
+@@ -17,6 +17,8 @@
# For plotting
from matplotlib import pyplot
-+import latex, html
++import latex
++import html
# Define the graphene lattice
-@@ -63,7 +64,7 @@
+@@ -63,7 +65,7 @@
return (-1 < x < 1) and (-0.4 * r < y < 0.4 * r)
lead0 = kwant.Builder(sym0)
@@ -17,7 +18,7 @@
for hopping in hoppings:
lead0[lead0.possible_hoppings(*hopping)] = -1
-@@ -105,11 +106,21 @@
+@@ -105,11 +107,20 @@
smatrix = kwant.solve(sys, energy)
data.append(smatrix.transmission(0, 1))
@@ -35,15 +36,14 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("4-graphene_result.pdf")
-+ fig.savefig("4-graphene_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("4-graphene_result.png", dpi=html.dpi)
def plot_bandstructure(flead, momenta):
-@@ -117,11 +128,21 @@
+@@ -117,11 +128,20 @@
# the bandstructure
energy_list = [flead.energies(k) for k in momenta]
@@ -61,37 +61,38 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("4-graphene_bs.pdf")
-+ fig.savefig("4-graphene_bs.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("4-graphene_bs.png", dpi=html.dpi)
def main():
-@@ -136,17 +157,20 @@
- lcol=kwant.plotter.black)}
+@@ -134,17 +154,22 @@
+ return 0 if site.group == a else 1
# Plot the closed system without leads.
-- kwant.plot(sys, symbols=plotter_symbols)
+- kwant.plot(sys, site_color=group_colors, colorbar=False)
-
- # Compute some eigenvalues.
- compute_evs(sys.finalized())
-+ kwant.plot(sys, symbols=plotter_symbols,
-+ filename="4-graphene_sys1.pdf", width=latex.figwidth_pt)
-+ kwant.plot(sys, symbols=plotter_symbols,
-+ filename="4-graphene_sys1.png", width=html.figwidth_px)
++ size = (latex.figwidth_in, latex.figwidth_in)
++ kwant.plot(sys, site_color=group_colors, colorbar=False,
++ file="4-graphene_sys1.pdf", fig_size=size, dpi=html.dpi)
++ kwant.plot(sys, site_color=group_colors, colorbar=False,
++ file="4-graphene_sys1.png", fig_size=size, dpi=html.dpi)
# Attach the leads to the system.
for lead in leads:
sys.attach_lead(lead)
# Then, plot the system with leads.
-- kwant.plot(sys, symbols=plotter_symbols)
-+ kwant.plot(sys, symbols=plotter_symbols,
-+ filename="4-graphene_sys2.pdf", width=latex.figwidth_pt)
-+ kwant.plot(sys, symbols=plotter_symbols,
-+ filename="4-graphene_sys2.png", width=html.figwidth_px)
+- kwant.plot(sys, site_color=group_colors, colorbar=False)
++ size = (latex.figwidth_in, 0.9 * latex.figwidth_in)
++ kwant.plot(sys, site_color=group_colors, colorbar=False,
++ file="4-graphene_sys2.pdf", fig_size=size, dpi=html.dpi)
++ kwant.plot(sys, site_color=group_colors, colorbar=False,
++ file="4-graphene_sys2.png", fig_size=size, dpi=html.dpi)
# Finalize the system.
sys = sys.finalized()
diff --git a/doc/source/images/5-superconductor_band_structure.py.diff b/doc/source/images/5-superconductor_band_structure.py.diff
index ee2fbdd6dd7500a0c6c34153447c38aece0ab6c4..7392cd639bca0e8f253f26978dedfb8a30aaea65 100644
--- a/doc/source/images/5-superconductor_band_structure.py.diff
+++ b/doc/source/images/5-superconductor_band_structure.py.diff
@@ -8,7 +8,7 @@
tau_x = np.array([[0, 1], [1, 0]])
tau_z = np.array([[1, 0], [0, -1]])
-@@ -46,12 +47,20 @@
+@@ -46,12 +47,19 @@
# the bandstructure
energy_list = [lead.energies(k) for k in momenta]
@@ -23,11 +23,10 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("5-superconductor_band_structure_result.pdf")
-+ fig.savefig("5-superconductor_band_structure_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("5-superconductor_band_structure_result.png", dpi=html.dpi)
def main():
diff --git a/doc/source/images/5-superconductor_transport.py.diff b/doc/source/images/5-superconductor_transport.py.diff
index 241d417dfc0ce8261658f2a10c6afc29deb35958..8f6d6d9dcf8f9e42cf8c6083be3a814ebc2e7bfc 100644
--- a/doc/source/images/5-superconductor_transport.py.diff
+++ b/doc/source/images/5-superconductor_transport.py.diff
@@ -8,7 +8,7 @@
def make_system(a=1, W=10, L=10, barrier=1.5, barrierpos=(3, 4),
-@@ -95,19 +96,24 @@
+@@ -95,19 +96,23 @@
smatrix.transmission(0, 0) +
smatrix.transmission(1, 0))
@@ -22,11 +22,10 @@
+ fontsize=latex.mpl_tick_size)
+ pyplot.setp(fig.get_axes()[0].get_yticklabels(),
+ fontsize=latex.mpl_tick_size)
-+ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in*3./4.)
++ fig.set_size_inches(latex.mpl_width_in, latex.mpl_width_in * 3. / 4.)
+ fig.subplots_adjust(left=0.15, right=0.95, top=0.95, bottom=0.15)
+ fig.savefig("5-superconductor_transport_result.pdf")
-+ fig.savefig("5-superconductor_transport_result.png",
-+ dpi=(html.figwidth_px/latex.mpl_width_in))
++ fig.savefig("5-superconductor_transport_result.png", dpi=html.dpi)
def main():
diff --git a/doc/source/images/html.py b/doc/source/images/html.py
index 120f7de2110c088c243dd72b3be884a431a0eb79..e0415a3b3aab1fe67d8f87c77a80829c1f926175 100644
--- a/doc/source/images/html.py
+++ b/doc/source/images/html.py
@@ -1,4 +1,2 @@
-# Default width of figures in pixels
-figwidth_px = 600
-# Width for smaller figures
-figwidth_small_px = 400
+# dpi for conversion from inches
+dpi = 90
diff --git a/doc/source/images/latex.py b/doc/source/images/latex.py
index 870c858e678a4d1a9f1a9ebdec2b5664f7410e5d..4abf019cc69b4b1ed730045fe2c32e53dc2ff9d3 100644
--- a/doc/source/images/latex.py
+++ b/doc/source/images/latex.py
@@ -1,13 +1,14 @@
-pt_to_in = 1./72.
+pt_to_in = 1. / 72.
# Default width of figures in pts
-figwidth_pt = 300
+figwidth_pt = 600
+figwidth_in = figwidth_pt * pt_to_in
# Width for smaller figures
-figwidth_small_pt = 200
+figwidth_small_pt = 400
figwidth_small_in = figwidth_small_pt * pt_to_in
# Sizes for matplotlib figures
mpl_width_in = figwidth_pt * pt_to_in
-mpl_label_size = 10 # font sizes in points
-mpl_tick_size = 9
+mpl_label_size = None # font sizes in points
+mpl_tick_size = None
diff --git a/doc/source/reference/kwant.plotter.rst b/doc/source/reference/kwant.plotter.rst
index d88d9b53795c94a921c1313347c78635d1cc8c34..0ffcae77e9671cd446aa4664ffbc4c6e9dec2cae 100644
--- a/doc/source/reference/kwant.plotter.rst
+++ b/doc/source/reference/kwant.plotter.rst
@@ -10,30 +10,16 @@ Plotting routine
:toctree: generated/
plot
- show
- interpolate
+ map
-Auxiliary types
-----------------
+Data-generating functions
+-------------------------
.. autosummary::
:toctree: generated/
- Circle
- Polygon
- Line
- LineStyle
- Color
+ sys_leads_sites
+ sys_leads_hoppings
+ sys_leads_pos
+ sys_leads_hopping_pos
+ mask_interpolate
-Pre-defined colors
-------------------
-+------------------------+
-| `~kwant.plotter.black` |
-+------------------------+
-| `~kwant.plotter.white` |
-+------------------------+
-| `~kwant.plotter.red` |
-+------------------------+
-| `~kwant.plotter.green` |
-+------------------------+
-| `~kwant.plotter.blue` |
-+------------------------+
diff --git a/doc/source/tutorial/1-quantum_wire.py b/doc/source/tutorial/1-quantum_wire.py
index 9e8ad75a5d3ea08f54e337dc229327073c0b56e0..670f99a55ed0f17e81cc9df94bb157a599c96219 100644
--- a/doc/source/tutorial/1-quantum_wire.py
+++ b/doc/source/tutorial/1-quantum_wire.py
@@ -8,6 +8,7 @@
# - Making scattering region and leads
# - Using the simple sparse solver for computing Landauer conductance
+from matplotlib import pyplot
#HIDDEN_BEGIN_dwhx
import kwant
#HIDDEN_END_dwhx
@@ -119,7 +120,6 @@ for ie in xrange(100):
# Use matplotlib to write output
# We should see conductance steps
#HIDDEN_BEGIN_lliv
-from matplotlib import pyplot
pyplot.figure()
pyplot.plot(energies, data)
diff --git a/doc/source/tutorial/4-graphene.py b/doc/source/tutorial/4-graphene.py
index e024b7f0bee2a43882553aedc862d09ebe1bf5aa..147fd890038defb62afd4fb9419529af1c6888e9 100644
--- a/doc/source/tutorial/4-graphene.py
+++ b/doc/source/tutorial/4-graphene.py
@@ -151,13 +151,11 @@ def main():
# To highlight the two sublattices of graphene, we plot one with
# a filled, and the other one with an open circle:
- plotter_symbols = {a: kwant.plotter.Circle(r=0.3),
- b: kwant.plotter.Circle(r=0.3,
- fcol=kwant.plotter.white,
- lcol=kwant.plotter.black)}
+ def group_colors(site):
+ return 0 if site.group == a else 1
# Plot the closed system without leads.
- kwant.plot(sys, symbols=plotter_symbols)
+ kwant.plot(sys, site_color=group_colors, colorbar=False)
#HIDDEN_END_itkk
# Compute some eigenvalues.
@@ -170,7 +168,7 @@ def main():
sys.attach_lead(lead)
# Then, plot the system with leads.
- kwant.plot(sys, symbols=plotter_symbols)
+ kwant.plot(sys, site_color=group_colors, colorbar=False)
# Finalize the system.
sys = sys.finalized()
diff --git a/doc/source/tutorial/tutorial4.rst b/doc/source/tutorial/tutorial4.rst
index 4d9eb5f4402e377fba10f9616168b266e700bbb7..528850fb85a00130c7bfad3d4cc14cb4c8ccc8e5 100644
--- a/doc/source/tutorial/tutorial4.rst
+++ b/doc/source/tutorial/tutorial4.rst
@@ -118,27 +118,16 @@ plot the system:
:start-after: #HIDDEN_BEGIN_itkk
:end-before: #HIDDEN_END_itkk
-We customize the plotting: `plotter_symbols` is a dictionary with the
-sublattice objects `a` and `b` as keys, and the `~kwant.plotter.Circle` objects
-specify that the sublattice `a` should be drawn using a filled black circle,
-and `b` using a white circle with a black outline. ::
-
- plotter_symbols = {a: kwant.plotter.Circle(r=0.3),
- b: kwant.plotter.Circle(r=0.3,
- fcol=kwant.plotter.white,
- lcol=kwant.plotter.black)}
-
-The radius of the circle is given in relative units: `~kwant.plotter.plot` uses
-a typical length scale as a reference length. By default, the typical length
-scale is the smallest distance between lattice points. `~kwant.plotter.plot`
-can find this length by itself, but must then go through all
-hoppings. Alternatively, one can specify the typical length scale using the
-argument `a` as in the example (not to be confused with the sublattice `a`)
-which is here set to the distance between carbon atoms in the graphene
-lattice. Specifying ``r=0.3`` in `~kwant.plotter.Circle` hence means that the
-radius of the circle is 30% of the carbon-carbon distance. Using this relative
-unit it is easy to make good-looking plots where the symbols cover a
-well-defined part of the plot.
+We customize the plotting: we set the `site_colors` argument of
+`~kwant.plotter.plot` to a function which returns 0 for
+sublattice `a` and 1 for sublattice `b`::
+
+ def group_colors(site):
+ return 0 if site.group == a else 1
+
+The function `~kwant.plotter.plot` shows these values using a color scale
+(grayscale by default). The symbol `size` is specified in points, and is
+independent on the overall figure size.
Plotting the closed system gives this result:
diff --git a/doc/source/whatsnew/0.2.rst b/doc/source/whatsnew/0.2.rst
index 1344b7afc27740fa215ecf3bdac7ea791c68644c..2fc2710d112e2e95cba32d46407b10168772be76 100644
--- a/doc/source/whatsnew/0.2.rst
+++ b/doc/source/whatsnew/0.2.rst
@@ -29,33 +29,31 @@ New tutorial dealing with superconductivity
-------------------------------------------
:doc:`../tutorial/tutorial5`
-`~kwant.plotter.plot` more useful for low level systems
--------------------------------------------------------
-The behavior of `~kwant.plotter.plot` has been changed when a `low level system
-<kwant.system.System>` is plotted. Previously, only low level systems which
-were finalized `builders <kwant.builder.Builder>` were supported and there was
-no difference between plotting a low-level system and a builder.
+New `~kwant.plotter` module
+---------------------------
+`~kwant.plotter` has been rewritten using `matplotlib`, which allows
+plot post-processing, basic 3D plotting and many other features. Due to the
+possibility to easily modify a `matplotlib` plot after it has been generated,
+function `~kwant.plotter.plot` has much fewer input parameters, and is less
+flexible than its previous implementation. Its interface is also much more
+similar to that of `matplotlib`. For the detailed interface and input
+description check `~kwant.plotter.plot` documentation.
-* Arguments of plot which are functions are given site numbers in place of
- `~kwant.builder.Site` objects when plotting a low level system. This
- provides an easy way to make the appearance of lines and symbols depend on
- computation results.
+The behavior of `~kwant.plotter.plot` with low level systems has changed.
+Arguments of plot which are functions are given site numbers in place of
+`~kwant.builder.Site` objects when plotting a low level system. This
+provides an easy way to make the appearance of lines and symbols depend on
+computation results.
-* Only the scattering region (without leads) is plotted for low level systems.
-
-* For plotting low level systems, dictionaries with site group keys are no
- longer supported as plot arguments.
+A new function `~kwant.plotter.map` was implemented. It allows to show a map of
+spatial dependence of a function of a system site (e.g. density of states)
+without showing the sites themselves.
Calculation of the local density of states
------------------------------------------
The new function of sparse solvers `~kwant.solvers.common.SparseSolver.ldos`
allows the calculation of the local density of states.
-Plotting of functions of system sites
--------------------------------------
-The new function `kwant.plotter.show` plots functions of the system, i.e. the
-potential or the LDOS.
-
Return value of sparse solver
-----------------------------
The function `~kwant.solvers.common.SparseSolver.solve` of sparse solvers now
@@ -63,6 +61,6 @@ always returns a single instance of `~kwant.solvers.common.BlockResult`. The
latter has been generalized to include more information for leads defined as
infinite systems.
-Return value of `~kwant.solvers.sparse.make_linear_sys` has changed
--------------------------------------------------------------------
+Return value of `~kwant.solvers.common.SparseSolver.make_linear_sys` has changed
+--------------------------------------------------------------------------------
A namedtuple is used for more clarity.
diff --git a/kwant/plotter.py b/kwant/plotter.py
index 46ccdd314726935e31ca1a424f054a82187516a0..a73ecc3495cfbf4db5353da98befc08e4532a4f7 100644
--- a/kwant/plotter.py
+++ b/kwant/plotter.py
@@ -1,876 +1,704 @@
-"""kwant.plotter docstring"""
+"""Plotter module for kwant.
-from __future__ import division
-from math import sqrt, pi, sin, cos, tan
-import scipy.interpolate
-from numpy import dot, add, subtract
-import numpy as np
+This module provides iterators useful for any plotter routine, such as a list
+of system sites, their coordinates, lead sites at any lead unit cell, etc. If
+`matplotlib` is available, it also provides simple functions for plotting the
+system in two or three dimensions.
+"""
+
+import itertools
import warnings
-import cairo
-import matplotlib.pyplot as plt
+import numpy as np
+from scipy import spatial, interpolate
+# All matplotlib imports must be isolated in a try, because even without
+# matplotlib iterators remain useful. Further, mpl_toolkits used for 3D
+# plotting are also imported separately, to ensure that 2D plotting works even
+# if 3D does not.
try:
- import Image
- defaultname = None
- has_pil = True
-except:
- defaultname = "plot.pdf"
- has_pil = False
-
-from . import builder, system
+ import matplotlib
+ from matplotlib.figure import Figure
+ from matplotlib import collections
+ from matplotlib.backends.backend_agg import FigureCanvasAgg
+ _mpl_enabled = True
+ from matplotlib.cbook import is_string_like, is_sequence_of_strings
+ try:
+ from mpl_toolkits import mplot3d
+ except ImportError:
+ warnings.warn("3D plotting not available.")
+except ImportError:
+ warnings.warn("matplotlib is not available, only iterator-providing"
+ "functions will work.")
+ _mpl_enabled = False
+
+from . import system, builder
+
+__all__ = ['plot', 'map', 'sys_leads_sites', 'sys_leads_hoppings',
+ 'sys_leads_pos', 'sys_leads_hopping_pos', 'mask_interpolate']
+
+
+# matplotlib helper functions.
+
+def set_edge_colors(color, collection, cmap, norm=None):
+ """Process a color specification to a format accepted by collections.
-__all__ = ['plot', 'Circle', 'Polygon', 'Line', 'Color', 'LineStyle',
- 'black', 'white', 'red', 'green', 'blue',
- 'interpolate', 'show']
-
-class Color(object):
- """RGBA color.
+ Parameters
+ ----------
+ color : color specification
+ collection : instance of a subclass of `matplotlib.collections.Collection`
+ Collection to which the color is added.
+ cmap : `matplotlib` color map specification or None
+ Color map to be used if colors are specified as floats.
+ norm : `matplotlib` color norm
+ Norm to be used if colors are specified as floats.
+ """
+ length = len(collection.get_paths())
+ if isinstance(collection, mplot3d.art3d.Line3DCollection):
+ length = len(collection._segments3d) # Once again, matplotlib fault!
+ color_is_stringy = is_string_like(color) or is_sequence_of_strings(color)
+ if not color_is_stringy:
+ color = np.asanyarray(color)
+ if color.size == length:
+ color = np.ma.ravel(color)
+ if color_is_stringy:
+ colors = matplotlib.colors.colorConverter.to_rgba_array(color)
+ else:
+ # The inherent ambiguity is resolved in favor of color
+ # mapping, not interpretation as rgb or rgba:
+ if color.size == length:
+ colors = None # use cmap, norm after collection is created
+ else:
+ colors = matplotlib.colors.colorConverter.to_rgba_array(color)
+ collection.set_color(colors)
+ if colors is None:
+ if norm is not None and not isinstance(norm,
+ matplotlib.colors.Normalize):
+ raise ValueError('Illegal value of norm.')
+ collection.set_array(np.asarray(color))
+ collection.set_cmap(cmap)
+ collection.set_norm(norm)
- Standard Color object that can be used to specify colors in
- `plot`.
- When creating the Color object, the color is specified in an RGBA scheme,
- i.e. by specifying the red (r), green (g) and blue (b) components
- of the color and optionally an alpha channel controlling the transparancy.
+def lines(axes, x0, x1, y0, y1, colors='k', linestyles='solid', cmap=None,
+ norm=None, **kwargs):
+ """Add a collection of line segments to an axes instance.
Parameters
----------
- r, g, b : float in the range [0, 1]
- specifies the values of the red, green and blue components of the color
- alpha : float in the range [0, 1], optional
- specifies the transparancy, with alpha=0 completely transparent and
- alpha=1 completely opaque (not transparent).
- Defaults to 1 (opaque).
+ axes : matplotlib.axes.Axes instance
+ Axes to which the lines have to be added.
+ x0 : array_like
+ Starting x-coordinates of each line segment
+ x1 : array_like
+ Ending x-coordinates of each line segment
+ y0 : array_like
+ Starting y-coordinates of each line segment
+ y1 : array_like
+ Ending y-coordinates of each line segment
+ colors : color definition, optional
+ Either a single object that is a proper matplotlib color definition
+ or a sequence of such objects of appropriate length. Defaults to all
+ segments black.
+ linestyles :linestyle definition, optional
+ Either a single object that is a proper matplotlib line style
+ definition or a sequence of such objects of appropriate length.
+ Defaults to all segments solid.
+ cmap : `matplotlib` color map specification or None
+ Color map to be used if colors are specified as floats.
+ norm : `matplotlib` color norm
+ Norm to be used if colors are specified as floats.
+ **kwargs : dict
+ keyword arguments to pass to `matplotlib.collections.LineCollection`.
- Examples
- --------
- The color black is specified using
+ Returns
+ -------
+ `matplotlib.collections.LineCollection` instance containing all the
+ segments that were added.
+ """
+ coords = (y0, y1, x0, x1)
- >>> black = Color(0, 0, 0)
+ if not all(len(coord) == len(y0) for coord in coords):
+ raise ValueError('Incompatible lengths of coordinate arrays.')
- and white using
+ if len(x0) == 0:
+ coll = collections.LineCollection([], linestyles=linestyles)
+ axes.add_collection(coll)
+ axes.autoscale_view()
+ return coll
- >>> white = Color(1, 1, 1)
+ segments = (((i[0], i[1]), (i[2], i[3])) for
+ i in itertools.izip(x0, y0, x1, y1))
+ coll = collections.LineCollection(segments, linestyles=linestyles)
+ set_edge_colors(colors, coll, cmap, norm)
+ axes.add_collection(coll)
+ coll.update(kwargs)
- By default, a color is completely opaque (not transparent). Using the
- optional parameter alpha one can specify transparancy. For example,
+ minx = min(x0.min(), x1.min())
+ maxx = max(x0.max(), x1.max())
+ miny = min(y0.min(), y1.min())
+ maxy = max(y0.max(), y1.max())
- >>> black_transp = Color(0, 0, 0, alpha=0.5)
+ corners = (minx, miny), (maxx, maxy)
- is black with 50% transparancy.
+ axes.update_datalim(corners)
+ axes.autoscale_view()
- """
- def __init__(self, r, g, b, alpha=1.0):
- for val in (r, g, b, alpha):
- if val < 0 or val > 1:
- raise ValueError("r, g, b, and alpha must be in "
- "the range [0,1]")
- self.r = r
- self.g = g
- self.b = b
- self.alpha = alpha
-
- def _set_color_cairo(self, ctx, fading=None):
- if fading is not None:
- ctx.set_source_rgba(self.r + fading[1] * (fading[0].r - self.r),
- self.g + fading[1] * (fading[0].g - self.g),
- self.b + fading[1] * (fading[0].b - self.b),
- self.alpha + fading[1] *
- (fading[0].alpha - self.alpha))
- else:
- ctx.set_source_rgba(self.r, self.g, self.b, self.alpha)
+ return coll
-black = Color(0, 0, 0)
-white = Color(1, 1, 1)
-red = Color(1, 0, 0)
-green = Color(0, 1, 0)
-blue = Color(0, 0, 1)
-# TODO: possibly add dashed, etc.
-class LineStyle(object):
- """Object for describing a line style. Can be used as a parameter in the
- class `Line`.
-
- Right now, the LineStyle object only allows to specify the line cap (i.e.
- the shape of the end of the line). In the future might include dashing,
- etc.
+def lines3d(axes, x0, x1, y0, y1, z0, z1,
+ colors='k', linestyles='solid', cmap=None, norm=None, **kwargs):
+ """Add a collection of 3D line segments to an Axes3D instance.
Parameters
----------
- lcap : { 'butt', 'round', 'square'}, optional
- Specifies the shape of the end of the line:
-
- 'butt'
- End of the line is rectangular and ends exactly at the end point.
- 'round'
- End of the line is rounded, as if a half-circle is drawn around the
- end point.
- 'square'
- End of the line is rectangular, but protrudes beyond the end point,
- as if a square was drawn centered at the end point.
-
- Defaults to 'butt'.
+ axes : matplotlib.axes.Axes instance
+ Axes to which the lines have to be added.
+ x0 : array_like
+ Starting x-coordinates of each line segment
+ x1 : array_like
+ Ending x-coordinates of each line segment
+ y0 : array_like
+ Starting y-coordinates of each line segment
+ y1 : array_like
+ Ending y-coordinates of each line segment
+ z0 : array_like
+ Starting z-coordinates of each line segment
+ z1 : array_like
+ Ending z-coordinates of each line segment
+ colors : color definition, optional
+ Either a single object that is a proper matplotlib color definition
+ or a sequence of such objects of appropriate length. Defaults to all
+ segments black.
+ linestyles :linestyle definition, optional
+ Either a single object that is a proper matplotlib line style
+ definition or a sequence of such objects of appropriate length.
+ Defaults to all segments solid.
+ cmap : `matplotlib` color map specification or None
+ Color map to be used if colors are specified as floats.
+ norm : `matplotlib` color norm
+ Norm to be used if colors are specified as floats.
+ **kwargs : dict
+ keyword arguments to pass to `matplotlib.collections.LineCollection`.
+
+ Returns
+ -------
+ `mpl_toolkits.mplot3d.art3d.Line3DCollection` instance containing all the
+ segments that were added.
"""
- def __init__(self, lcap="butt"):
- if lcap == "butt":
- self.lcap = cairo.LINE_CAP_BUTT
- elif lcap == "round":
- self.lcap = cairo.LINE_CAP_ROUND
- elif lcap == "square":
- self.lcap = cairo.LINE_CAP_SQUARE
- else:
- raise ValueError("Unknown line cap style "+lcap)
+ had_data = axes.has_data()
+ coords = (y0, y1, x0, x1, z0, z1)
- def _set_props_cairo(self, ctx, reflen):
- ctx.set_line_cap(self.lcap)
+ if not all(len(coord) == len(y0) for coord in coords):
+ raise ValueError('Incompatible lengths of coordinate arrays.')
-class Line(object):
- """Draws a straight line between the two sites connected by a hopping.
+ if len(x0) == 0:
+ coll = mplot3d.art3d.Line3DCollection([], linestyles=linestyles)
+ axes.add_collection(coll)
+ return coll
- Standard object that can be used to specify how to draw
- a line representing a hopping in `plot`.
+ segments = [(i[: 3], i[3:]) for
+ i in itertools.izip(x0, y0, z0, x1, y1, z1)]
+ coll = mplot3d.art3d.Line3DCollection(segments, linestyles=linestyles)
+ set_edge_colors(colors, coll, cmap, norm)
+ coll.update(kwargs)
+ axes.add_collection(coll)
- Parameters
- ----------
- lw : float
- line width relative to the reference length (see `plot`)
- lcol : object realizing the "color functionality" (see `plot`)
- line color
- lsty : a LineStyle object
- line style
- """
- def __init__(self, lw, lcol=black, lsty=LineStyle()):
- self.lw = lw
- self.lcol = lcol
- self.lsty = lsty
-
- def _draw_cairo(self, ctx, pos1, pos2, reflen, fading=None):
- ctx.new_path()
- if self.lw > 0 and self.lcol is not None and self.lsty is not None:
- ctx.set_line_width(self.lw * reflen)
- self.lcol._set_color_cairo(ctx, fading=fading)
- self.lsty._set_props_cairo(ctx, reflen)
- ctx.move_to(pos1[0], pos1[1])
- ctx.line_to(pos2[0], pos2[1])
- ctx.stroke()
-
-class Circle(object):
- """Draw circle with (relative) radius r centered at a site.
-
- Standard symbol object that can be used with `plot`.
- Sizes are always given in terms of the reference length
- of `plot`.
+ min_max = lambda a, b: np.array(min(a.min(), b.min()),
+ max(a.max(), b.max()))
+ x, y, z = min_max(x0, x1), min_max(y0, y1), min_max(z0, z1)
- Parameters
- ----------
- r : float
- Radius of the circle
- fcol : color_like object or None, optional
- Fill color. If None, the circle is not filled. Defaults to black.
- lw : float, optional
- Line width of the outline. If 0, no outline is drawn.
- Defaults to 0.1.
- lcol : color_like object or None, optional
- Color of the outline. If None, no outline is drawn. Defaults to None.
- lsty : `LineStyle` object
- Line style of the outline. Defaults to LineStyle().
- """
- def __init__(self, r, fcol=black, lw=0.1, lcol=None, lsty=LineStyle()):
- self.r = r
- self.fcol = fcol
- self.lw = lw
- self.lcol= lcol
- self.lsty = lsty
-
- def _draw_cairo(self, ctx, pos, reflen, fading=None):
- ctx.new_path()
-
- if self.fcol is not None:
- self.fcol._set_color_cairo(ctx, fading=fading)
- ctx.arc(pos[0], pos[1], self.r * reflen, 0, 2*pi)
- ctx.fill()
-
- if self.lw > 0 and self.lcol is not None and self.lsty is not None:
- ctx.set_line_width(self.lw * reflen)
- self.lcol._set_color_cairo(ctx, fading=fading)
- self.lsty._set_props_cairo(ctx, reflen)
- ctx.arc(pos[0], pos[1], self.r * reflen, 0, 2*pi)
- ctx.stroke()
-
-class Polygon(object):
- """Draw a regular n-sided polygon centered at a site.
-
- Standard symbol object that can be used with `plot`.
- Sizes are always given in terms of the reference length
- of `plot`.
-
- The size of the polygon can be specifed in one of two ways:
- - either by specifying the side length `a`
- - or by demanding that the area of the polygon is equal to a circle
- with radius `size`
+ axes.auto_scale_xyz(x, y, z, had_data)
- Parameters
- ----------
- n : int
- Number of sides (i.e. `n=3` is a triangle, `n=4` a square, etc.)
- a, size : float, exactly one must be given
- The size of the polygon, either specified by the side length `a`
- or the radius `size` of a circle of equal area.
- angle : float, optional
- Rotate the polygon counter-clockwise by `angle` (specified
- in radians. Defaults to 0.
- fcol : color_like object or None, optional
- Fill color. If None, the polygon is not filled. Defaults to black.
- lw : float, optional
- Line width of the outline. If 0, no outline is drawn.
- Defaults to 0.1.
- lcol : color_like object or None, optional
- Color of the outline. If None, no outline is drawn. Defaults to None.
- lsty : `LineStyle` object
- Line style of the outline. Defaults to LineStyle().
- """
- def __init__(self, n, a=None, size=None,
- angle=0, fcol=black, lw=0.1, lcol=None, lsty=LineStyle()):
- if ((a is None and size is None) or
- (a is not None and size is not None)):
- raise ValueError("Either sidelength or equivalent circle radius "
- "must be specified")
-
- self.n = n
- if a is None:
- # make are of triangle equal to circle of radius size
- a = sqrt(4 * tan(pi / n) / n * pi) * size
- # note: self.rc is the radius of the circumscribed circle
- self.rc = a / (2 * sin(pi / n))
- self.angle = angle
- self.fcol = fcol
- self.lw = lw
- self.lcol = lcol
- self.lsty = lsty
-
- def _draw_cairo_poly(self, ctx, pos, reflen):
- ctx.move_to(pos[0] + sin(self.angle) * self.rc * reflen,
- pos[1] + cos(self.angle) * self.rc * reflen)
- for i in xrange(1, self.n):
- phi = i * 2 * pi / self.n
- ctx.line_to(pos[0] + sin(self.angle + phi) * self.rc * reflen,
- pos[1] + cos(self.angle + phi) * self.rc * reflen)
- ctx.close_path()
-
- def _draw_cairo(self, ctx, pos, reflen, fading=None):
- ctx.new_path()
-
- if self.fcol is not None:
- self.fcol._set_color_cairo(ctx, fading=fading)
- self._draw_cairo_poly(ctx, pos, reflen)
- ctx.fill()
-
- if self.lw > 0 and self.lcol is not None and self.lsty is not None:
- ctx.set_line_width(self.lw * reflen)
- self.lcol._set_color_cairo(ctx, fading=fading)
- self.lsty._set_props_cairo(ctx, reflen)
- self._draw_cairo_poly(ctx, pos, reflen)
- ctx.stroke()
-
-
-def iterate_lead_sites_builder(syst, lead_copies):
- for lead in syst.leads:
- if not isinstance(lead, builder.BuilderLead):
- continue
- sym = lead.builder.symmetry
- shift = sym.which(lead.interface[0]) + 1
-
- for i in xrange(lead_copies):
- for site in lead.builder.sites():
- yield sym.act(shift + i, site), i
-
-
-def iterate_lead_hoppings_builder(syst, lead_copies):
- for lead in syst.leads:
- if not isinstance(lead, builder.BuilderLead):
- continue
- sym = lead.builder.symmetry
- shift = sym.which(lead.interface[0]) + 1
-
- for i in xrange(lead_copies):
- for site1, site2 in lead.builder.hoppings():
- shift1 = sym.which(site1)[0]
- shift2 = sym.which(site2)[0]
- if shift1 >= shift2:
- yield (sym.act(shift + i, site1),
- sym.act(shift + i, site2),
- i + shift1, i + shift2)
- else:
- # Note: this makes sure that hoppings beyond the unit
- # cell are always ordered such that they are into
- # the previous slice
- yield (sym.act(shift + i - 1, site1),
- sym.act(shift + i - 1, site2),
- i - 1 + shift1, i - 1 + shift2)
+ return coll
-def iterate_scattreg_sites_builder(syst):
- for site in syst.sites():
- yield site
+def output_fig(fig, output_mode='auto', file=None, savefile_opts=None,
+ show=True):
+ """Output a matplotlib figure using a given output mode.
+ Parameters
+ ----------
+ fig : matplotlib.figure.Figure instance
+ The figure to be output.
+ output_mode : string
+ The output mode to be used. Can be one of the following:
+ 'pyplot' : attach the figure to pyplot, with the same behavior as if
+ pyplot.plot was called to create this figure.
+ 'ipython' : attach a `FigureCanvasAgg` to the figure and return it.
+ 'return' : return the figure.
+ 'file' : same as 'ipython', but also save the figure into a file.
+ 'auto' : if fname is given, save to a file, else if pyplot
+ is imported, attach to pyplot, otherwise just return. See also the
+ notes below.
+ file : string or a file object
+ The name of the target file or the target file itself
+ (opened for writing).
+ savefile_opts : (list, dict) or None
+ args and kwargs passed to `print_figure` of `matplotlib`
+ show : bool
+ Whether to call `matplotlib.pyplot.show()`. Only has an effect if the
+ output uses pyplot.
-def iterate_scattreg_hoppings_builder(syst):
- for hopping in syst.hoppings():
- yield hopping
+ Notes
+ -----
+ For IPython with inline plotting, automatic mode selects 'return', since
+ there is a better way to show a figure by just calling `display(figure)`.
+ The behavior of this function producing a file is different from that of
+ matplotlib in that the `dpi` attribute of the figure is used by defaul
+ instead of the matplotlib config setting.
+ """
+ if not _mpl_enabled:
+ raise RuntimeError('matplotlib is not installed.')
+ if output_mode == 'auto':
+ if file is not None:
+ output_mode = 'file'
+ else:
+ try:
+ if matplotlib.pyplot.get_backend() != \
+ 'module://IPython.zmq.pylab.backend_inline':
+ output_mode = 'pyplot'
+ else:
+ output_mode = 'return'
+ except AttributeError:
+ output_mode = 'pyplot'
+ if output_mode == 'pyplot':
+ try:
+ fake_fig = matplotlib.pyplot.figure()
+ except AttributeError:
+ msg = 'matplotlib.pyplot is unavailable. Execute `import ' \
+ 'matplotlib.pyplot` or use a different output mode.'
+ raise RuntimeError(msg)
+ fake_fig.canvas.figure = fig
+ fig.canvas = fake_fig.canvas
+ for ax in fig.axes:
+ try:
+ ax.mouse_init() # Make 3D interface interactive.
+ except AttributeError:
+ pass
+ if show:
+ matplotlib.pyplot.show()
+ return fig
+ elif output_mode == 'return':
+ canvas = FigureCanvasAgg(fig)
+ fig.canvas = canvas
+ return fig
+ elif output_mode == 'file':
+ canvas = FigureCanvasAgg(fig)
+ if savefile_opts is None:
+ savefile_opts = ([], {})
+ if 'dpi' not in savefile_opts[1]:
+ savefile_opts[1]['dpi'] = fig.dpi
+ canvas.print_figure(file, *savefile_opts[0],
+ **savefile_opts[1])
+ return fig
+ else:
+ assert False, 'Unknown output_mode'
-def empty_generator(*args, **kwds):
- return
- yield
+
+# Extracting necessary data from the system.
+def sys_leads_sites(sys, n_lead_copies=2):
+ """Return all the sites of the system and of the leads as a list.
-def iterate_scattreg_sites_llsys(syst):
- return xrange(syst.graph.num_nodes)
+ Parameters
+ ----------
+ sys : kwant.builder.Builder or kwant.system.System instance
+ The system, sites of which should be returned.
+ n_lead_copies : integer
+ The number of times lead sites from each lead should be returned.
+ This is useful for showing several unit cells of the lead next to the
+ system.
+ Returns
+ -------
+ sites : list of (site, lead_number, copy_number) tuples
+ A site is a `builder.Site` instance if the system is not finalized,
+ and an integer otherwise. For system sites `lead_number` is `None` and
+ `copy_number` is `0`, for leads both are integers.
-def iterate_scattreg_hoppings_llsys(syst):
- for i in xrange(syst.graph.num_nodes):
- for j in syst.graph.out_neighbors(i):
- # Only yield half of the hoppings (as builder does)
- if i < j:
- yield i, j
+ Notes
+ -----
+ Leads are only supported if they are of the same type as the original
+ system, i.e. sites of `builder.BuilderLead` leads are returned with an
+ unfinalized system, and sites of `system.InfiniteSystem` leads are
+ returned with a finalized system.
+ """
+ if isinstance(sys, builder.Builder):
+ sites = [(site, None, 0) for site in sys.sites()]
+ for leadnr, lead in enumerate(sys.leads):
+ if hasattr(lead, 'builder'):
+ sites.extend(((site, leadnr, i) for site in
+ lead.builder.sites() for i in
+ xrange(n_lead_copies)))
+ elif isinstance(sys, system.FiniteSystem):
+ sites = [(i, None, 0) for i in xrange(sys.graph.num_nodes)]
+ for leadnr, lead in enumerate(sys.leads):
+ # We will only plot leads with a graph and with a symmetry.
+ if hasattr(lead, 'graph') and hasattr(lead, 'symmetry'):
+ sites.extend(((site, leadnr, i) for site in
+ xrange(lead.slice_size) for i in
+ xrange(n_lead_copies)))
+ else:
+ raise TypeError('Unrecognized system type.')
+ return sites
-def extent(pos, sites):
- """Figure out the extent of the system."""
- minx = miny = inf = float('inf')
- maxx = maxy = float('-inf')
- for site in sites:
- point = pos(site)
- try:
- x, y = point
- except:
- raise ValueError(
- "Position must be 2d. Consider using the `pos` argument.")
- minx = min(x, minx)
- maxx = max(x, maxx)
- miny = min(y, miny)
- maxy = max(y, maxy)
- if minx == inf:
- warnings.warn("Plotting empty system");
- return 0, 1, 0, 1
- return minx, maxx, miny, maxy
-
-
-def typical_distance(pos, hoppings, sites):
- min_sq_dist = inf = float('inf')
- for site1, site2 in hoppings:
- tmp = subtract(pos(site1), pos(site2))
- sq_dist = dot(tmp, tmp)
- if 0 < sq_dist < min_sq_dist:
- min_sq_dist = sq_dist
-
- # If there were no hoppings, then we can only find the distance by checking
- # the distances between all pairs sites (potentially slow). To speed this
- # only look at the distances between 10 chosen sites and all the remaining
- # sites. This simple heuristics works well in practice and is fast enough.
- if min_sq_dist == inf:
- first = True
- positions = list(pos(site) for site in sites)
-
- for site1 in positions[:: max(len(positions) // 10, 1)]:
- for site2 in positions:
- tmp = subtract(site1, site2)
- sq_dist = dot(tmp, tmp)
- if 0 < sq_dist < min_sq_dist:
- min_sq_dist = sq_dist
-
- # If min_sq_dist ist still 0, all sites sit at the same spot In this case I
- # can just use any value for dist (rangex and rangey will also be 0 then)
- return sqrt(min_sq_dist) if min_sq_dist != inf else 1
-
-
-def default_pos(syst):
- if isinstance(syst, builder.Builder):
- return lambda site: site.pos
- elif isinstance(syst, builder.FiniteSystem):
- return lambda i: syst.site(i).pos
- else:
- raise ValueError("`pos` argument needed when plotting"
- " systems which are not (finalized) builders")
-
-
-def plot(syst, filename=defaultname, fmt=None, a=None,
- width=600, height=None, border=0.1, bcol=white, pos=None,
- symbols=Circle(r=0.3), lines=Line(lw=0.1),
- lead_symbols=-1, lead_lines=-1,
- lead_fading=[0.6, 0.85]):
- """Plot two-dimensional systems (or two-dimensional representations
- of a system).
-
- `plot` can be used to plot both unfinalized kwant.builder.Builder
- instances, and low level systems (i.e. instances of
- kwant.system.FiniteSystem), including finalized builders.
-
- This function behaves differently for builders and low-level systems:
- builders are plotted including those of their leads which are builders
- themselves. For the leads, several copies of the lead unit cell are
- plotted (per default 2), and they are gradually faded towards the
- background color (at least in the default behavior). For low-level systems
- the leads are ignored as there is no general way to recover the necessary
- information about leads for low level systems.
-
- When arguments to this function are functions themselves, "sites" will be
- passed to them as arguments. The meaning of "site" depends on whether the
- system to be plotted is a builder or a low level system. For builders, a
- site is a kwant.builder.Site object. For low level systems, a site is an
- integer -- the site number.
-
- The output of `plot` is highly modifyable, as it does not perform any
- drawing itself, but instead lets objects passed by the user (or as default
- parameters) do the actual drawing work. `plot` itself does figure out the
- range of positions occupied by the sites, as well as the smallest distance
- between two sites which then serves as a reference length, unless the user
- specifies explicitely a reference length. This reference length is then
- used so that the sizes of symbols or lines are always given relative to
- that reference length. This is particularly advantageous for regular
- lattices, as it makes it easy to specify the area covered by symbols, etc.
-
- The objects that determine `plot`'s behavior are symbol_like (symbols
- representing sites), line_like (lines representing hoppings) and color_like
- (representing colors). The notes below explain in detail how to implement
- custom classes. In most cases it is enough to use the predefined standard
- objects:
-
- - for symbol_like: `Circle` and `Polygon`
- - for line_like: `Line`
- - for color_like: `Color`.
+def sys_leads_pos(sys, site_lead_nr):
+ """Return an array of positions of sites in a system.
Parameters
----------
- syst : (un)finalized system
- System to plot. Either an unfinalized Builder
- (instance of `kwant.builder.Builder`)
- or a finalized builder (instance of
- `kwant.builder.FiniteSystem`).
- filename : string or None, optional
- Name of the file the plot should be written to. The format
- of the file can be determined from the suffix (see `fmt`).
- If None, the plot is output on the screen [provided that the
- Python Image Library (PIL) is installed]. Default is
- None if the PIL is installed, and "plot.pdf" otherwise.
- fmt : {"pdf", "ps", "eps", "svg", "png", "jpg", None}, optional
- Format of the output file, if `filename` is not None. If
- `fmt` is None, the format is determined from the suffix of the
- `filename`. Defaults to None.
- a : float, optional
- Reference length. If None, the reference length is determined
- as the smallest nonzero distance between sites. Defaults to None.
- width, height : float or None, optional
- Width and height of the output picture. In units of
- "pt" for the vector graphics formats (pdf, ps, eps, svg)
- and in pixels for the bitmap formats (png, jpg, and output to screen).
- For the bitmap formats, `width` and `height` are rounded to the nearest
- integer. One of `width` and `height` may be None (but not both
- simultaneously). In this case, the unspecified size is chosen to
- fit with the aspect ratio of the plot. If both are specified, the plot
- is centered on the canvas (possibly with increasing the blank borders).
- `width` defaults to 600, and `height` to None.
- border : float, optional
- Size of the blank border around the plot, relative to the
- total size. Defaults to 0.1.
- bcol : color_like, optional
- Background color. Defaults to white.
-
- (If the plot is saved in a vector graphics format, `white`
- actually corresponds to no background. This is a bit hacky
- maybe [fading to bcol e.g. still makes a white symbol, not a
- transparant symbol], but then again there is no reason for
- having a white box behind everything)
- pos : function or None, optional
- When passed a site should return its (2D) position as a sequence of
- length 2. If None, the real space position of the site is used if the
- system to be plotted is a (finalized) builder. For other low level
- systems it is required to specify this argument and an error will be
- reported if it is missing. Defaults to None.
- symbols : {symbol_like, function, dict, None}, optional
- Object responsible for drawing the symbols correspodning to sites.
- Either must be a single symbol_like object (the same symbol is drawn
- for every site), a function that returns a symbol_like object when
- passed a site, or None (in which case no symbols are drawn). Instead of
- a symbol_like object the function may also return None corresponding to
- no symbol.
-
- If the system is a builder, `symbols` may also be a dictionary with
- site groups as keys and symbol_like as values. This allows to specify
- different symbols for different site groups.
-
- Defaults to ``Circle(r=0.3)``.
-
- The standard symbols available are `Circle` and `Polygon`.
- lines : {line_like, function, dict, None}, optional
- Object responsible for drawing the lines representing the hoppings
- between sites. Either a single line_like object (the same type of line
- is drawn for all hoppings), a function that returns a line_like object
- when passed two sites, or None (in which case no hoppings are
- drawn). Instead of a line_like object the function may also return None
- corresponding to no line. Defaults to ``Line(lw=0.1)``.
-
- If the system is a builder, `lines` may also be a dictionary with
- tuples of two site groups as keys and line_like objects as values.
- This allows to specify different line styles for different hoppings.
- Note that if the hopping (a, b) is specified, (b, a) needs not be
- included in the dictionary.
-
- The standard line available is `Line`.
- lead_symbols : {symbol_like, function, dict, -1, None}, optional
- Symbols to be drawn for the sites in the leads. The special
- value -1 indicates that `symbols` (which is used for system sites)
- should be used also for the leads. The other possible values are
- as for the system `symbols`.
- Defaults to -1.
- lead_lines : {line_like, function, dict, -1, None}, optional
- Lines to be drawn for the hoppings in the leads. The special
- value -1 indicates that `lines` (which is used for system hoppings)
- should be used also for the leads. The other possible values are
- as for the system `lines`.
- Defaults to -1.
- lead_fading : list, optional
- The number of entries in the list determines the number of
- lead unit cells that are plotted. The unit cell `i` is then
- faded by the ratio ``lead_fading[i]`` towards the
- background color `bcol`. Here ``lead_fading[i]==0`` implies no fading
- (i.e. the original symbols and lines),
- whereas ``lead_fading[i]==1`` corresponds to the background color.
+ sys : `kwant.builder.Builder` or `kwant.system.System` instance
+ The system, coordinates of sites of which should be returned.
+ sites : list of `(site, leadnr, copynr)` tuples
+ Output of `sys_leads_sites` applied to the system.
+
+ Returns
+ -------
+ coords : numpy.ndarray of floats
+ Array of coordinates of the sites.
Notes
-----
-
- `plot` knows three different legitimate classes representing
- symbols (symbol_like), lines (line_like), and colors (color_like).
- In order to serve as a legitimate object for these,
- a class has to implement certain methods. In particular these
- are
-
- - symbol_like: objects representing symbols for sites::
-
- _draw_cairo(ctx, pos, reflen[, fading])
-
- which draws the symbol onto the cairo context `ctx`
- at the position `pos` (passed as a sequence of length 2).
- `reflen` is the reference length, allowing the symbol to use
- relative sizes. (Note though that `pos` is in **absolute** cairo
- coordinates).
-
- If the symbol should also be used to draw leads, `_draw_cairo`
- should also take the optional parameter `fading` wich is a tuple
- `(fadecol, percent)` where `fadecol` is the color towards which
- the symbol should be faded, and `percent` is a number between 0
- and 1 indicating the amount of fading, with `percent=0` no
- fading, and `percent=1` fully faded to `fadecol`. Note that
- while "fading" usually will imply color fading, this is not
- required by plot. Anything conceivable is legitimate.
-
- The module :mod:`plot` provides two standard symbol classes:
- `Circle` and `Polygon`.
-
- - line_like: objects representing lines for hoppings::
-
- _draw_cairo(ctx, pos1, pos2, reflen[, fading])
-
- which draws the something (typically a line of some sort) onto
- the cairo context `ctx` connecting the position `pos1` and
- `pos2` (passed as sequences of length 2). `reflen` is the
- reference length, allowing the line to use relative sizes. (Note
- though that `pos1` and `pos2` are in **absolute** cairo
- coordinates).
-
- If the line should also be used to draw leads, `_draw_cairo`
- should also take the optional parameter `fading` wich is a tuple
- `(fadecol, percent)` where `fadecol` is the color towards which
- the symbol should be faded, and `percent` is a number between 0
- and 1 indicating the amount of fading, with `percent=0` no
- fading, and `percent=1` fully faded to `fadecol`. Note that
- while "fading" usually will imply color fading, this is not
- required by plot. Anything conceivable is legitimate.
-
- The module :mod:`plot` provides one standard line class: `Line`.
-
- - color_like: for objects representing colors::
-
- def _set_color_cairo(ctx[, fading]):
-
- which sets the current color of the cairo context `ctx`.
-
- If the color is passed to an object that requires fading in
- order to be applicable for the representation of leads,
- it must also take the optional parameter 'fading' wich is a tuple
- `(fadecol, percent)` where `fadecol` is the color towards which
- the symbol should be faded, and `percent` is a number between 0
- and 1 indicating the amount of fading, with `percent=0` no
- fading, and `percent=1` fully faded to `fadecol`. Note that
- while "fading" usually will imply color fading, this is not
- required by plot. Anything conceivable is legitimate.
-
- The module :mod:`plot` provides one standard color class:
- `Color`. In addition, a few common colors are predefined
- as instances of `Color`:`black`, `white`, `red`, `green`,
- and `blue`.
+ This function uses `site.pos` property to get the position of a builder
+ site and `sys.pos(sitenr)` for finalized systems. This function requires
+ that all the positions of all the sites have the same dimensionality.
"""
+ is_builder = isinstance(sys, builder.Builder)
+ n_lead_copies = site_lead_nr[-1][2] + 1
+ if is_builder:
+ pos = np.array([i[0].pos for i in site_lead_nr])
+ else:
+ sys_from_lead = lambda lead: (sys if (lead is None)
+ else sys.leads[lead])
+ pos = np.array([sys_from_lead(i[1]).pos(i[0]) for i in site_lead_nr])
+ if pos.dtype == object: # Happens if not all the pos are same length.
+ raise ValueError("pos attribute of the sites does not have consistent"
+ " values.")
+ dim = pos.shape[1]
+
+ def get_vec_domain(lead_nr):
+ if lead_nr is None:
+ return np.zeros((dim,)), 0
+ if is_builder:
+ sym = sys.leads[lead_nr].builder.symmetry
+ site = sys.leads[lead_nr].interface[0]
+ else:
+ sym = sys.leads[lead_nr].symmetry
+ site = sys.site(sys.lead_interfaces[lead_nr][0])
+ dom = sym.which(site)[0] + 1
+ # TODO (Anton): vec = sym.periods[0] not supported by ta.ndarray
+ # Remove conversion to np.ndarray when not necessary anymore.
+ vec = np.array(sym.periods)[0]
+ return vec, dom
+ vecs_doms = dict((i, get_vec_domain(i)) for i in xrange(len(sys.leads)))
+ vecs_doms[None] = np.zeros((dim,)), 0
+ for k, v in vecs_doms.iteritems():
+ vecs_doms[k] = [v[0] * i for i in xrange(v[1], v[1] + n_lead_copies)]
+ pos += [vecs_doms[i[1]][i[2]] for i in site_lead_nr]
+ return pos
+
+
+def sys_leads_hoppings(sys, n_lead_copies=2):
+ """Return all the hoppings of the system and of the leads as an iterator.
- def iterate_all_sites(syst, lead_copies=0):
- for site in iterate_scattreg_sites(syst):
- yield site
-
- for site, ucindx in iterate_lead_sites(syst, lead_copies):
- yield site
+ Parameters
+ ----------
+ sys : kwant.builder.Builder or kwant.system.System instance
+ The system, sites of which should be returned.
+ n_lead_copies : integer
+ The number of times lead sites from each lead should be returned.
+ This is useful for showing several unit cells of the lead next to the
+ system.
- def iterate_all_hoppings(syst, lead_copies=0):
- for site1, site2 in iterate_scattreg_hoppings(syst):
- yield site1, site2
+ Returns
+ -------
+ hoppings : list of (hopping, lead_number, copy_number) tuples
+ A site is a `builder.Site` instance if the system is not finalized,
+ and an integer otherwise. For system sites `lead_number` is `None` and
+ `copy_number` is `0`, for leads both are integers.
- for site1, site2, i1, i2 in iterate_lead_hoppings(syst, lead_copies):
- yield site1, site2
+ Notes
+ -----
+ Leads are only supported if they are of the same type as the original
+ system, i.e. hoppings of `builder.BuilderLead` leads are returned with an
+ unfinalized system, and hoppings of `system.InfiniteSystem` leads are
+ returned with a finalized system.
+ """
+ hoppings = []
+ if isinstance(sys, builder.Builder):
+ hoppings.extend(((hop, None, 0) for hop in sys.hoppings()))
- is_builder = isinstance(syst, builder.Builder)
- is_lowlevel = isinstance(syst, system.FiniteSystem)
- if is_builder:
- iterate_scattreg_sites = iterate_scattreg_sites_builder
- iterate_scattreg_hoppings = iterate_scattreg_hoppings_builder
- iterate_lead_sites = iterate_lead_sites_builder
- iterate_lead_hoppings = iterate_lead_hoppings_builder
- elif is_lowlevel:
- iterate_scattreg_sites = iterate_scattreg_sites_llsys
- iterate_scattreg_hoppings = iterate_scattreg_hoppings_llsys
- # We do not plot leads for low level systems, as there is no general
- # way to do that.
- iterate_lead_sites = empty_generator
- iterate_lead_hoppings = empty_generator
+ def lead_hoppings(lead):
+ sym = lead.symmetry
+ for site2, site1 in lead.hoppings():
+ shift1 = sym.which(site1)[0]
+ shift2 = sym.which(site2)[0]
+ # We need to make sure that the hopping is between a site in a
+ # fundamental domain and a site with a negative domain. The
+ # direction of the hopping is chosen arbitrarily
+ # NOTE(Anton): This may need to be revisited with the future
+ # builder format changes.
+ shift = max(shift1, shift2)
+ yield sym.act([-shift], site2), sym.act([-shift], site1)
+
+ for leadnr, lead in enumerate(sys.leads):
+ if hasattr(lead, 'builder'):
+ hoppings.extend(((hop, leadnr, i) for hop in
+ lead_hoppings(lead.builder) for i in
+ xrange(n_lead_copies)))
+ elif isinstance(sys, system.System):
+ def ll_hoppings(sys):
+ for i in xrange(sys.graph.num_nodes):
+ for j in sys.graph.out_neighbors(i):
+ if i < j:
+ yield i, j
+ hoppings.extend(((hop, None, 0) for hop in ll_hoppings(sys)))
+ for leadnr, lead in enumerate(sys.leads):
+ # We will only plot leads with a graph and with a symmetry.
+ if hasattr(lead, 'graph') and hasattr(lead, 'symmetry'):
+ hoppings.extend(((hop, leadnr, i) for hop in
+ ll_hoppings(lead) for i in
+ xrange(n_lead_copies)))
else:
- raise ValueError("Plotting not suported for given system")
-
- if width is None and height is None:
- raise ValueError("One of width and height must be not None")
+ raise TypeError('Unrecognized system type.')
+ return hoppings
- if pos is None:
- pos = default_pos(syst)
- if fmt is None and filename is not None:
- # Try to figure out the format from the filename
- fmt = filename.split(".")[-1].lower()
- elif fmt is not None and filename is None:
- raise ValueError("If fmt is specified, filename must be given, too")
+def sys_leads_hopping_pos(sys, hop_lead_nr):
+ """Return arrays of coordinates of all hoppings in a system.
- if fmt not in [None, "pdf", "ps", "eps", "svg", "png", "jpg"]:
- raise ValueError("Unknwon format " + fmt)
+ Parameters
+ ----------
+ sys : `kwant.builder.Builder` or `kwant.system.System` instance
+ The system, coordinates of sites of which should be returned.
+ hoppings : list of `(hopping, leadnr, copynr)` tuples
+ Output of `sys_leads_hoppings` applied to the system.
- # Those two need the PIL
- if fmt in [None, "jpg"] and not has_pil:
- raise ValueError("The requested functionality requires the "
- "Python Image Library (PIL)")
+ Returns
+ -------
+ coords : (end_site, start_site): tuple of numpy arrays of floats
+ Array of coordinates of the hoppings. The first half of coordinates
+ in each array entry are those of the first site in the hopping, the
+ last half are those of the second site.
- # Symbols and lines may be constant or functions. Wrap them as functions.
- if hasattr(symbols, "__call__"):
- fsymbols = symbols
- elif is_builder and hasattr(symbols, "__getitem__"):
- fsymbols = lambda x : symbols[x.group]
+ Notes
+ -----
+ This function uses `site.pos` property to get the position of a builder
+ site and `sys.pos(sitenr)` for finalized systems. This function requires
+ that all the positions of all the sites have the same dimensionality.
+ """
+ is_builder = isinstance(sys, builder.Builder)
+ if len(hop_lead_nr) == 0:
+ return np.empty((0, 3)), np.empty((0, 3))
+ n_lead_copies = hop_lead_nr[-1][2] + 1
+ if is_builder:
+ pos = np.array([np.r_[i[0][0].pos, i[0][1].pos] for i in hop_lead_nr])
else:
- fsymbols = lambda x : symbols
+ sys_from_lead = lambda lead: (sys if (lead is None)
+ else sys.leads[lead])
+ pos = [(sys_from_lead(i[1]).pos(i[0][0]),
+ sys_from_lead(i[1]).pos(i[0][1])) for i in hop_lead_nr]
+ pos = np.array([np.r_[i[0], i[1]] for i in pos])
+ if pos.dtype == object: # Happens if not all the pos are same length.
+ raise ValueError("pos attribute of the sites does not have consistent"
+ " values.")
+ dim = pos.shape[1]
+
+ def get_vec_domain(lead_nr):
+ if lead_nr is None:
+ return np.zeros((dim,)), 0
+ if is_builder:
+ sym = sys.leads[lead_nr].builder.symmetry
+ site = sys.leads[lead_nr].interface[0]
+ else:
+ sym = sys.leads[lead_nr].symmetry
+ site = sys.site(sys.lead_interfaces[lead_nr][0])
+ dom = sym.which(site)[0] + 1
+ # TODO (Anton): vec = sym.periods[0] not supported by ta.ndarray
+ # Remove conversion to np.ndarray when not necessary anymore.
+ vec = np.array(sym.periods)[0]
+ return np.r_[vec, vec], dom
+
+ vecs_doms = dict((i, get_vec_domain(i)) for i in xrange(len(sys.leads)))
+ vecs_doms[None] = np.zeros((dim,)), 0
+ for k, v in vecs_doms.iteritems():
+ vecs_doms[k] = [v[0] * i for i in xrange(v[1], v[1] + n_lead_copies)]
+ pos += [vecs_doms[i[1]][i[2]] for i in hop_lead_nr]
+ return np.copy(pos[:, : dim / 2]), np.copy(pos[:, dim / 2:])
+
+
+# Useful plot functions (to be extended).
+
+def plot(sys, n_lead_copies=2, site_color='b', hop_color='b', cmap='gray',
+ size=4, thickness=None, pos_transform=None, colorbar=True, file=None,
+ show=True, dpi=None, fig_size=None):
+ """Plot a system in 2 or 3 dimensions.
- if hasattr(lines, "__call__"):
- flines = lines
- elif is_builder and hasattr(lines, "__getitem__"):
- flines = lambda x, y : (lines[x.group, y.group] if (x.group, y.group)
- in lines else lines[y.group, x.group])
- else:
- flines = lambda x, y : lines
-
- if lead_symbols == -1:
- flsymbols = fsymbols
- elif hasattr(lead_symbols, "__call__"):
- flsymbols = lead_symbols
- elif is_builder and hasattr(lead_symbols, "__getitem__"):
- flsymbols = lambda x : lead_symbols[x.group]
- else:
- flsymbols = lambda x : lead_symbols
-
- if lead_lines == -1:
- fllines = flines
- elif hasattr(lead_lines, "__call__"):
- fllines = lead_lines
- elif is_builder and hasattr(lines, "__getitem__"):
- fllines = lambda x, y : (lead_lines[x.group, y.group]
- if (x.group, y.group) in lead_lines
- else lead_lines[y.group ,x.group])
- else:
- fllines = lambda x, y : lead_lines
+ Parameters
+ ----------
+ sys : kwant.builder.Builder or kwant.system.FiniteSystem
+ A system to be plotted.
+ n_lead_copies : int
+ Number of lead copies to be shown with the system.
+ site_color : `matplotlib` color description or a function
+ A color used for plotting a site in the system or a function returning
+ this color when given a site of the system (ignored for lead sites).
+ If a colormap is used, this function should return a single float.
+ hop_color : `matplotlib` color description or a function
+ Same as site_color, but for hoppings. A function is passed two sites
+ in this case.
+ cmap : `matplotlib` color map or a tuple of two color maps or `None`
+ The color map used for sites and optionally hoppings.
+ size : float or `None`
+ Site size in points. If `None`, `matplotlib` default is used.
+ thickness : float or `None`
+ Line thickness in points. If `None`, `matplotlib` default is used.
+ pos_transform : function or None
+ Transformation to be applied to the site position.
+ colorbar : bool
+ Whether to show a colorbar if colormap is used
+ file : string or file object or `None`
+ The output file. If `None`, output will be shown instead.
+ show : bool
+ Whether `matplotlib.pyplot.show()` is to be called, and the output is
+ to be shown immediately. Defaults to `True`.
+ dpi : float
+ Number of pixels per inch. If not set the `matplotlib` default is
+ used.
+ fig_size : tuple
+ Figure size `(width, height)` in inches. If not set, the default
+ `matplotlib` value is used.
- minx, maxx, miny, maxy = \
- extent(pos, iterate_all_sites(syst, len(lead_fading)))
+ Notes
+ -----
+ - The meaning of "site" depends on whether the system to be plotted is a
+ builder or a low level system. For builders, a site is a
+ kwant.builder.Site object. For low level systems, a site is an integer
+ -- the site number.
- # If the user gave no typical distance between sites, we need to figure it
- # out ourselves
- # (Note: it is enough to consider one copy of the lead unit cell for
- # figuring out distances, because of the translational symmetry)
- if a is None:
- a = typical_distance(pos, iterate_all_hoppings(syst, lead_copies=1),
- iterate_all_sites(syst, lead_copies=1))
- elif a <= 0:
- raise ValueError("The distance a must be >0")
-
- # Use the typical distance, if one of the ranges is 0
- # (e.g. in a one-dimensional system)
- rangex = (maxx - minx) / (1 - 2 * border)
- if rangex == 0:
- rangex = a / (1 - 2 * border)
- rangey = (maxy - miny) / (1 - 2 * border)
- if rangey == 0:
- rangey = a / (1 - 2 * border)
-
- # Compare with the desired dimensions of the plot
- if height is None:
- height = width * rangey / rangex
- elif width is None:
- width = height * rangex / rangey
+ - The dimensionality of the plot (2D vs 3D) is inferred from the coordinate
+ array. If there are more than three coordinates, only the first three
+ are used. If there is just one coordinate, the second one is padded with
+ zeros.
+
+ - The system is scaled to fit the smaller dimension of the figure, given
+ its aspect ratio.
+ """
+ # Generate data.
+ sites = sys_leads_sites(sys, n_lead_copies)
+ n_sys_sites = sum(i[1] is None for i in sites)
+ sites_pos = sys_leads_pos(sys, sites)
+ hops = sys_leads_hoppings(sys, n_lead_copies)
+ n_sys_hops = sum(i[1] is None for i in hops)
+ end_pos, start_pos = sys_leads_hopping_pos(sys, hops)
+ # Apply transformations to the data and generate the colors.
+ if pos_transform is not None:
+ sites_pos = np.apply_along_axis(pos_transform, 1, sites_pos)
+ end_pos = np.apply_along_axis(pos_transform, 1, end_pos)
+ start_pos = np.apply_along_axis(pos_transform, 1, start_pos)
+ if hasattr(site_color, '__call__'):
+ site_color = [site_color(i[0]) for i in sites if i[1] is None]
+ if hasattr(hop_color, '__call__'):
+ hop_color = [hop_color(*i[0]) for i in hops if i[1] is None]
+ # Choose plot type.
+ dim = 3 if (sites_pos.shape[1] == 3) else 2
+ ar = np.zeros((len(sites_pos), dim))
+ ar[:, : min(dim, sites_pos.shape[1])] = sites_pos
+ sites_pos = ar
+ end_pos.resize(len(end_pos), min(dim, end_pos.shape[1]))
+ start_pos.resize(len(start_pos), min(dim, start_pos.shape[1]))
+ fig = Figure()
+ if dpi is not None:
+ fig.set_dpi(dpi)
+ if fig_size is not None:
+ fig.set_figwidth(fig_size[0])
+ fig.set_figheight(fig_size[1])
+ if isinstance(cmap, tuple):
+ hop_cmap = cmap[1]
+ cmap = cmap[0]
else:
- # both width and height specified
- # check in which direction to expand the border
- if width/height > rangex / rangey:
- rangex = rangey * width / height
+ hop_cmap = None
+ if dim == 2:
+ ax = fig.add_subplot(111, aspect='equal')
+ ax.scatter(*sites_pos[: n_sys_sites].T, c=site_color, cmap=cmap,
+ s=size ** 2, zorder=2)
+ end, start = end_pos[: n_sys_hops], start_pos[: n_sys_hops]
+ lines(ax, end[:, 0], start[:, 0], end[:, 1], start[:, 1], hop_color,
+ linewidths=thickness, zorder=1, cmap=hop_cmap)
+ lead_site_colors = np.array([i[2] for i in
+ sites if i[1] is not None], dtype=float)
+ # Avoid the matplotlib autoscale bug (remove when fixed)
+ if len(sites_pos) > n_sys_sites:
+ ax.scatter(*sites_pos[n_sys_sites:].T, c=lead_site_colors,
+ cmap='gist_yarg_r', s=size ** 2, zorder=2,
+ norm=matplotlib.colors.Normalize(-1, n_lead_copies + 1))
else:
- rangey = rangex * height / width
-
- # Setup cairo
- if fmt == "pdf":
- surface = cairo.PDFSurface(filename, width, height)
- elif fmt == "ps":
- surface = cairo.PSSurface(filename, width, height)
- elif fmt == "eps":
- surface = cairo.PSSurface(filename, width, height)
- surface.set_eps(True)
- elif fmt == "svg":
- surface = cairo.SVGSurface(filename, width, height)
- elif fmt == "png" or fmt == "jpg" or fmt is None:
- surface = cairo.ImageSurface(cairo.FORMAT_ARGB32,
- int(round(width)), int(round(height)))
- ctx = cairo.Context(surface)
-
- # The default background in the image surface is black
- if fmt == "png" or fmt == "jpg" or fmt is None:
- bcol._set_color_cairo(ctx)
- ctx.rectangle(0, 0, int(round(width)), int(round(height)))
- ctx.fill()
- elif bcol is not white:
- # only draw a background rectangle if background color is not white
- bcol._set_color_cairo(ctx)
- ctx.rectangle(0, 0, width, height)
- ctx.fill()
-
- # Setup the coordinate transformation
-
- # Note: Cairo uses a coordinate system
- # ---> x positioned in the left upper corner
- # | of the screen.
- # v y
- #
- # Instead, we use a mathematical coordinate system.
-
- # TODO: figure out, if file sizes are smaller without transformation
- # i. e. if we do the transformation ourselves
- scrminx = width * 0.5 * (rangex - (maxx - minx)) / rangex
- scrminy = height * 0.5 * (rangey - (maxy - miny)) / rangey
-
- ctx.translate(scrminx, height - scrminy)
- ctx.scale(width/rangex, -height/rangey)
- ctx.translate(-minx, -miny)
-
- #### Draw the lines for the hoppings.
- for site1, site2 in iterate_scattreg_hoppings(syst):
- line = flines(site1, site2)
-
- if line is not None:
- line._draw_cairo(ctx, pos(site1), pos(site2), a)
-
- for site1, site2, ucindx1, ucindx2 in \
- iterate_lead_hoppings(syst, len(lead_fading)):
- if ucindx1 == ucindx2:
- line = fllines(site1, site2)
-
- if line is not None:
- line._draw_cairo(ctx, pos(site1), pos(site2), a,
- fading=(bcol, lead_fading[ucindx1]))
+ ax.add_collection(matplotlib.collections.PathCollection([]))
+ lead_hop_colors = np.array([i[2] for i in
+ hops if i[1] is not None], dtype=float)
+ end, start = end_pos[n_sys_hops:], start_pos[n_sys_hops:]
+ lines(ax, end[:, 0], start[:, 0], end[:, 1], start[:, 1],
+ lead_hop_colors, linewidths=thickness, cmap='gist_yarg_r',
+ norm=matplotlib.colors.Normalize(-1, n_lead_copies + 1),
+ zorder=1)
+ else:
+ warnings.filterwarnings('ignore', message=r'.*rotation.*')
+ ax = fig.add_subplot(111, projection='3d')
+ warnings.resetwarnings()
+ ax.scatter(*sites_pos[: n_sys_sites].T, c=site_color, cmap=cmap,
+ s=size ** 2)
+ end, start = end_pos[: n_sys_hops], start_pos[: n_sys_hops]
+ lines3d(ax, end[:, 0], start[:, 0], end[:, 1], start[:, 1],
+ end[:, 2], start[:, 2], hop_color, cmap=hop_cmap,
+ linewidths=thickness)
+ lead_site_colors = np.array([i[2] for i in
+ sites if i[1] is not None], dtype=float)
+ lead_site_colors = 1 / np.sqrt(1. + lead_site_colors)
+ # Avoid the matplotlib autoscale bug (remove when fixed)
+ if len(sites_pos) > n_sys_sites:
+ ax.scatter(*sites_pos[n_sys_sites:].T, c=lead_site_colors,
+ cmap='gist_yarg_r', s=size ** 2,
+ norm=matplotlib.colors.Normalize(-1, n_lead_copies + 1))
else:
- if ucindx1 > -1:
- line = fllines(site1, site2)
- if line is not None:
- line._draw_cairo(ctx, pos(site1),
- 0.5 * add(pos(site1), pos(site2)),
- a, fading=(bcol, lead_fading[ucindx1]))
- else:
- #one end of the line is in the system
- line = flines(site1, site2)
- if line is not None:
- line._draw_cairo(ctx, pos(site1),
- 0.5 * add(pos(site1), pos(site2)), a)
-
- if ucindx2 > -1:
- line = fllines(site2, site1)
- if line is not None:
- line._draw_cairo(ctx, pos(site2),
- 0.5 * add(pos(site1), pos(site2)),
- a, fading=(bcol, lead_fading[ucindx2]))
- else:
- # One end of the line is in the system
- line = flines(site2, site1)
- if line is not None:
- line._draw_cairo(ctx, pos(site2),
- 0.5 * add(pos(site1), pos(site2)), a)
-
- #### Draw the symbols for the sites.
- for site in iterate_scattreg_sites(syst):
- symbol = fsymbols(site)
-
- if symbol is not None:
- symbol._draw_cairo(ctx, pos(site), a)
-
- for site, ucindx in iterate_lead_sites(syst,
- lead_copies=len(lead_fading)):
- symbol = flsymbols(site)
-
- if symbol is not None:
- symbol._draw_cairo(ctx, pos(site), a,
- fading=(bcol, lead_fading[ucindx]))
-
-
- # Show or save the picture, if necessary (depends on format).
- if fmt == None:
- im = Image.frombuffer("RGBA",
- (surface.get_width(), surface.get_height()),
- surface.get_data(), "raw", "BGRA", 0, 1)
- im.show()
- elif fmt == "png":
- surface.write_to_png(filename)
- elif fmt == "jpg":
- im = Image.frombuffer("RGBA",
- (surface.get_width(), surface.get_height()),
- surface.get_data(), "raw", "BGRA", 0, 1)
- im.save(filename, "JPG")
-
-
-def interpolate(syst, function, a=None, pos=None,
- method='nearest', oversampling=3):
- """Interpolate a scalar function defined for the sites of a system.
+ ax.add_collection(mplot3d.art3d.Patch3DCollection([]))
+ lead_hop_colors = np.array([i[2] for i in
+ hops if i[1] is not None], dtype=float)
+ lead_hop_colors = 1 / np.sqrt(1. + lead_hop_colors)
+ end, start = end_pos[n_sys_hops:], start_pos[n_sys_hops:]
+ lines3d(ax, end[:, 0], start[:, 0], end[:, 1], start[:, 1],
+ end[:, 2], start[:, 2],
+ lead_hop_colors, linewidths=thickness, cmap='gist_yarg_r',
+ norm=matplotlib.colors.Normalize(-1, n_lead_copies + 1))
+ min_ = np.min(sites_pos, 0)
+ max_ = np.max(sites_pos, 0)
+ w = np.max(max_ - min_) / 2
+ m = (min_ + max_) / 2
+ ax.auto_scale_xyz(*[(i - w, i + w) for i in m], had_data=True)
+ if ax.collections[0].get_array() is not None and colorbar:
+ fig.colorbar(ax.collections[0])
+ if ax.collections[1].get_array() is not None and colorbar:
+ fig.colorbar(ax.collections[1])
+ return output_fig(fig, file=file, show=show)
+
+
+def mask_interpolate(coords, values, a=None, method='nearest', oversampling=3):
+ """Interpolate a scalar function in vicinity of given points.
+
+ Create a masked array corresponding to interpolated values of the function
+ at points lying not further than a certain distance from the original
+ data points provided.
Parameters
----------
- syst : kwant.system.FiniteSystem or kwant.builder.Builder
- The system for whose sites `function` is to be plotted.
- function : function or mapping
- Function which takes a site and returns a number, or a mapping whose
- keys are sites and whose values are numbers.
+ coords : np.ndarray
+ An array with site coordinates.
+ values : np.ndarray
+ An array with the values from which the interpolation should be built.
a : float, optional
- Reference length. If not given, it is determined as the smallest
- nonzero distance between sites.
- pos : function, optional
- When passed a site should return its (2D) position as a sequence of
- length 2. If None, the real space position of the site is used if the
- system to be plotted is a (finalized) builder. For other low level
- systems it is required to specify this argument and an error will be
- reported if it is missing. Defaults to None.
+ Reference length. If not given, it is determined as a typical
+ nearest neighbor distance.
method : string, optional
Passed to `scipy.interpolate.griddata`: "nearest" (default), "linear",
or "cubic"
@@ -887,11 +715,6 @@ def interpolate(syst, function, a=None, pos=None,
Notes
-----
- - The meaning of "site" depends on whether the system to be plotted is a
- builder or a low level system. For builders, a site is a
- kwant.builder.Site object. For low level systems, a site is an integer
- -- the site number.
-
- `min` and `max` are chosen such that when plotting a system on a square
lattice and `oversampling` is set to an odd integer, each site will lie
exactly at the center of a pixel of the output array.
@@ -900,90 +723,99 @@ def interpolate(syst, function, a=None, pos=None,
makes sense to set `oversampling` to ``1`` to minimize the size of the
output array.
"""
- if isinstance(syst, builder.Builder):
- iterate_scattreg_sites = iterate_scattreg_sites_builder
- iterate_scattreg_hoppings = iterate_scattreg_hoppings_builder
- elif isinstance(syst, system.FiniteSystem):
- iterate_scattreg_sites = iterate_scattreg_sites_llsys
- iterate_scattreg_hoppings = iterate_scattreg_hoppings_llsys
- else:
- raise ValueError("Plotting not suported for given system.")
+ # Build the bounding box.
+ cmin, cmax = coords.min(0), coords.max(0)
- if not hasattr(function, '__call__'):
- try:
- function = function.__getitem__
- except:
- raise TypeError("`function` must be either callable or a mapping.")
-
- if pos is None:
- pos = default_pos(syst)
+ tree = spatial.cKDTree(coords)
if a is None:
- a = typical_distance(pos, iterate_scattreg_hoppings(syst),
- iterate_scattreg_sites(syst))
+ points = coords[np.random.randint(len(coords), size=10)]
+ a = np.min(tree.query(points, 2)[0][:, 1])
elif a <= 0:
- raise ValueError("The distance a must be >0")
-
- points = []
- values = []
- for site in iterate_scattreg_sites(syst):
- point = pos(site)
- if point.shape != (2,):
- raise ValueError(
- 'Position must be 2d. Consider using the `pos` argument.')
- points.append(point)
- values.append(function(site))
- points = np.array(points)
- values = np.array(values)
-
- min = points.min(0)
- max = points.max(0)
- shape = (((max - min) / a + 1) * oversampling).round()
+ raise ValueError("The distance a must be strictly positive.")
+
+ shape = (((cmin - cmax) / a + 1) * oversampling).round()
delta = 0.5 * (oversampling - 1) * a / oversampling
- min -= delta
- max += delta
- grid_x, grid_y = np.ogrid[min[0]:max[0]:complex(0, shape[0]),
- min[1]:max[1]:complex(0, shape[1])]
- img = scipy.interpolate.griddata(points, values, (grid_x, grid_y),
- method)
+ cmin -= delta
+ cmax += delta
+ dims = tuple(slice(cmin[i], cmax[i], 1j * shape[i]) for i in
+ range(len(cmin)))
+ grid = tuple(np.ogrid[dims])
+ img = interpolate.griddata(coords, values, grid, method)
+ mask = np.mgrid[dims].reshape(len(cmin), -1).T
+ mask = tree.query(mask, eps=1.)[0] > 1.5 * a
+
+ return np.ma.masked_array(img, mask), cmin, cmax
- return img, min, max
-def show(syst, function, colorbar=True, **kwds):
- """Show a scalar function defined for the sites of a systems.
+def map(sys, value, colorbar=True, cmap=None,
+ a=None, method='nearest', oversampling=3, file=None, show=True):
+ """Show interpolated map of a function defined for the sites of a system.
Create a pixmap representation of a function of the sites of a system by
- calling `~kwant.plotter.interpolate` and show this pixmap using matplotlib.
+ calling `~kwant.plotter.mask_interpolate` and show this pixmap using
+ matplotlib.
Parameters
----------
- syst : kwant.system.FiniteSystem or kwant.builder.Builder
- The system for whose sites `function` is to be plotted.
- function : function or mapping
- Function which takes a site and returns a number, or a mapping whose
- keys are sites and whose values are numbers.
+ sys : kwant.system.FiniteSystem or kwant.builder.Builder
+ The system for whose sites `value` is to be plotted.
+ value : function or list
+ Function which takes a site and returns a value if the system is a
+ builder, or a list of function values for each system site of the
+ finalized system.
colorbar : bool, optional
Whether to show a color bar. Defaults to `true`.
- kwds : other arguments
- All other arguments are passed to `~kwant.plotter.interpolate`.
+ cmap : `matplotlib` color map or `None`
+ The color map used for sites and optionally hoppings, if `None`,
+ `matplotlib` default is used.
+ a : float, optional
+ Reference length. If not given, it is determined as a typical
+ nearest neighbor distance.
+ method : string, optional
+ Passed to `scipy.interpolate.griddata` and to `matplotlib`
+ `Axes.imshow.interpolation`: "nearest" (default), "linear", or "cubic".
+ oversampling : integer, optional
+ Number of pixels per reference length. Defaults to 3.
+ file : string or file object or `None`
+ The output file. If `None`, output will be shown instead.
+ show : bool
+ Whether `matplotlib.pyplot.show()` is to be called, and the output is
+ to be shown immediately. Defaults to `True`.
Notes
-----
- - See notes of `~kwant.plotter.interpolate`.
-
- - This function uses matplotlib to show the interpolated function.
+ - See notes of `~kwant.plotter.show_interpolate`.
- Matplotlib's interpolation is turned off, if the keyword argument
`method` is not set or set to the default value "nearest".
"""
- img, min, max = interpolate(syst, function, **kwds)
+ sites = sys_leads_sites(sys, 0)
+ coords = sys_leads_pos(sys, sites)
+ if coords.shape[1] != 2:
+ raise ValueError('Only 2D systems can be plotted this way.')
+ if hasattr(value, '__call__'):
+ value = [value(site[0]) for site in sites]
+ else:
+ if not isinstance(sys, system.FiniteSystem):
+ raise ValueError('List of values is only allowed as input'
+ 'for finalized systems.')
+ value = np.array(value)
+ img, min, max = mask_interpolate(coords, value, a, method, oversampling)
border = 0.5 * (max - min) / (np.asarray(img.shape) - 1)
min -= border
max += border
- interpolation = 'nearest' if kwds.get('method') in [None, 'nearest'] \
- else None
- plt.imshow(img.T, extent=(min[0], max[0], min[1], max[1]), origin='lower',
- interpolation=interpolation)
+ fig = Figure()
+ ax = fig.add_subplot(111, aspect='equal')
+ if method != 'nearest':
+ method = 'bi' + method
+ image = ax.imshow(img.T, extent=(min[0], max[0], min[1], max[1]),
+ origin='lower', interpolation=method, cmap=cmap)
if colorbar:
- plt.colorbar()
- plt.show()
+ fig.colorbar(image)
+ return output_fig(fig, file=file, show=show)
+
+# TODO (Anton): Fix plotting of parts of the system using color = np.nan.
+# Not plotting sites currently works, not plotting hoppings does not.
+# TODO (Anton): Allow a more flexible treatment of position than pos_transform
+# (an interface for user-defined pos).
diff --git a/kwant/tests/test_plotter.py b/kwant/tests/test_plotter.py
index 29dc81bc23f0ed6e61170e8a94225c0f1fc9c471..61fffe207eaf05afb955d7dc647969251cc5245a 100644
--- a/kwant/tests/test_plotter.py
+++ b/kwant/tests/test_plotter.py
@@ -1,83 +1,114 @@
-import tempfile, os
-from nose.tools import assert_raises
-import numpy as np
+import tempfile
+import nose
import kwant
from kwant import plotter
+if plotter._mpl_enabled:
+ from mpl_toolkits import mplot3d
+ from matplotlib import pyplot
+
+
+def sys_2d(W=3, r1=3, r2=8):
+ a = 1
+ t = 1.0
+ lat = kwant.lattice.Square(a)
+ sys = kwant.Builder()
+
+ def ring(pos):
+ (x, y) = pos
+ rsq = x ** 2 + y ** 2
+ return r1 ** 2 < rsq < r2 ** 2
+
+ sys[lat.shape(ring, (0, r1 + 1))] = 4 * t
+ for hopping in lat.nearest:
+ sys[sys.possible_hoppings(*hopping)] = - t
+ sym_lead0 = kwant.TranslationalSymmetry([lat.vec((-1, 0))])
+ lead0 = kwant.Builder(sym_lead0)
+
+ def lead_shape(pos):
+ (x, y) = pos
+ return (-1 < x < 1) and (-W / 2 < y < W / 2)
+
+ lead0[lat.shape(lead_shape, (0, 0))] = 4 * t
+ for hopping in lat.nearest:
+ lead0[lead0.possible_hoppings(*hopping)] = - t
+ lead1 = lead0.reversed()
+ sys.attach_lead(lead0)
+ sys.attach_lead(lead1)
+ return sys
+
+
+def sys_3d(W=3, r1=2, r2=4, a=1, t=1.0):
+ lat = kwant.make_lattice(((a, 0, 0), (0, a, 0), (0, 0, a)))
+ lat.nearest = (((1, 0, 0), lat, lat), ((0, 1, 0), lat, lat),
+ ((0, 0, 1), lat, lat))
+ sys = kwant.Builder()
+
+ def ring(pos):
+ (x, y, z) = pos
+ rsq = x ** 2 + y ** 2
+ return (r1 ** 2 < rsq < r2 ** 2) and abs(z) < 2
+ sys[lat.shape(ring, (0, -r2 + 1, 0))] = 4 * t
+ for hopping in lat.nearest:
+ sys[sys.possible_hoppings(*hopping)] = - t
+ sym_lead0 = kwant.TranslationalSymmetry([lat.vec((-1, 0, 0))])
+ lead0 = kwant.Builder(sym_lead0)
+
+ def lead_shape(pos):
+ (x, y, z) = pos
+ return (-1 < x < 1) and (-W / 2 < y < W / 2) and abs(z) < 2
+
+ lead0[lat.shape(lead_shape, (0, 0, 0))] = 4 * t
+ for hopping in lat.nearest:
+ lead0[lead0.possible_hoppings(*hopping)] = - t
+ lead1 = lead0.reversed()
+ sys.attach_lead(lead0)
+ sys.attach_lead(lead1)
+ return sys
-lat = kwant.lattice.Square()
-
-def make_ribbon(width, dir, E, t):
- b = kwant.Builder(kwant.TranslationalSymmetry([(dir, 0)]))
-
- # Add sites to the builder.
- for y in xrange(width):
- b[lat(0, y)] = E
-
- # Add hoppings to the builder.
- for y in xrange(width):
- b[lat(0, y), lat(1, y)] = t
- if y+1 < width:
- b[lat(0, y), lat(0, y+1)] = t
-
- return b
-
-
-def make_rectangle(length, width, E, t):
- b = kwant.Builder()
-
- # Add sites to the builder.
- for x in xrange(length):
- for y in xrange(width):
- b[lat(x, y)] = E
-
- # Add hoppings to the builder.
- for x in xrange(length):
- for y in xrange(width):
- if x+1 < length:
- b[lat(x, y), lat(x+1, y)] = t
- if y+1 < width:
- b[lat(x, y), lat(x, y+1)] = t
-
- return b
def test_plot():
- E = 4.0
- t = -1.0
- length = 5
- width = 5
-
- b = make_rectangle(length, width, E, t)
- b.attach_lead(make_ribbon(width, -1, E, t))
- b.attach_lead(make_ribbon(width, 1, E, t))
-
- directory = tempfile.mkdtemp()
- filename = os.path.join(directory, "test.pdf")
-
- kwant.plot(b.finalized(), filename=filename,
- symbols=plotter.Circle(r=0.25, fcol=plotter.red),
- lines=plotter.Line(lw=0.1, lcol=plotter.red),
- lead_symbols=plotter.Circle(r=0.25, fcol=plotter.black),
- lead_lines=plotter.Line(lw=0.1, lcol=plotter.black),
- lead_fading=[0, 0.2, 0.4, 0.6, 0.8])
-
- os.unlink(filename)
- os.rmdir(directory)
-
-def test_non_2d_fails():
- directory = tempfile.mkdtemp()
- filename = os.path.join(directory, "test.pdf")
-
- for d in [1, 2, 3, 15]:
- b = kwant.Builder()
- lat = kwant.make_lattice(np.identity(d))
- site = kwant.builder.Site(lat, (0,) * d)
- b[site] = 0
- if d == 2:
- kwant.plot(b, filename=filename)
- plotter.interpolate(b, b)
- else:
- assert_raises(ValueError, kwant.plot, b)
- assert_raises(ValueError, plotter.interpolate, b, b)
-
- os.unlink(filename)
- os.rmdir(directory)
+ plot = plotter.plot
+ if not plotter._mpl_enabled:
+ raise nose.SkipTest
+ sys2d = sys_2d()
+ sys3d = sys_3d()
+ color_opts = ['k', (lambda site: site.tag[0]),
+ lambda site: (abs(site.tag[0] / 100),
+ abs(site.tag[1] / 100), 0)]
+ for color in color_opts:
+ for sys in (sys2d, sys3d):
+ fig = plot(sys, site_color=color, cmap='binary', show=False)
+ if color != 'k' and isinstance(color(iter(sys2d.sites()).next()),
+ float):
+ assert fig.axes[0].collections[0].get_array() is not None
+ assert len(fig.axes[0].collections) == 4
+ color_opts = ['k', (lambda site, site2: site.tag[0]),
+ lambda site, site2: (abs(site.tag[0] / 100),
+ abs(site.tag[1] / 100), 0)]
+ for color in color_opts:
+ for sys in (sys2d, sys3d):
+ fig = plot(sys2d, hop_color=color, cmap='binary', show=False,
+ fig_size=(2, 10), dpi=30)
+ if color != 'k' and isinstance(color(iter(sys2d.sites()).next(),
+ None), float):
+ assert fig.axes[0].collections[1].get_array() is not None
+
+ assert isinstance(plot(sys3d, show=False).axes[0], mplot3d.axes3d.Axes3D)
+
+ sys2d.leads = []
+ plot(sys2d, show=False)
+ del sys2d[list(sys2d.hoppings())]
+ plot(sys2d, show=False)
+ with tempfile.TemporaryFile('w+b') as output:
+ plot(sys3d, file=output)
+
+
+def test_map():
+ sys = sys_2d()
+ with tempfile.TemporaryFile('w+b') as output:
+ plotter.map(sys, lambda site: site.tag[0], file=output,
+ method='linear', a=4, oversampling=4, cmap='flag')
+ plotter.map(sys.finalized(), xrange(len(sys.sites())),
+ file=output)
+ nose.tools.assert_raises(ValueError, plotter.map,
+ sys, xrange(len(sys.sites())), file=output)