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plot.py
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plot.py
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try:
import matplotlib.pyplot as native_plot
from mpl_toolkits.mplot3d import Axes3D
except ImportError:
class FakePlotLibrary(object):
def stub(self, *args, **kwargs):
raise Exception("No plot library available")
figure = stub
plot = stub
scatter = stub
hist = stub
semilogx = stub
semilogy = stub
loglog = stub
errorbar = stub
text = stub
imshow = stub
pcolor = stub
pcolormesh = stub
xlabel = stub
ylabel = stub
xlim = stub
ylim = stub
native_plot = FakePlotLibrary()
import numpy
try:
from pynbody.array import SimArray
from pynbody.snapshot import SimSnap
try:
from pynbody.snapshot import new
except:
from pynbody.snapshot import _new as new
import pynbody.plot.sph as pynbody_sph
HAS_PYNBODY = True
except ImportError:
HAS_PYNBODY = False
from amuse.support.exceptions import AmuseException
from amuse.units import units, constants
from amuse.units import quantities
from amuse.support import console
auto_label = "{0}"
custom_label = "{0} {1}"
class UnitlessArgs(object):
current_plot = None
@classmethod
def strip(self, *args, **kwargs):
if self.current_plot is native_plot.gca():
args = [arg.as_quantity_in(unit) if quantities.is_quantity(arg) else arg
for arg, unit in map(lambda *x : tuple(x), args, self.arg_units)]
self.clear()
self.current_plot = native_plot.gca()
for arg in args:
if quantities.is_quantity(arg):
arg = console.current_printing_strategy.convert_quantity(arg)
self.stripped_args.append(arg.value_in(arg.unit))
self.arg_units.append(arg.unit)
self.unitnames_of_args.append("["+str(arg.unit)+"]")
else:
self.stripped_args.append(arg)
self.arg_units.append(None)
self.unitnames_of_args.append("")
return self.stripped_args
@classmethod
def clear(self):
self.stripped_args = []
self.arg_units = []
self.unitnames_of_args = []
@classmethod
def value_in(self, unit, *args):
if len(args) < 1:
return args
if unit is not None:
args = [arg.value_in(unit) for arg in args]
if len(args) > 1:
return args
else:
return args[0]
@classmethod
def value_in_x_unit(self, *args):
return self.value_in(UnitlessArgs.arg_units[0], *args)
@classmethod
def value_in_y_unit(self, *args):
return self.value_in(UnitlessArgs.arg_units[1], *args)
@classmethod
def value_in_z_unit(self, *args):
return self.value_in(UnitlessArgs.arg_units[2], *args)
@classmethod
def x_label(self, s=None):
unit_name = self.unitnames_of_args[0]
return self.label(s, unit_name)
@classmethod
def y_label(self, s=None):
unit_name = self.unitnames_of_args[1]
return self.label(s, unit_name)
@classmethod
def z_label(self, s=None):
unit_name = self.unitnames_of_args[2]
return self.label(s, unit_name)
@classmethod
def label(self, s, unit_name):
if s is None:
return auto_label.format(unit_name)
else:
return custom_label.format(s, unit_name)
def latex_support():
from matplotlib import rc
#rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
#rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
def plot(*args, **kwargs):
args = UnitlessArgs.strip(*args, **kwargs)
result = native_plot.plot(*args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def plot3(*args, **kwargs):
args = UnitlessArgs.strip(*args, **kwargs)
fig = native_plot.figure()
ax = fig.gca(projection='3d')
return ax.plot(*args, **kwargs)
def semilogx(*args, **kwargs):
args = UnitlessArgs.strip(*args, **kwargs)
result = native_plot.semilogx(*args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def semilogy(*args, **kwargs):
args = UnitlessArgs.strip(*args, **kwargs)
result = native_plot.semilogy(*args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def loglog(*args, **kwargs):
args = UnitlessArgs.strip(*args, **kwargs)
result = native_plot.loglog(*args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def scatter(x, y, **kwargs):
args = UnitlessArgs.strip(x,y)
result = native_plot.scatter(*UnitlessArgs.stripped_args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def fill_between(x, y1, y2, **kwargs):
x, y1 = UnitlessArgs.strip(x,y1)
y2 = UnitlessArgs.value_in_y_unit(y2)
result = native_plot.fill_between(x, y1, y2, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def hist(x, **kwargs):
args = UnitlessArgs.strip(x)
result = native_plot.hist(args[0], **kwargs)
UnitlessArgs.unitnames_of_args.append("")
return result
def errorbar(*args, **kwargs):
for label in ['yerr', 'xerr']:
if label in kwargs:
args += (kwargs.pop(label),)
else:
args += (None,)
yerr, xerr = args[2:4]
args1 = UnitlessArgs.strip(*args[:2])
if xerr is not None:
xerr = UnitlessArgs.value_in_x_unit(xerr)
if yerr is not None:
yerr = UnitlessArgs.value_in_y_unit(yerr)
args = args1 + [yerr, xerr]
result = native_plot.errorbar(*args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def text(x, y, s, **kwargs):
strp_x, strp_y = UnitlessArgs.strip(x, y)
return native_plot.text(strp_x, strp_y, s, **kwargs)
def xlabel(s, *args, **kwargs):
if not '[' in s:
s = UnitlessArgs.x_label(s)
return native_plot.xlabel(s, *args, **kwargs)
def ylabel(s, *args, **kwargs):
if not '[' in s:
s = UnitlessArgs.y_label(s)
return native_plot.ylabel(s, *args, **kwargs)
def xlim(*args, **kwargs):
if len(UnitlessArgs.arg_units) is 0:
raise AmuseException("Cannot call xlim function before plotting")
args = UnitlessArgs.value_in_x_unit(*args)
for name in ("xmin", "xmax"):
if name in kwargs:
kwargs[name] = UnitlessArgs.value_in_x_unit(kwargs[name])
native_plot.xlim(*args, **kwargs)
def ylim(*args, **kwargs):
if len(UnitlessArgs.arg_units) is 0:
raise AmuseException("Cannot call ylim function before plotting")
args = UnitlessArgs.value_in_y_unit(*args)
for name in ("ymin", "ymax"):
if name in kwargs:
kwargs[name] = UnitlessArgs.value_in_y_unit(kwargs[name])
native_plot.ylim(*args, **kwargs)
def axvline(x, **kwargs):
x_number = UnitlessArgs.value_in_x_unit(x)
return native_plot.axvline(x_number, **kwargs)
def axhline(y, **kwargs):
y_number = UnitlessArgs.value_in_y_unit(y)
return native_plot.axhline(y_number, **kwargs)
def circle_with_radius(x, y, radius, **kwargs):
x, y = UnitlessArgs.strip(x, y)[:2]
radius = UnitlessArgs.value_in_x_unit(radius)
circle = native_plot.Circle((x, y), radius, **kwargs)
return native_plot.gca().add_artist(circle)
def fix_xyz_axes(X, Y, Z):
if not (X.shape == Z.shape and Y.shape == Z.shape):
X, Y = numpy.meshgrid(X, Y)
return X, Y, Z
def log_norm(Z, vmin, vmax):
# for log scale, 0 is considered a missing value
masked_Z = numpy.ma.masked_equal(Z, 0.0, copy=False)
vmin = UnitlessArgs.value_in_z_unit(vmin) if vmin else masked_Z.min()
vmax = UnitlessArgs.value_in_z_unit(vmax) if vmax else masked_Z.max()
from matplotlib.colors import LogNorm
return masked_Z, LogNorm(vmin=vmin, vmax=vmax)
def fix_pcolor_norm(args, kwargs):
args = [a for a in args]
if 'vlog' in kwargs and kwargs['vlog']:
zmin = kwargs.pop("vmin", None)
zmax = kwargs.pop("vmax", None)
args[2], kwargs['norm']= log_norm(args[2], zmin, zmax)
del kwargs['vlog']
else:
for name in ("vmin", "vmax"):
if name in kwargs:
kwargs[name] = UnitlessArgs.value_in_z_unit(kwargs[name])
return args, kwargs
def has_log_scaling(array):
diff = numpy.diff(array)
if numpy.all(diff - diff[0] < diff[0]/10.):
return False
logdiff = numpy.diff(numpy.log10(array))
if numpy.all(logdiff - logdiff[0] < logdiff[0]/10.):
return True
raise AmuseException("This method cannot be used for non regular arrays")
def imshow_color_plot(x, y, z, label=None, add_colorbar=False, **kwargs):
"""
Plot a density matrix as a color map using imshow,
this gives a smoother image then pcolor(mesh)
It only works if x and y are regular (linear or logarithmic).
"""
X, Y, Z = UnitlessArgs.strip(x, y, z)
X, Y, Z = fix_xyz_axes(X, Y, Z)
xlow = X[0,0]
xhigh = X[-1,-1]
ylow = Y[0,0]
yhigh = Y[-1,-1]
extent = (xlow, xhigh, ylow, yhigh)
(X, Y, Z), kwargs = fix_pcolor_norm((X, Y, Z), kwargs)
kwargs['origin'] = 'lower'
kwargs['aspect'] = 'auto'
kwargs['extent'] = extent
cax = native_plot.imshow(Z, **kwargs)
if has_log_scaling(X[0,:]):
native_plot.gca().set_xscale('log')
if has_log_scaling(Y[:,0]):
native_plot.gca().set_yscale('log')
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
if add_colorbar:
bar = native_plot.colorbar(cax)
bar.set_label(UnitlessArgs.z_label(label))
return cax, bar
else:
return cax
def pcolor(*args, **kwargs):
stripped_args = UnitlessArgs.strip(*args)
stripped_args, kwargs = fix_pcolor_norm(stripped_args, kwargs)
result = native_plot.pcolor(*stripped_args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def pcolormesh(*args, **kwargs):
stripped_args = UnitlessArgs.strip(*args)
stripped_args, kwargs = fix_pcolor_norm(stripped_args, kwargs)
result = native_plot.pcolormesh(*stripped_args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def contour(*args, **kwargs):
if len(args)%2 == 0:
stripped_args = UnitlessArgs.strip(*args[:-1])
levels = args[-1]
z_unit = UnitlessArgs.arg_units[-1]
if quantities.is_quantity(levels):
stripped_args.append(levels.value_in(z_unit))
else:
stripped_args = UnitlessArgs.strip(*args)
if 'levels' in kwargs:
levels = kwargs['levels']
z_unit = UnitlessArgs.arg_units[-1]
if quantities.is_quantity(levels):
kwargs['levels'] = levels.value_in(z_unit)
result = native_plot.contour(*stripped_args, **kwargs)
native_plot.xlabel(UnitlessArgs.x_label())
native_plot.ylabel(UnitlessArgs.y_label())
return result
def smart_length_units_for_vector_quantity(quantity):
length_units = [units.Mpc, units.kpc, units.parsec, units.AU, units.RSun, units.km]
total_size = max(quantity) - min(quantity)
for length_unit in length_units:
if total_size > (1 | length_unit):
return length_unit
return units.m
def sph_particles_plot(particles, u_range = None, min_size = 100, max_size = 10000,
alpha = 0.1, gd_particles=None, width=None, view=None):
"""
Very simple and fast procedure to make a plot of the hydrodynamics state of
a set of SPH particles. The particles must have the following attributes defined:
position, u, h_smooth.
For a more accurate plotting procedure, see for example:
examples/applications/christmas_card_2010.py
:argument particles: the SPH particles to be plotted
:argument u_range: range of internal energy for color scale [umin, umax]
:argument min_size: minimum size to use for plotting particles, in pixel**2
:argument max_size: maximum size to use for plotting particles, in pixel**2
:argument alpha: the opacity of each particle
:argument gd_particles: non-SPH particles can be indicated with white circles
:argument view: the (physical) region to plot [xmin, xmax, ymin, ymax]
"""
positions = particles.position
us = particles.u
h_smooths = particles.h_smooth
x, y, z = positions.x, positions.y, positions.z
z, x, y, us, h_smooths = z.sorted_with(x, y, us, h_smooths)
if u_range:
u_min, u_max = u_range
else:
u_min, u_max = min(us), max(us)
log_u = numpy.log((us / u_min)) / numpy.log((u_max / u_min))
clipped_log_u = numpy.minimum(numpy.ones_like(log_u), numpy.maximum(numpy.zeros_like(log_u), log_u))
red = 1.0 - clipped_log_u**4
blue = clipped_log_u**4
green = numpy.minimum(red, blue)
colors = numpy.transpose(numpy.array([red, green, blue]))
n_pixels = native_plot.gcf().get_dpi() * native_plot.gcf().get_size_inches()
current_axes = native_plot.gca()
current_axes.set_axis_bgcolor('#101010')
if width is not None:
view = width * [-0.5, 0.5, -0.5, 0.5]
if view:
current_axes.set_aspect("equal", adjustable = "box")
length_unit = smart_length_units_for_vector_quantity(view)
current_axes.set_xlim(view[0].value_in(length_unit),
view[1].value_in(length_unit), emit=True, auto=False)
current_axes.set_ylim(view[2].value_in(length_unit),
view[3].value_in(length_unit), emit=True, auto=False)
phys_to_pix2 = n_pixels[0]*n_pixels[1] / ((view[1]-view[0])**2 + (view[3]-view[2])**2)
else:
current_axes.set_aspect("equal", adjustable = "datalim")
length_unit = smart_length_units_for_vector_quantity(x)
phys_to_pix2 = n_pixels[0]*n_pixels[1] / ((max(x)-min(x))**2 + (max(y)-min(y))**2)
sizes = numpy.minimum(numpy.maximum((h_smooths**2 * phys_to_pix2), min_size), max_size)
x = x.as_quantity_in(length_unit)
y = y.as_quantity_in(length_unit)
scatter(x, y, s=sizes, c=colors, edgecolors="none", alpha=alpha)
if gd_particles:
scatter(gd_particles.x, gd_particles.y, c='w', marker='o')
xlabel('x')
ylabel('y')
def convert_particles_to_pynbody_data(particles, length_unit=units.kpc, pynbody_unit="kpc"):
if not HAS_PYNBODY:
raise AmuseException("Couldn't find pynbody")
if hasattr(particles, "u"):
pynbody_data = new(gas=len(particles))
else:
pynbody_data = new(dm=len(particles))
pynbody_data._filename = "AMUSE"
if hasattr(particles, "mass"):
pynbody_data['mass'] = SimArray(particles.mass.value_in(units.MSun), "Msol")
if hasattr(particles, "position"):
pynbody_data['x'] = SimArray(particles.x.value_in(length_unit), pynbody_unit)
pynbody_data['y'] = SimArray(particles.y.value_in(length_unit), pynbody_unit)
pynbody_data['z'] = SimArray(particles.z.value_in(length_unit), pynbody_unit)
if hasattr(particles, "velocity"):
pynbody_data['vx'] = SimArray(particles.vx.value_in(units.km / units.s), "km s^-1")
pynbody_data['vy'] = SimArray(particles.vy.value_in(units.km / units.s), "km s^-1")
pynbody_data['vz'] = SimArray(particles.vz.value_in(units.km / units.s), "km s^-1")
if hasattr(particles, "h_smooth"):
pynbody_data['smooth'] = SimArray(particles.h_smooth.value_in(length_unit), pynbody_unit)
if hasattr(particles, "rho"):
pynbody_data['rho'] = SimArray(particles.rho.value_in(units.g / units.cm**3),
"g cm^-3")
if hasattr(particles, "temp"):
pynbody_data['temp'] = SimArray(particles.temp.value_in(units.K), "K")
elif hasattr(particles, "u"):
# pynbody_data['u'] = SimArray(particles.u.value_in(units.km**2 / units.s**2), "km^2 s^-2")
temp = 2.0/3.0 * particles.u * mu() / constants.kB
pynbody_data['temp'] = SimArray(temp.value_in(units.K), "K")
return pynbody_data
def mu(X = None, Y = 0.25, Z = 0.02, x_ion = 0.1):
"""
Compute the mean molecular weight in kg (the average weight of particles in a gas)
X, Y, and Z are the mass fractions of Hydrogen, of Helium, and of metals, respectively.
x_ion is the ionisation fraction (0 < x_ion < 1), 1 means fully ionised
"""
if X is None:
X = 1.0 - Y - Z
elif abs(X + Y + Z - 1.0) > 1e-6:
raise AmuseException("Error in calculating mu: mass fractions do not sum to 1.0")
return constants.proton_mass / (X*(1.0+x_ion) + Y*(1.0+2.0*x_ion)/4.0 + Z*x_ion/2.0)
def _smart_length_units_for_pynbody_data(length):
length_units = [(units.Gpc, "Gpc"), (units.Mpc, "Mpc"), (units.kpc, "kpc"),
(units.parsec, "pc"), (units.AU, "au"), (1.0e9*units.m, "1.0e9 m"),
(1000*units.km, "1000 km"), (units.km, "km")]
for length_unit, pynbody_unit in length_units:
if length > (1 | length_unit):
return length_unit, pynbody_unit
return units.m, "m"
def pynbody_column_density_plot(particles, width=None, qty='rho', units=None,
sideon=False, faceon=False, **kwargs):
if not HAS_PYNBODY:
raise AmuseException("Couldn't find pynbody")
if width is None:
width = 2.0 * particles.position.lengths_squared().amax().sqrt()
length_unit, pynbody_unit = _smart_length_units_for_pynbody_data(width)
pyndata = convert_particles_to_pynbody_data(particles, length_unit, pynbody_unit)
UnitlessArgs.strip([1]|length_unit, [1]|length_unit)
if sideon:
function = pynbody_sph.sideon_image
elif faceon:
function = pynbody_sph.faceon_image
else:
function = pynbody_sph.image
if units is None and qty == 'rho':
units = 'm_p cm^-2'
result = function(pyndata, width=width.value_in(length_unit), qty=qty, units=units, **kwargs)
UnitlessArgs.current_plot = native_plot.gca()
return result
def effective_iso_potential_plot(gravity_code,
omega,
center_of_rotation = [0, 0]|units.AU,
xlim = [-1.5, 1.5] | units.AU,
ylim = [-1.5, 1.5] | units.AU,
resolution = [1000, 1000],
number_of_contours = 20,
fraction_screen_filled = 0.5,
quadratic_contour_levels = True,
contour_kwargs = dict(),
omega2 = None,
center_of_rotation2 = [0, 0]|units.AU,
fraction_screen_filled2 = 0.2,
projection3D=False):
"""
Create a contour plot of the effective potential of particles in a gravity code.
The code needs to support 'get_potential_at_point' only, so it can also be an
instance of Bridge.
:argument gravity_code: an instance of a gravity code
:argument omega: The angular velocity of the system
:argument center_of_rotation: The (2D) center around which the system rotates, usually the center of mass
:argument xlim: Range in x coordinate; width of window
:argument ylim: Range in y coordinate; width of window
:argument resolution: Number of points to sample potential for x and y direction
:argument number_of_contours: How many contour lines to plot
:argument fraction_screen_filled: Lowest contour will enclose this fraction of the screen
:argument quadratic_contour_levels: Quadratic or linear scaling between contour levels
:argument contour_kwargs: Optional keyword arguments for pyplot.contour
"""
UnitlessArgs.strip(xlim, ylim)
xlim, ylim = UnitlessArgs.stripped_args
x_num = numpy.linspace(xlim[0], xlim[1], resolution[0])
y_num = numpy.linspace(ylim[0], ylim[1], resolution[1])
x_num, y_num = numpy.meshgrid(x_num, y_num)
x = (x_num | UnitlessArgs.arg_units[0]).flatten()
y = (y_num | UnitlessArgs.arg_units[1]).flatten()
zeros = x.aszeros()
potential = gravity_code.get_potential_at_point(zeros, x, y, zeros)
potential -= omega**2 * ((x-center_of_rotation[0])**2 + (y-center_of_rotation[1])**2) / 2.0
if projection3D:
from matplotlib import cm
ax = native_plot.gca(projection='3d')
Z = potential.number.reshape(resolution[::-1])
levels = set_contour_levels(potential, number_of_contours, fraction_screen_filled, quadratic_contour_levels)
Z = numpy.maximum(Z, levels[0])
ax.plot_surface(x_num, y_num, Z, rstride=1, cstride=1, cmap=cm.spectral,
linewidth=0, antialiased=False, vmin=levels[0], vmax=3*levels[-1]-2*levels[0])
ax.set_xlabel('X')
ax.set_xlim(-1, 1)
ax.set_ylabel('Y')
ax.set_ylim(-1, 1)
ax.set_zlabel('Z')
ax.set_zlim(levels[0], levels[-1])
return potential
levels = set_contour_levels(potential, number_of_contours, fraction_screen_filled, quadratic_contour_levels)
CS = native_plot.contour(x_num, y_num, potential.number.reshape(resolution[::-1]), levels, **contour_kwargs)
#~native_plot.clabel(CS, inline=1, fontsize=10)
if omega2 is None:
return potential
potential2 = potential - omega2**2 * ((x-center_of_rotation2[0])**2 + (y-center_of_rotation2[1])**2) / 2.0
#~levels = set_contour_levels(potential, number_of_contours2, fraction_screen_filled2, quadratic_contour_levels2)
levels = set_contour_levels(potential2, number_of_contours, fraction_screen_filled2, quadratic_contour_levels)
CS = native_plot.contour(x_num, y_num, potential2.number.reshape(resolution[::-1]), levels, **contour_kwargs)
return potential.reshape(resolution[::-1]), potential2.reshape(resolution[::-1])
def set_contour_levels(potential, number_of_contours, fraction_screen_filled, quadratic_contour_levels):
uniform = numpy.linspace(0.0, 1.0, number_of_contours)
V_max = potential.amax().number
V_min = potential.sorted().number[int(len(potential)*(1-fraction_screen_filled))]
if quadratic_contour_levels:
levels = V_min + (V_max-V_min) * uniform * (2 - uniform)
else:
levels = V_min + (V_max-V_min) * uniform
return levels