def plotRadialProjection(pattern,
parameters,
logscale=True,
offset=1.e-5,
unit="q_nm^-1"):
""" Perform integration over azimuthal angle and plot as function of radius.
:param unit:can be "q_nm^-1", "q_A^-1", "2th_deg", "2th_rad", "r_mm".
:type unit: str
"""
qs, intensities = azimuthalIntegration(pattern, parameters, unit=unit)
if logscale:
plt.semilogy(qs, intensities + offset)
else:
plt.plot(qs, intensities)
if (unit == "q_nm^-1"):
plt.xlabel("q (1/nm)")
elif (unit == "q_A^-1"):
plt.xlabel("q (1/A)")
elif (unit == "2th_deg"):
plt.xlabel("2theta (degrees)")
elif (unit == "2th_rad"):
plt.xlabel("2theta (radians)")
elif (unit == "r_mm"):
plt.xlabel("mm")
plt.ylabel("Intensity (arb. units)")
plt.tight_layout()
def plotImage(pattern, logscale=False, offset=1e-1):
""" Workhorse function to plot an image
:param logscale: Whether to show the data on logarithmic scale (z axis) (default False).
:type logscale: bool
:param offset: Offset to apply if logarithmic scaling is on.
:type offset: float
"""
plt.figure()
# Get limits.
mn, mx = pattern.min(), pattern.max()
x_range, y_range = pattern.shape
if logscale:
if mn <= 0.0:
mn += pattern.min() + offset
pattern = pattern.astype(float) + mn
plt.imshow(pattern,
norm=mpl.colors.LogNorm(vmin=mn, vmax=mx),
cmap="viridis")
else:
plt.imshow(pattern, norm=Normalize(vmin=mn, vmax=mx), cmap='viridis')
plt.xlabel(r'$x$ (pixel)')
plt.ylabel(r'$y$ (pixel)')
plt.xlim([0, x_range - 1])
plt.ylim([0, y_range - 1])
plt.tight_layout()
plt.colorbar()
def plotImage(pattern,
logscale=False,
offset=1e-1,
symlog=False,
*argv,
**kwargs):
""" Workhorse function to plot an image
:param logscale: Whether to show the data on logarithmic scale (z axis) (default False).
:type logscale: bool
:param offset: Offset to apply if logarithmic scaling is on.
:type offset: float
:param symlog: If logscale is True, to show the data on symlogarithmic scale (z axis) (default False).
:type symlog: bool
:return: the handles of figure and axis
:rtype: figure,axis
"""
fig, ax = plt.subplots()
# Get limits.
mn, mx = pattern.min(), pattern.max()
x_range, y_range = pattern.shape
if logscale:
if mn <= 0.0:
mn = pattern.min() + offset
mx = pattern.max() + offset
pattern = pattern.astype(float) + offset
# default plot setup
kwargs['cmap'] = kwargs.pop('cmap', "viridis")
if symlog:
kwargs['norm'] = kwargs.pop(
'norm', mpl.colors.SymLogNorm(0.015, vmin=mn, vmax=mx))
else:
kwargs['norm'] = kwargs.pop('norm',
mpl.colors.LogNorm(vmin=mn, vmax=mx))
axes = kwargs.pop('axes', None)
plt.imshow(pattern, *argv, **kwargs)
else:
kwargs['norm'] = kwargs.pop('norm', Normalize(vmin=mn, vmax=mx))
kwargs['cmap'] = kwargs.pop('cmap', "viridis")
plt.imshow(pattern, *argv, **kwargs)
plt.xlabel(r'$x$ (pixel)')
plt.ylabel(r'$y$ (pixel)')
plt.xlim([0, x_range - 1])
plt.ylim([0, y_range - 1])
plt.tight_layout()
plt.colorbar()
return fig, ax
def plotRadialProjection(pattern, parameters, logscale=True, offset=1.e-5):
""" Perform integration over azimuthal angle and plot as function of radius. """
qs, intensities = azimuthalIntegration(pattern, parameters)
if logscale:
plt.semilogy(qs, intensities + offset)
else:
plt.plot(qs, intensities)
plt.xlabel("q (1/nm)")
plt.ylabel("Intensity (arb. units)")
plt.tight_layout()
def plotImage(pattern,
logscale=False,
offset=None,
symlog=False,
*argv,
**kwargs):
""" Workhorse function to plot an image
:param logscale: Whether to show the data on logarithmic scale (z axis) (default False).
:type logscale: bool
:param offset: Offset to apply to the pattern.
:type offset: float
:param symlog: To show the data on symlogarithmic scale (z axis) (default False).
:type symlog: bool
"""
fig, ax = plt.subplots()
# Get limits.
mn, mx = pattern.min(), pattern.max()
x_range, y_range = pattern.shape
if offset:
mn = pattern.min() + offset
mx = pattern.max() + offset
pattern = pattern.astype(float) + offset
if (logscale and symlog):
print('logscale and symlog are both true.\noverrided by logscale')
# default plot setup
if (logscale or symlog):
kwargs['cmap'] = kwargs.pop('cmap', "viridis")
if logscale:
kwargs['norm'] = kwargs.pop('norm', mpl.colors.LogNorm())
elif symlog:
kwargs['norm'] = kwargs.pop('norm', mpl.colors.SymLogNorm(0.015))
axes = kwargs.pop('axes', None)
plt.imshow(pattern, *argv, **kwargs)
else:
kwargs['norm'] = kwargs.pop('norm', Normalize(vmin=mn, vmax=mx))
kwargs['cmap'] = kwargs.pop('cmap', "viridis")
plt.imshow(pattern, *argv, **kwargs)
plt.xlabel(r'$x$ (pixel)')
plt.ylabel(r'$y$ (pixel)')
plt.xlim([0, x_range - 1])
plt.ylim([0, y_range - 1])
plt.tight_layout()
plt.colorbar()