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add AR map plotter
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@andrewjlevin
andrewjlevin committed Sep 6, 2024
1 parent 4331a4f commit bbc3f76
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Showing 3 changed files with 126 additions and 280 deletions.
124 changes: 107 additions & 17 deletions src/PyHyperScattering/PlotTools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,7 +116,7 @@ def plot_Imap(self,
chi_width=None,
q_slice=None,
e_slice=None,
cmap=None,
I_cmap=None,
xscale=None,
sample_name=None,
save=True,
Expand All @@ -130,7 +130,7 @@ def plot_Imap(self,
chi_width (int): width of chi wedge for para/perp slices
q_slice (slice): q range entered as slice object
e_slice (slice): energy range entered as slice object
cmap (str or plt.cm): matplotlib colormap, default is 'turbo'
I_cmap (str or plt.cm): matplotlib colormap for intensity, default is 'turbo'
xscale (str): 'log' (default) or 'linear'
sample_name (str): sample name to be included in plot title
save (bool, default True): save figure to new folder in notebook directory
Expand All @@ -146,7 +146,7 @@ def plot_Imap(self,
'energy_default': 285}
Example 'plot_hints' format:
plot_hints = {'cmap': 'turbo',
plot_hints = {'I_cmap': 'turbo',
'xscale': 'log'}
Returns:
Expand All @@ -167,8 +167,8 @@ def plot_Imap(self,
if e_slice is None:
e_tup = self._obj.plot_roi['energy_range']
e_slice = slice(e_tup[0], e_tup[1])
if cmap is None:
cmap = self._obj.plot_hints['cmap']
if I_cmap is None:
I_cmap = self._obj.plot_hints['I_cmap']
if xscale is None:
xscale = self._obj.plot_hints['xscale']
if sample_name is None:
Expand All @@ -194,11 +194,11 @@ def plot_Imap(self,
cmax = float(para_slice.quantile(0.995))

# Generate plot
para_slice.plot(ax=axs[0], xscale=xscale, cmap=cmap, norm=LogNorm(cmin, cmax), add_colorbar=False)
perp_slice.plot(ax=axs[1], xscale=xscale, cmap=cmap, norm=LogNorm(cmin, cmax), add_colorbar=False)
para_slice.plot(ax=axs[0], xscale=xscale, cmap=I_cmap, norm=LogNorm(cmin, cmax), add_colorbar=False)
perp_slice.plot(ax=axs[1], xscale=xscale, cmap=I_cmap, norm=LogNorm(cmin, cmax), add_colorbar=False)

# Add colorbar
sm = plt.cm.ScalarMappable(cmap=cmap, norm=LogNorm(cmin, cmax))
sm = plt.cm.ScalarMappable(cmap=I_cmap, norm=LogNorm(cmin, cmax))
cax = axs[1].inset_axes([1.03, 0, 0.05, 1])
cbar = fig.colorbar(sm, cax=cax, orientation='vertical')
cbar.set_label(label='Intensity [arb. units]', labelpad=12, rotation=270)
Expand All @@ -225,7 +225,7 @@ def plot_IvQ(self,
chi_width=None,
q_slice=None,
selected_energies=None,
cmap=None,
I_cmap=None,
xscale=None,
yscale=None,
sample_name=None,
Expand All @@ -240,7 +240,7 @@ def plot_IvQ(self,
chi_width (int): width of chi wedge for para/perp slices
q_slice (slice): q range entered as slice object
e_slice (slice): energy range entered as slice object
cmap (str or plt.cm): matplotlib colormap, default is 'turbo'
I_cmap (str or plt.cm): matplotlib colormap for intensity, default is 'turbo'
xscale (str): 'log' (default) or 'linear'
sample_name (str): sample name to be included in plot title
save (bool, default True): save figure to new folder in notebook directory
Expand All @@ -258,7 +258,7 @@ def plot_IvQ(self,
[275, 284, 284.4, 284.8, 285.2, 285.6, 286.2, 287, 300, 335])}
Example 'plot_hints' format:
plot_hints = {'cmap': 'turbo',
plot_hints = {'I_cmap': 'turbo',
'xscale': 'log',
'yscale': 'log}
Expand All @@ -279,10 +279,10 @@ def plot_IvQ(self,
q_slice = slice(q_tup[0], q_tup[1])
if selected_energies is None:
selected_energies = self._obj.plot_roi['selected_energies']
if cmap is None:
cmap = self._obj.plot_hints['cmap']
if isinstance(cmap, str): # convert to matplotlib ListedColormap object
cmap = getattr(plt.cm, cmap)
if I_cmap is None:
I_cmap = self._obj.plot_hints['I_cmap']
if isinstance(I_cmap, str): # convert to matplotlib ListedColormap object
I_cmap = getattr(plt.cm, I_cmap)
if xscale is None:
xscale = self._obj.plot_hints['xscale']
if yscale is None:
Expand All @@ -305,7 +305,7 @@ def plot_IvQ(self,
# Plot
fig, axs = plt.subplots(ncols=2, figsize=(8,4), tight_layout=True)

colors = cmap(np.linspace(0,1,len(selected_energies)))
colors = I_cmap(np.linspace(0,1,len(selected_energies)))
for i, energy in enumerate(para_slice.energy.values):
para_slice.sel(energy=energy).plot.line(ax=axs[0], color=colors[i], yscale=yscale, xscale=xscale, label=energy)
perp_slice.sel(energy=energy).plot.line(ax=axs[1], color=colors[i], yscale=yscale, xscale=xscale, label=energy)
Expand All @@ -328,4 +328,94 @@ def plot_IvQ(self,
fig.savefig(savePath.joinpath(filename))

return fig, axs


def plot_ARmap(self,
pol=None,
chi_width=None,
q_slice=None,
e_slice=None,
I_cmap=None,
xscale=None,
ar_vlim=None,
sample_name=None,
save=True,
filename=None,
savePath=None):
"""
Plots anistropy ratio heatmap
Inputs:
pol (int): X-ray polarization to determine para/perp chi regions
chi_width (int): width of chi wedge for para/perp slices
q_slice (slice): q range entered as slice object
e_slice (slice): energy range entered as slice object
I_cmap (str or plt.cm): matplotlib colormap for intensity, default is 'turbo'
xscale (str): 'log' (default) or 'linear'
sample_name (str): sample name to be included in plot title
save (bool, default True): save figure to new folder in notebook directory
savePath (pathlib.Path): pathlib directory for where to save plots (will create directory)
defaults to a new 'ARmap_plots' folder in notebook working directory
filename (str): filename to name saved figure
defaults to f'{sample_name}_chi-{chi_width}_q-{q_slice.start}-{q_slice.stop}_energy-{e_slice.start}-{e_slice.stop}_.png'
Example 'plot_roi' format:
plot_roi = {'chi_width': 90,
'q_range': (0.01, 0.09),
'energy_range': (280, 295),
'energy_default': 285}
Example 'plot_hints' format:
plot_hints = {'I_cmap': 'turbo',
'xscale': 'log'}
Returns:
fig: matplotlib figure object of the AR map plots
ax: list of the 2 matplotlib axes object of the AR map plots
"""
# Load default plot roi values from 'plot_roi' attribute / dict
# Load default plot hint values from 'plot_hints' attribute / dict
# Can be overwritten in the function call
if pol is None:
pol = int(self._obj.polarization)
if chi_width is None:
chi_width = self._obj.plot_roi['chi_width']
if q_slice is None:
q_tup = self._obj.plot_roi['q_range']
q_slice = slice(q_tup[0], q_tup[1])
if e_slice is None:
e_tup = self._obj.plot_roi['energy_range']
e_slice = slice(e_tup[0], e_tup[1])
if I_cmap is None:
I_cmap = self._obj.plot_hints['I_cmap']
if xscale is None:
xscale = self._obj.plot_hints['xscale']
if ar_vlim is None:
ar_vlim = 1
if sample_name is None:
sample_name = str(self._obj.sample_name.values)

# Extract AR data:
sel_DA = self._obj.sel(q=q_slice, energy=e_slice)
ar_DA = sel_DA.rsoxs.AR(chi_width=chi_width/2)

# Plot
im = ar_DA.plot.pcolormesh(figsize=(6,4), norm=plt.Normalize(-ar_vlim, ar_vlim))

im.figure.suptitle('Anisotropy Ratio (AR) Map', fontsize=14, x=0.43)
im.axes.set(title=f'{sample_name}, Polarization = {pol}°, Chi Width = {chi_width}°', ylabel='Photon Energy [eV]', xlabel='q [$Å^{-1}$]', xscale='log')
im.colorbar.set_label('AR [arb. units]', rotation=270, labelpad=12)

# Extract figure and axes to return matplotlib object
fig = im.figure
ax = im.axes

# Save plot if true (saves to notebook working directory)
if save:
if filename is None:
filename = f'{sample_name}_chi-{chi_width}_q-{q_slice.start}-{q_slice.stop}_energy-{e_slice.start}-{e_slice.stop}_.png'
if savePath is None:
savePath = pathlib.Path.cwd().joinpath('ARmap_plots')
savePath.mkdir(exist_ok=True)
fig.savefig(savePath.joinpath(filename))

return fig, ax
14 changes: 7 additions & 7 deletions src/PyHyperScattering/RSoXS.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -102,10 +102,10 @@ def select_chi(self,chi,method='nearest'):

def select_q(self,q,method='interp'):
return self._obj.sel(q=q,method=method)

def select_pol(self,pol,method='nearest'):
return self._obj.sel(polarization=pol,method=method)


def AR(self,calc2d=False,two_AR=False,chi_width=5,calc2d_norm_energy=None):
'''
Calculate the RSoXS Anisotropic Ratio (AR) of either a single RSoXS scan or a polarized pair of scans.
Expand All @@ -120,18 +120,18 @@ def AR(self,calc2d=False,two_AR=False,chi_width=5,calc2d_norm_energy=None):
chi_width (int, default 5): the width of chi slices used in calculating AR.
'''
if(not calc2d):
para = self.slice_chi(0,chi_width=chi_width)
perp = self.slice_chi(-90,chi_width=chi_width)
para = self.slice_chi(0,chi_width=chi_width).mean('chi')
perp = self.slice_chi(-90,chi_width=chi_width).mean('chi')
return ((para - perp) / (para+perp))
elif(calc2d):
para_pol = self.select_pol(0)
perp_pol = self.select_pol(90)

para_para = para_pol.rsoxs.slice_chi(0,chi_width=chi_width)
para_perp = para_pol.rsoxs.slice_chi(-90,chi_width=chi_width)
para_para = para_pol.rsoxs.slice_chi(0,chi_width=chi_width).mean('chi')
para_perp = para_pol.rsoxs.slice_chi(-90,chi_width=chi_width).mean('chi')

perp_perp = perp_pol.rsoxs.slice_chi(-90,chi_width=chi_width)
perp_para = perp_pol.rsoxs.slice_chi(0,chi_width=chi_width)
perp_perp = perp_pol.rsoxs.slice_chi(-90,chi_width=chi_width).mean('chi')
perp_para = perp_pol.rsoxs.slice_chi(0,chi_width=chi_width).mean('chi')

AR_para = ((para_para - para_perp)/(para_para+para_perp))
AR_perp = ((perp_perp - perp_para)/(perp_perp+perp_para))
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