def _corr2d4fig(spec, a1_label=r'$\bar{A}(\nu_1)$',
a2_label=r'$\bar{A}(\nu_2)$', **contourkwds):
""" Abstract layout for 2d correlation analysis plot.
**contourkwds
Passed directly to _gencontour; includes keywords like xlabel, ylabel
and so forth.
"""
# Maybe this should take X, Y, Z not ts
#fig, ax #how to handle these in general 2d
# Maybe it's helpful to have args for top plots (ie ax1,2,3)
title = contourkwds.pop('title', '')
cbar = contourkwds.pop('cbar', False)
grid = contourkwds.setdefault('grid', True) #Adds grid to plot and side plots
# REFACTOR THIS
cbar_nticks = 5
# This will create a fig
ax1 = plt.subplot2grid((5,5), (0,0), colspan=1) # top left
plt.subplots_adjust(hspace = 0, wspace=0) # Remove whitespace
ax1.plot([0,-1], color='black')
ax1.text(.18, -.78, a1_label, size=12)
ax1.text(.55, -.35, a2_label, size=12)
ax2 = plt.subplot2grid((5,5), (0,1), colspan=4) # top
ax3 = plt.subplot2grid((5,5), (1,0), colspan=1, rowspan=4) #left
ax4 = plt.subplot2grid((5,5), (1, 1), colspan=4, rowspan=4) #main contour
ax3.invert_xaxis()
ax4.yaxis.tick_right()
ax4.xaxis.tick_bottom() #remove top xticks
ax4.yaxis.set_label_position('right')
ax4, contours = _gen2d3d(spec, ax=ax4, **contourkwds)
# Bisecting line
pvutil.diag_line(ax4)
# Fig is created by _gen2d in ax4 _gen2d3d
fig = plt.gcf()
# Hide axis labels
for ax in [ax2, ax3]:
if grid:
pvutil.hide_axis(ax, axis='both', axislabel=True, ticklabels=True)
else:
pvutil.hide_axis(ax, axis='both', hide_everything = True)
pvutil.hide_axis(ax1, axis='both', hide_everything=True)
#plt.colorbar() doesn't work
# Handles its own colorbar (See links below; important)
# http://stackoverflow.com/questions/13784201/matplotlib-2-subplots-1-colorbar
# http://matplotlib.org/api/colorbar_api.html#matplotlib.colorbar.make_axes
if cbar:
if cbar in ['left', 'right', 'top', 'bottom']:
# if bottom or right, should repad this
location = cbar
else:
location = 'top'
cax,kw = mplcbar.make_axes([ax1, ax2, ax3, ax4],
location=location,
pad = 0.05,
aspect = 30, #make skinnier
shrink=0.75)
cb = fig.colorbar(contours, cax=cax,**kw)# ticks=[0,zz.max().max()], **kw)
cb.locator = mplticker.MaxNLocator(nbins=cbar_nticks+1) #Cuts off one usually
cb.set_label(spec.iunit)
cb.update_ticks()
#ax1 will take care of itself in contour
if grid:
if grid == True:
ax2.grid()
ax3.grid()
else:
ax2.grid(color=grid)
ax3.grid(color=grid)
fig.suptitle(title, fontsize='large') # Still overpads
return (ax1, ax2, ax3, ax4)
def _corr2d4fig(spec,
a1_label=r'$\bar{A}(\nu_1)$',
a2_label=r'$\bar{A}(\nu_2)$',
**contourkwds):
""" Abstract layout for 2d correlation analysis plot.
**contourkwds
Passed directly to _gencontour; includes keywords like xlabel, ylabel
and so forth.
"""
# Maybe this should take X, Y, Z not ts
#fig, ax #how to handle these in general 2d
# Maybe it's helpful to have args for top plots (ie ax1,2,3)
title = contourkwds.pop('title', '')
cbar = contourkwds.pop('cbar', False)
grid = contourkwds.setdefault('grid',
True) #Adds grid to plot and side plots
# REFACTOR THIS
cbar_nticks = 5
# This will create a fig
ax1 = plt.subplot2grid((5, 5), (0, 0), colspan=1) # top left
plt.subplots_adjust(hspace=0, wspace=0) # Remove whitespace
ax1.plot([0, -1], color='black')
ax1.text(.18, -.78, a1_label, size=12)
ax1.text(.55, -.35, a2_label, size=12)
ax2 = plt.subplot2grid((5, 5), (0, 1), colspan=4) # top
ax3 = plt.subplot2grid((5, 5), (1, 0), colspan=1, rowspan=4) #left
ax4 = plt.subplot2grid((5, 5), (1, 1), colspan=4, rowspan=4) #main contour
ax3.invert_xaxis()
ax4.yaxis.tick_right()
ax4.xaxis.tick_bottom() #remove top xticks
ax4.yaxis.set_label_position('right')
ax4, contours = _gen2d3d(spec, ax=ax4, **contourkwds)
# Bisecting line
pvutil.diag_line(ax4)
# Fig is created by _gen2d in ax4 _gen2d3d
fig = plt.gcf()
# Hide axis labels
for ax in [ax2, ax3]:
if grid:
pvutil.hide_axis(ax, axis='both', axislabel=True, ticklabels=True)
else:
pvutil.hide_axis(ax, axis='both', hide_everything=True)
pvutil.hide_axis(ax1, axis='both', hide_everything=True)
#plt.colorbar() doesn't work
# Handles its own colorbar (See links below; important)
# http://stackoverflow.com/questions/13784201/matplotlib-2-subplots-1-colorbar
# http://matplotlib.org/api/colorbar_api.html#matplotlib.colorbar.make_axes
if cbar:
if cbar in ['left', 'right', 'top', 'bottom']:
# if bottom or right, should repad this
location = cbar
else:
location = 'top'
cax, kw = mplcbar.make_axes(
[ax1, ax2, ax3, ax4],
location=location,
pad=0.05,
aspect=30, #make skinnier
shrink=0.75)
cb = fig.colorbar(contours, cax=cax,
**kw) # ticks=[0,zz.max().max()], **kw)
cb.locator = mplticker.MaxNLocator(nbins=cbar_nticks +
1) #Cuts off one usually
cb.set_label(spec.iunit)
cb.update_ticks()
#ax1 will take care of itself in contour
if grid:
if grid == True:
ax2.grid()
ax3.grid()
else:
ax2.grid(color=grid)
ax3.grid(color=grid)
fig.suptitle(title, fontsize='large') # Still overpads
return (ax1, ax2, ax3, ax4)
def corr2d(spec2d, sideplots='mean', **pltkwargs):
""" Visualize synchronous, asynchronous or phase angle spectra.
Parameters
----------
sideplots: str or bool ('mean')
If True, sideplots will be put on side axis of cross plots. Use
'empty' to return blank sideplots. mean', 'min', 'max', will
plot these respective spectra on the sideplots.
fill : bool (True)
Contours are lines, or filled regions.
**pltkwargs: dict
Any valid matplotlib contour plot keyword, or skspec general
plotting keyword (grid, title etc...)
Returns
-------
tuple (matplotlib.Axes)
If side plots, returns (ax1, ax2, ax3, ax4)
If not side plots, returns ax4 only
"""
SIDEPAD = 1.10
_reverse_axis = pltkwargs.pop('_reverse_axis', False)
#Side plot line kwds (passed to Spectrum())
linekwds = dict(linewidth=1,
linestyle='-',
color='k', #black
custompadding=0,
)
if sideplots:
if spec2d.spec is None:
raise CorrPlotError('Sideplots require access to original dataset.'
' Not found on %s object.' % type(spec2d))
if sideplots == True:
sideplots = 'mean'
# Should be identical, but maybe not when do heterspectral corr...
try:
symbol1 = spec2d.index._unit.symbol
symbol2 = spec2d.columns._unit.symbol
except Exception:
symbol1 = symbol2 = r'\lambda'
# if centered, add a bar (doesn't matter what kind of centering)
if spec2d.centered:
label1 = r'$\bar{A}(%s_1)$' % symbol1
label2 = r'$\bar{A}(%s_2)$' % symbol2
else:
label1, label2 = r'$A(%s_1)$' % symbol1, r'$A(%s_2)$' % symbol2
# BASED ON SELF, NOT BASED ON THE OBJECT ITSELF!
ax1, ax2, ax3, ax4 = _corr2d4fig(spec2d, label1, label2, **pltkwargs )
# Side plots
data_orig_top = spec2d.spec.loc[spec2d.index[0]:spec2d.index[-1], :]
data_orig_side = spec2d.spec.loc[spec2d.columns[0]:spec2d.columns[-1], :]
top = None
if sideplots == 'mean':
top = data_orig_top.mean(axis=1)
side = data_orig_side.mean(axis=1)
elif sideplots == 'max':
top = data_orig_top.max(axis=1)
side = data_orig_side.max(axis=1)
elif sideplots == 'min':
top = data_orig_top.min(axis=1)
side = data_orig_side.min(axis=1)
elif sideplots == 'all':
top = data_orig_top
side = data_orig_side
elif sideplots == 'empty':
pass
else:
raise CorrPlotError('sideplots keyword must be "mean", "max", "min",'
' "all", or "empty".')
if top is not None:
top.plot(ax=ax2, **linekwds)
side.plot(ax=ax3, **linekwds)
for ax in (ax2, ax3):
pvutil.hide_axis(ax, axis='both')
ax.set_title('')
# Reorient ax3
pvutil.invert_ax(ax3)
#Set sideplot labels
if sideplots != 'empty':
ax2.set_ylabel(sideplots)
ax2.yaxis.set_label_position('right')
#Scale x/y padding just a hair
y1, y2 = ax2.get_ylim()
ax2.set_ylim(y1, SIDEPAD*y2)
x1, x2 = ax3.get_xlim()
ax3.set_xlim(SIDEPAD*x1, x2)
if _reverse_axis:
ax4.set_ylim(ax4.get_ylim()[::-1])
ax4.set_xlim(ax4.get_xlim()[::-1])
#sideplots
ax2.set_xlim(ax2.get_xlim()[::-1])
ax3.set_ylim(ax3.get_ylim()[::-1])
return (ax1, ax2, ax3, ax4)
else:
# If no sideplots, can allow for 3d plots
pltkwargs.setdefault('kind', 'contour')
ax = pltkwargs.pop('ax', _gen2d3d(spec2d, **pltkwargs)[0]) #return axes, not contours
if _reverse_axis:
ax.set_ylim(ax.get_ylim()[::-1])
ax.set_xlim(ax.get_xlim()[::-1])
return ax
def corr2d(spec2d, sideplots='mean', **pltkwargs):
""" Visualize synchronous, asynchronous or phase angle spectra.
Parameters
----------
sideplots: str or bool ('mean')
If True, sideplots will be put on side axis of cross plots. Use
'empty' to return blank sideplots. mean', 'min', 'max', will
plot these respective spectra on the sideplots.
fill : bool (True)
Contours are lines, or filled regions.
**pltkwargs: dict
Any valid matplotlib contour plot keyword, or skspec general
plotting keyword (grid, title etc...)
Returns
-------
tuple (matplotlib.Axes)
If side plots, returns (ax1, ax2, ax3, ax4)
If not side plots, returns ax4 only
"""
SIDEPAD = 1.10
_reverse_axis = pltkwargs.pop('_reverse_axis', False)
#Side plot line kwds (passed to Spectrum())
linekwds = dict(
linewidth=1,
linestyle='-',
color='k', #black
custompadding=0,
)
if sideplots:
if spec2d.spec is None:
raise CorrPlotError('Sideplots require access to original dataset.'
' Not found on %s object.' % type(spec2d))
if sideplots == True:
sideplots = 'mean'
# Should be identical, but maybe not when do heterspectral corr...
try:
symbol1 = spec2d.index._unit.symbol
symbol2 = spec2d.columns._unit.symbol
except Exception:
symbol1 = symbol2 = r'\lambda'
# if centered, add a bar (doesn't matter what kind of centering)
if spec2d.centered:
label1 = r'$\bar{A}(%s_1)$' % symbol1
label2 = r'$\bar{A}(%s_2)$' % symbol2
else:
label1, label2 = r'$A(%s_1)$' % symbol1, r'$A(%s_2)$' % symbol2
# BASED ON SELF, NOT BASED ON THE OBJECT ITSELF!
ax1, ax2, ax3, ax4 = _corr2d4fig(spec2d, label1, label2, **pltkwargs)
# Side plots
data_orig_top = spec2d.spec.loc[spec2d.index[0]:spec2d.index[-1], :]
data_orig_side = spec2d.spec.loc[
spec2d.columns[0]:spec2d.columns[-1], :]
top = None
if sideplots == 'mean':
top = data_orig_top.mean(axis=1)
side = data_orig_side.mean(axis=1)
elif sideplots == 'max':
top = data_orig_top.max(axis=1)
side = data_orig_side.max(axis=1)
elif sideplots == 'min':
top = data_orig_top.min(axis=1)
side = data_orig_side.min(axis=1)
elif sideplots == 'all':
top = data_orig_top
side = data_orig_side
elif sideplots == 'empty':
pass
else:
raise CorrPlotError(
'sideplots keyword must be "mean", "max", "min",'
' "all", or "empty".')
if top is not None:
top.plot(ax=ax2, **linekwds)
side.plot(ax=ax3, **linekwds)
for ax in (ax2, ax3):
pvutil.hide_axis(ax, axis='both')
ax.set_title('')
# Reorient ax3
pvutil.invert_ax(ax3)
#Set sideplot labels
if sideplots != 'empty':
ax2.set_ylabel(sideplots)
ax2.yaxis.set_label_position('right')
#Scale x/y padding just a hair
y1, y2 = ax2.get_ylim()
ax2.set_ylim(y1, SIDEPAD * y2)
x1, x2 = ax3.get_xlim()
ax3.set_xlim(SIDEPAD * x1, x2)
if _reverse_axis:
ax4.set_ylim(ax4.get_ylim()[::-1])
ax4.set_xlim(ax4.get_xlim()[::-1])
#sideplots
ax2.set_xlim(ax2.get_xlim()[::-1])
ax3.set_ylim(ax3.get_ylim()[::-1])
return (ax1, ax2, ax3, ax4)
else:
# If no sideplots, can allow for 3d plots
pltkwargs.setdefault('kind', 'contour')
ax = pltkwargs.pop(
'ax',
_gen2d3d(spec2d, **pltkwargs)[0]) #return axes, not contours
if _reverse_axis:
ax.set_ylim(ax.get_ylim()[::-1])
ax.set_xlim(ax.get_xlim()[::-1])
return ax