def gen_subtitles_time(self, times):
"""Generate titles for subplots at a list of times.
"""
time_unit = unit2tex(self.get_unit('Time'))
subtitles = ["t = %s" % (time if time == 0 else
label_quantity(time, time_unit)) for time in times]
start_time, stop_time = self.get_times('Time', [0, -1])
for i, time in enumerate(times):
if time == start_time:
subtitles[i] += " (initial)"
elif time == stop_time:
subtitles[i] += " (final)"
return subtitles
def gen_subtitles_time(self, times):
"""Generate titles for subplots at a list of times.
"""
time_unit = unit2tex(self.get_unit('Time'))
subtitles = [
"t = %s" % (time if time == 0 else label_quantity(time, time_unit))
for time in times
]
start_time, stop_time = self.get_times('Time', [0, -1])
for i, time in enumerate(times):
if time == start_time:
subtitles[i] += " (initial)"
elif time == stop_time:
subtitles[i] += " (final)"
return subtitles
def quiverfig_subregions(self,
vect,
times=[0],
n_rows=1,
title="",
subtitles=None,
label="quiver",
slice_axis='z',
slice_index=0,
xlabel=None,
xticklabels=None,
ylabel=None,
yticklabels=None,
margin_left=rcParams['figure.subplot.left'],
margin_right=1 - rcParams['figure.subplot.right'],
margin_bottom=rcParams['figure.subplot.bottom'],
margin_top=1 - rcParams['figure.subplot.top'],
wspace=0.1,
hspace=0.25,
**kwargs):
"""Create a figure with 2D vector data at given time(s) as arrows in 2D
Cartesian coordinates.
**Arguments:**
- *vect*: Name of the vector to be plotted
*vect* should contain "%i" where the vector indices should be
inserted; the vector will be indexed according to the choice of
*slice_axis* (see below). *vect* will be appended to the names of
the subregions (separated by '.') in order to determine the full
names of the vectors.
- *times*: List of times at which the data should be sampled
If multiple times are given, then subfigures will be generated.
- *n_rows*: Number of rows of (sub)plots
- *title*: Title for the figure
- *subtitles*: List of subtitles (i.e., titles for each subplot)
If not provided, "t = *xx* s" will be used, where *xx* is the
time of each entry. "(initial)" or "(final)" is appended if
appropriate.
- *label*: Label for the figure
This will be used as a base filename if the figure is saved.
- *slice_axis*: Axis normal to the screen or page ('x', 'y', or 'z')
- *slice_index*: Position along *slice_axis*
- *xlabel*: Label for the x-axes (only shown for the subplots on the
bottom row)
If *xlabel* is *None*, then the axis is labeled with "X Axis",
"Y Axis", or "Z Axis" (as appropriate).
- *xticklabels*: Labels for the x-axis ticks
X-axis ticks are only shown for the subplots on the bottom row.
If *xticklabels* is *None*, then the tick labels are "1", "2", ...
- *ylabel*: Label for the y axis (only shown for the subplots on the
left column)
If *ylabel* is *None*, then the axis is labeled with "X Axis",
"Y Axis", or "Z Axis" (as appropriate).
- *yticklabels*: Labels for the y-axis ticks
Y-axis ticks are only shown for the subplots on the left column.
If *None*, then the tick labels are "1", "2", ...
- *margin_left*: Left margin
- *margin_right*: Right margin
- *margin_bottom*: Bottom margin
- *margin_top*: Top margin
- *wspace*: The amount of width reserved for blank space between
subplots
- *hspace*: The amount of height reserved for white space between
subplots
- *\*\*kwargs*: Additional arguments for :meth:`modelicares.base.quiver` (and
thus to :meth:`matplotlib.pyplot.quiver`)
"""
# The quiver() scaling is static and manual; therefore, it shouldn't be
# necessary to coordinate the scaling among subplots like it is for
# colorfig().
# TODO 10/31/11:
# 1) Move the dots and the pivots to the centers of masses of the
# subregions.
# 2) Use and show a grid that is in scale with the geometry of the
# subregions (create a separate method for this it so that it can be
# used for colorfig too; x and y scales would need to be different).
# 3) Overlay the velocities at the faces with the velocities of the
# subregions (by calling this method again with hold on?).
from res import quiver
# Change the default quiver color.
color = kwargs.pop('color', 'b')
# Slice the subregions and retrieve the data.
(us, vs), (n_x, n_y), unit, blanks = self.get_vector_comps2D(
slice_axis, slice_index, vect, times)
# Generate xlabel, xticks, and xticklabels.
if xlabel is None:
xlabel = ('y-axis index' if slice_axis == 'x' else
'z-axis index' if slice_axis == 'y' else 'x-axis index')
if xticklabels is None:
xticklabels = [str(i + 1) for i in range(n_x)]
xticks = range(n_x)
#xticks = [0,1,2,10] # TODO temp
# Generate ylabel, yticks, and yticklabels.
if ylabel is None:
ylabel = ('z-axis index' if slice_axis == 'x' else
'x-axis index' if slice_axis == 'y' else 'y-axis index')
if yticklabels is None:
yticklabels = [str(i + 1) for i in range(n_y)]
yticks = range(n_y)
# Set up the subplots.
n_plots = len(times) # Number of plots
if not subtitles:
subtitles = self.gen_subtitles_time(times)
axs, n_cols = setup_subplots(n_plots=n_plots,
n_rows=n_rows,
title=title,
subtitles=subtitles,
label=label,
xlabel=xlabel,
xticklabels=xticklabels,
xticks=xticks,
ylabel=ylabel,
yticklabels=yticklabels,
yticks=yticks,
margin_left=margin_left,
margin_right=margin_right,
margin_bottom=margin_bottom,
margin_top=margin_top,
wspace=wspace,
hspace=hspace)
# Create the plots.
# scale = 1e-6 # 1 um/s; Zero may cause trouble wth quiverkey() TODO ?
quivers = []
scale = 0
for ax, u, v in zip(axs, us, vs):
quivers.append(quiver(ax, u, v, pad=2 * 0.5, color=color,
**kwargs))
# TODO test moving the locations
#quivers.append(quiver(ax, x = [0,1,2,10], y = [0], u=u, v=v,
# **kwargs))
# Add dots at the pivots.
for i_x in range(n_x):
for i_y in range(n_y):
ax.plot(i_x,
i_y,
marker='o',
color='k',
markerfacecolor='w' if blanks[i_x][i_y] else 'k')
# Find the maximum magnitude of the data.
scale = max(scale, np.amax(np.abs(u)), np.amax(np.abs(v)))
# TODO: Hack: For some reason, the last plot is badly scaled. Update
# it to match the one before it.
if n_plots > 1:
axs[-1].axis(axs[-2].axis())
# Add the key.
# Round the scale to one significant digit.
pow10 = get_pow10(scale)
scale = np.round(scale / 10**pow10) * 10**pow10
axs[n_cols - 1].quiverkey(Q=quivers[n_cols - 1],
X=1,
Y=1.15,
U=scale,
label=label_quantity(scale, unit),
labelpos='W')
# Label the layers.
self.label_layers(axs[len(axs) - n_cols:])
def annotate_conditions(conditions,
temp=True,
composition=True,
pressure=True,
humidity=True,
flow=True):
r"""Create a description of the operating conditions (e.g., to be used as a
subtitle).
**Arguments:**
- *conditions*: Dictionary of operating conditions
The dictionary may include the keys *T*, *p_an_out*, *p_ca_out*,
*n_O2*, *p_an_out*, *p_ca_out*, *T_an_in*, *T_ca_in*, *anInletRH*,
*caInletRH*, *anStoich*, and *caStoich*. Any entry is missing, it will
not be exclued. Each entry is a tuple of (number, unit string) that
describes a condition.
- *temp*: *True*, if temperature should be included in the description
- *composition*: *True*, if composition should be included in the
description
- *pressure*: *True*, if pressure should be included in the description
- *humidity*: *True*, if humidity should be included in the description
- *flow*: *True*, if the flow conditions (stoichiometry) should be included
in the description
**Example:**
>>> conditions = dict(T = (60, 'degC'),
... p = (150, 'kPa'),
... n_O2 = (21, '%'),
... anInletRH = (80, '%'),
... caInletRH = (50, '%'),
... anStoich = (1.5, '1'),
... caStoich = (2.0, '1'))
>>> annotate_conditions(conditions)
'60$\\,^{\\circ}\\!C$, 150$\\,kPa$; An|Ca: $H_2$|Air, 80|50$\\!$$\\,\\%$$\\,$RH, 1.5|2.0$\\,$stoich'
"""
# Main description
main_desc = []
if temp:
try:
main_desc += [label_quantity(*conditions['T'])]
temp = False
if 'T_an_in' in conditions:
print("T_an_in will be ignored.")
if 'T_ca_in' in conditions:
print("T_ca_in will be ignored.")
except KeyError:
pass
if pressure:
try:
main_desc += [label_quantity(*conditions['p'])]
pressure = False
if 'p_an_out' in conditions:
print("p_an_out will be ignored.")
if 'p_ca_out' in conditions:
print("p_ca_out will be ignored.")
except KeyError:
pass
main_desc = ", ".join(main_desc)
# Anode/cathode description
anca_desc = []
if flow:
try:
anca_desc += [
'%s|%sstoich' %
(label_quantity(conditions['anStoich'][0], format='%.1f'),
label_quantity(*conditions['caStoich'], format='%.1f'))
]
except KeyError:
pass
if composition:
try:
if conditions['n_O2'][0] == 1.0:
anca_desc += ['$H_2$|$O_2$']
else:
anca_desc += ['$H_2$|Air']
except KeyError:
pass
if temp:
try:
anca_desc += [
'%s|%s' %
(label_quantity(conditions['T_an_in'][0], format='%.0f'),
label_quantity(*conditions['T_ca_in'], format='%.0f'))
]
except KeyError:
pass
if pressure:
try:
anca_desc += [
'%s|%s' %
(label_quantity(conditions['p_an_out'][0], format='%.1f'),
label_quantity(*conditions['p_ca_out'], format='%.1f'))
]
except KeyError:
pass
if humidity:
try:
anca_desc += [
'%s|%s$\,$RH' %
(label_quantity(conditions['anInletRH'][0], format='%.0f'),
label_quantity(*conditions['caInletRH'], format='%.0f'))
]
except KeyError:
pass
anca_desc = ", ".join(anca_desc)
if anca_desc:
anca_desc = "An|Ca: " + anca_desc
# Finish.
if not anca_desc:
return main_desc
if not main_desc:
return anca_desc
return main_desc + "; " + anca_desc
def colorfig(self,
suffix,
times=[0],
n_rows=1,
title="",
subtitles=[],
label="color",
slice_axis='z',
slice_index=0,
xlabel="",
xticklabels=[],
xticks=[],
ylabel="",
yticklabels=[],
yticks=[],
clabel="",
cbar_orientation='vertical',
margin_left=rcParams['figure.subplot.left'],
margin_right=1 - rcParams['figure.subplot.right'],
margin_bottom=rcParams['figure.subplot.bottom'],
margin_top=1 - rcParams['figure.subplot.top'],
margin_cbar=0.2,
wspace=0.1,
hspace=0.25,
cbar_space=0.1,
cbar_width=0.05,
**kwargs):
"""Create a figure with 2D scalar data at given time(s) on a color axis
in 2D Cartesian coordinates.
**Arguments:**
- *suffix*: Name of the variable to be plotted (relative to the names
of the subregions)
- *times*: List of times at which the data should be sampled
If multiple times are given, then subfigures will be generated.
- *n_rows*: Number of rows of (sub)plots
- *title*: Title for the figure
- *subtitles*: List of subtitles (i.e., titles for each subplot)
If not provided, "t = xx s" will be used, where xx is the time of
each entry. "(initial)" or "(final)" are appended if appropriate.
- *label*: Label for the figure
This will be used as a base filename if the figure is saved.
- *slice_axis*: Axis normal to the screen or page ('x', 'y', or 'z')
- *slice_index*: Position along slice_axis
- *xlabel*: Label for the x-axes (only shown for the subplots on the
bottom row)
- *xticklabels*: Labels for the x-axis ticks (only shown for the
subplots on the bottom row)
- *xticks*: Positions of the x-axis ticks
- *ylabel*: Label for the y axis (only shown for the subplots on the
left column)
- *yticklabels*: Labels for the y-axis ticks (only shown for the
subplots on the left column)
- *yticks*: Positions of the y-axis ticks
- *clabel*: Label for the color- or c-bar axis
- *cbar_orientation*: Orientation of the colorbar ("vertical" or
"horizontal")
- *margin_left*: Left margin
- *margin_right*: Right margin (ignored if ``cbar_orientation ==
'vertical'``)
- *margin_bottom*: Bottom margin (ignored if ``cbar_orientation ==
'horizontal'``)
- *margin_top*: Top margin
- *margin_cbar*: Margin reserved for the colorbar (right margin if
``cbar_orientation == 'vertical'`` and bottom margin if
``cbar_orientation == 'horizontal'``)
- *wspace*: The amount of width reserved for blank space between
subplots
- *hspace*: The amount of height reserved for white space between
subplots
- *cbar_space*: Space between the subplot rectangles and the colorbar
- *cbar_width*: Width of the colorbar if vertical (or height if
horizontal)
- *\*\*kwargs*: Additional arguments for :meth:`modelicares.base.color`
"""
# The procedure for coordinating the images with a single colorbar was
# copied and modified from
# http://matplotlib.sourceforge.net/examples/pylab_examples/multi_image.html,
# accessed 11/8/10.
# 5/26/11: TODO: This method needs to be updated. Use quiverfig() as a
# reference.
from res import color
# Get the data.
n_plots = len(times)
if slice_axis == 'x':
names = presuffix(self.subregions[slice_index], suffix=suffix)
elif slice_axis == 'y':
names = presuffix(self.subregions[:][slice_index], suffix=suffix)
else:
names = presuffix(self.subregions[:][:][slice_index],
suffix=suffix)
c = self.get_values_at_times(names, times)
# Cast the data into a list of matrices (one at each sample time).
if slice_axis == 'x':
c = [
np.array([[
c[i_y + self.n_y * i_z][i]
for i_y in range(self.n_y - 1, -1, -1)
] for i_z in range(self.n_z)]) for i in range(n_plots)
]
elif slice_axis == 'y':
c = [
np.array([[
c[i_z + self.n_z * i_x][i]
for i_z in range(self.n_z - 1, -1, -1)
] for i_x in range(self.n_x)]) for i in range(n_plots)
]
else:
c = [
np.array([[
c[i_y + self.n_y * i_x][i]
for i_z in range(self.n_y - 1, -1, -1)
] for i_x in range(self.n_x)]) for i in range(n_plots)
]
[start_time, stop_time] = self.get_times('Time', [0, -1])
# Generate xlabel, xticks, and xticklabels.
if not xlabel:
if slice_axis == 'x':
xlabel = 'z-axis index'
elif slice_axis == 'y':
xlabel = 'x-axis index'
elif slice_axis == 'z':
xlabel = 'y-axis index'
if not xticklabels:
if slice_axis == 'x':
xticklabels = [str(i + 1) for i in range(self.n_z)]
elif slice_axis == 'y':
xticklabels = [str(i + 1) for i in range(self.n_x)]
else:
xticklabels = [str(i + 1) for i in range(self.n_x)]
if not xticks:
if slice_axis == 'x':
xticks = range(self.n_z)
elif slice_axis == 'y':
xticks = range(self.n_x)
else:
xticks = range(self.n_x)
# Generate ylabel, yticks, and yticklabels.
if not ylabel:
if slice_axis == 'x':
ylabel = 'y-axis index'
elif slice_axis == 'y':
ylabel = 'z-axis index'
else:
ylabel = 'x-axis index'
if not yticklabels:
if slice_axis == 'x':
yticklabels = [str(i + 1) for i in range(self.n_y)]
elif slice_axis == 'y':
yticklabels = [str(i + 1) for i in range(self.n_z)]
else:
yticklabels = [str(i + 1) for i in range(self.n_y)]
if not yticks:
if slice_axis == 'x':
yticks = range(self.n_y)
elif slice_axis == 'y':
yticks = range(self.n_z)
else:
yticks = range(self.n_y)
# Generate clabel.
if not clabel:
clabels = self.get_description(names)
# If all of the descriptions are the same, use the first one.
if len(set(clabels)) == 1:
clabel = clabels[0]
else:
clabel = "Value"
#units = self.get_unit(names)
units = self.get_unit(names)
if len(set(units)) == 1:
clabel += label_number("", units[0])
else:
raise UserWarning(
"The variables have inconsistent units. The "
"colorbar unit will not match the units of all of the "
"variables.")
# Set up the subplots.
if not subtitles:
#unit = unit2tex(self.get_unit('Time'))
unit = unit2tex(self.get_unit('Time'))
subtitles = [
"t = " + label_quantity(times[i], unit) for i in n_plots
]
for i, time in enumerate(times):
if time == start_time:
subtitle += " (initial)"
elif time == stop_time:
subtitle += " (final)"
ax, cax = setup_subplots(n_plots=n_plots,
n_rows=n_rows,
title=title,
subtitles=subtitles,
label=label,
xlabel=xlabel,
xticklabels=xticklabels,
xticks=xticks,
ylabel=ylabel,
yticklabels=yticklabels,
yticks=yticks,
ctype=cbar_orientation,
clabel=clabel,
margin_left=margin_left,
margin_right=margin_right,
margin_bottom=margin_bottom,
margin_top=margin_top,
margin_cbar=margin_cbar,
wspace=wspace,
hspace=hspace,
cbar_space=cbar_space,
cbar_width=cbar_width)
# Create the plots.
profiles = []
c_min = np.inf
c_max = -np.inf
for i, time in enumerate(times):
profiles.append(color(ax[i], c[i], **kwargs))
# Find the minimum and maximum of the color-axis data.
c_min = min(c_min, np.amin(c[i]))
c_max = max(c_max, np.amax(c[i]))
# Set the first image as the master, with all the others observing it
# for changes in norm.
class ImageFollower:
"""Update image in response to changes in clim on another image.
"""
def __init__(self, follower):
self.follower = follower
def __call__(self, leader):
self.follower.set_clim(leader.get_clim())
norm = Normalize(c_min=c_min, c_max=c_max)
for i, profile in enumerate(profiles):
profile.set_norm(norm)
if i > 0:
profiles[0].callbacksSM.connect('changed',
ImageFollower(profile))
# Add the colorbar. (It is also based on the master image.)
cbar = fig.colorbar(images[0], cax, orientation=cbar_orientation)
# Scale the colorbar.
if c_min == c_max:
yticks = [c_min]
for i in range(len(cbar.ax.get_yticks()) - 1):
yticks.append(None)
cbar.set_ticks(yticks)
def quiverfig_subregions(self, vect, times=[0], n_rows=1,
title="", subtitles=None, label="quiver",
slice_axis='z', slice_index=0,
xlabel=None, xticklabels=None,
ylabel=None, yticklabels=None,
margin_left=rcParams['figure.subplot.left'],
margin_right=1-rcParams['figure.subplot.right'],
margin_bottom=rcParams['figure.subplot.bottom'],
margin_top=1-rcParams['figure.subplot.top'],
wspace=0.1, hspace=0.25,
**kwargs):
"""Create a figure with 2D vector data at given time(s) as arrows in 2D
Cartesian coordinates.
**Arguments:**
- *vect*: Name of the vector to be plotted
*vect* should contain "%i" where the vector indices should be
inserted; the vector will be indexed according to the choice of
*slice_axis* (see below). *vect* will be appended to the names of
the subregions (separated by '.') in order to determine the full
names of the vectors.
- *times*: List of times at which the data should be sampled
If multiple times are given, then subfigures will be generated.
- *n_rows*: Number of rows of (sub)plots
- *title*: Title for the figure
- *subtitles*: List of subtitles (i.e., titles for each subplot)
If not provided, "t = *xx* s" will be used, where *xx* is the
time of each entry. "(initial)" or "(final)" is appended if
appropriate.
- *label*: Label for the figure
This will be used as a base filename if the figure is saved.
- *slice_axis*: Axis normal to the screen or page ('x', 'y', or 'z')
- *slice_index*: Position along *slice_axis*
- *xlabel*: Label for the x-axes (only shown for the subplots on the
bottom row)
If *xlabel* is *None*, then the axis is labeled with "X Axis",
"Y Axis", or "Z Axis" (as appropriate).
- *xticklabels*: Labels for the x-axis ticks
X-axis ticks are only shown for the subplots on the bottom row.
If *xticklabels* is *None*, then the tick labels are "1", "2", ...
- *ylabel*: Label for the y axis (only shown for the subplots on the
left column)
If *ylabel* is *None*, then the axis is labeled with "X Axis",
"Y Axis", or "Z Axis" (as appropriate).
- *yticklabels*: Labels for the y-axis ticks
Y-axis ticks are only shown for the subplots on the left column.
If *None*, then the tick labels are "1", "2", ...
- *margin_left*: Left margin
- *margin_right*: Right margin
- *margin_bottom*: Bottom margin
- *margin_top*: Top margin
- *wspace*: The amount of width reserved for blank space between
subplots
- *hspace*: The amount of height reserved for white space between
subplots
- *\*\*kwargs*: Additional arguments for :meth:`modelicares.base.quiver` (and
thus to :meth:`matplotlib.pyplot.quiver`)
"""
# The quiver() scaling is static and manual; therefore, it shouldn't be
# necessary to coordinate the scaling among subplots like it is for
# colorfig().
# TODO 10/31/11:
# 1) Move the dots and the pivots to the centers of masses of the
# subregions.
# 2) Use and show a grid that is in scale with the geometry of the
# subregions (create a separate method for this it so that it can be
# used for colorfig too; x and y scales would need to be different).
# 3) Overlay the velocities at the faces with the velocities of the
# subregions (by calling this method again with hold on?).
from res import quiver
# Change the default quiver color.
color = kwargs.pop('color', 'b')
# Slice the subregions and retrieve the data.
(us, vs), (n_x, n_y), unit, blanks = self.get_vector_comps2D(
slice_axis, slice_index, vect, times)
# Generate xlabel, xticks, and xticklabels.
if xlabel is None:
xlabel = ('y-axis index' if slice_axis == 'x' else 'z-axis index'
if slice_axis == 'y' else 'x-axis index')
if xticklabels is None:
xticklabels = [str(i+1) for i in range(n_x)]
xticks = range(n_x)
#xticks = [0,1,2,10] # TODO temp
# Generate ylabel, yticks, and yticklabels.
if ylabel is None:
ylabel = ('z-axis index' if slice_axis == 'x' else 'x-axis index'
if slice_axis == 'y' else 'y-axis index')
if yticklabels is None:
yticklabels = [str(i+1) for i in range(n_y)]
yticks = range(n_y)
# Set up the subplots.
n_plots = len(times) # Number of plots
if not subtitles:
subtitles = self.gen_subtitles_time(times)
axs, n_cols = setup_subplots(n_plots=n_plots, n_rows=n_rows,
title=title, subtitles=subtitles, label=label,
xlabel=xlabel, xticklabels=xticklabels, xticks=xticks,
ylabel=ylabel, yticklabels=yticklabels, yticks=yticks,
margin_left=margin_left, margin_right=margin_right,
margin_bottom=margin_bottom, margin_top=margin_top,
wspace=wspace, hspace=hspace)
# Create the plots.
# scale = 1e-6 # 1 um/s; Zero may cause trouble wth quiverkey() TODO ?
quivers = []
scale = 0
for ax, u, v in zip(axs, us, vs):
quivers.append(quiver(ax, u, v, pad=2*0.5, color=color, **kwargs))
# TODO test moving the locations
#quivers.append(quiver(ax, x = [0,1,2,10], y = [0], u=u, v=v,
# **kwargs))
# Add dots at the pivots.
for i_x in range(n_x):
for i_y in range(n_y):
ax.plot(i_x, i_y, marker='o', color='k',
markerfacecolor='w' if blanks[i_x][i_y] else 'k')
# Find the maximum magnitude of the data.
scale = max(scale, np.amax(np.abs(u)), np.amax(np.abs(v)))
# TODO: Hack: For some reason, the last plot is badly scaled. Update
# it to match the one before it.
if n_plots > 1:
axs[-1].axis(axs[-2].axis())
# Add the key.
# Round the scale to one significant digit.
pow10 = get_pow10(scale)
scale = np.round(scale/10**pow10)*10**pow10
axs[n_cols-1].quiverkey(Q=quivers[n_cols-1], X=1, Y=1.15, U=scale,
label=label_quantity(scale, unit),
labelpos='W')
# Label the layers.
self.label_layers(axs[len(axs)-n_cols:])
def annotate_conditions(conditions, temp=True, composition=True,
pressure=True, humidity=True, flow=True):
r"""Create a description of the operating conditions (e.g., to be used as a
subtitle).
**Arguments:**
- *conditions*: Dictionary of operating conditions
The dictionary may include the keys *T*, *p_an_out*, *p_ca_out*,
*n_O2*, *p_an_out*, *p_ca_out*, *T_an_in*, *T_ca_in*, *anInletRH*,
*caInletRH*, *anStoich*, and *caStoich*. Any entry is missing, it will
not be exclued. Each entry is a tuple of (number, unit string) that
describes a condition.
- *temp*: *True*, if temperature should be included in the description
- *composition*: *True*, if composition should be included in the
description
- *pressure*: *True*, if pressure should be included in the description
- *humidity*: *True*, if humidity should be included in the description
- *flow*: *True*, if the flow conditions (stoichiometry) should be included
in the description
**Example:**
>>> conditions = dict(T = (60, 'degC'),
... p = (150, 'kPa'),
... n_O2 = (21, '%'),
... anInletRH = (80, '%'),
... caInletRH = (50, '%'),
... anStoich = (1.5, '1'),
... caStoich = (2.0, '1'))
>>> annotate_conditions(conditions)
'60$\\,^{\\circ}\\!C$, 150$\\,kPa$; An|Ca: $H_2$|Air, 80|50$\\!$$\\,\\%$$\\,$RH, 1.5|2.0$\\,$stoich'
"""
# Main description
main_desc = []
if temp:
try:
main_desc += [label_quantity(*conditions['T'])]
temp = False
if 'T_an_in' in conditions:
print("T_an_in will be ignored.")
if 'T_ca_in' in conditions:
print("T_ca_in will be ignored.")
except KeyError:
pass
if pressure:
try:
main_desc += [label_quantity(*conditions['p'])]
pressure = False
if 'p_an_out' in conditions:
print("p_an_out will be ignored.")
if 'p_ca_out' in conditions:
print("p_ca_out will be ignored.")
except KeyError:
pass
main_desc = ", ".join(main_desc)
# Anode/cathode description
anca_desc = []
if flow:
try:
anca_desc += ['%s|%sstoich' % (label_quantity(conditions['anStoich'][0],
format='%.1f'),
label_quantity(*conditions['caStoich'],
format='%.1f'))]
except KeyError:
pass
if composition:
try:
if conditions['n_O2'][0] == 1.0:
anca_desc += ['$H_2$|$O_2$']
else:
anca_desc += ['$H_2$|Air']
except KeyError:
pass
if temp:
try:
anca_desc += ['%s|%s' % (label_quantity(conditions['T_an_in'][0],
format='%.0f'),
label_quantity(*conditions['T_ca_in'],
format='%.0f'))]
except KeyError:
pass
if pressure:
try:
anca_desc += ['%s|%s' % (label_quantity(conditions['p_an_out'][0],
format='%.1f'),
label_quantity(*conditions['p_ca_out'],
format='%.1f'))]
except KeyError:
pass
if humidity:
try:
anca_desc += ['%s|%s$\,$RH' % (label_quantity(conditions['anInletRH'][0],
format='%.0f'),
label_quantity(*conditions['caInletRH'],
format='%.0f'))]
except KeyError:
pass
anca_desc = ", ".join(anca_desc)
if anca_desc:
anca_desc = "An|Ca: " + anca_desc
# Finish.
if not anca_desc:
return main_desc
if not main_desc:
return anca_desc
return main_desc + "; " + anca_desc
def colorfig(self, suffix, times=[0], n_rows=1, title="", subtitles=[],
label="color", slice_axis='z', slice_index=0,
xlabel="", xticklabels=[], xticks=[],
ylabel="", yticklabels=[], yticks=[],
clabel="", cbar_orientation='vertical',
margin_left=rcParams['figure.subplot.left'],
margin_right=1-rcParams['figure.subplot.right'],
margin_bottom=rcParams['figure.subplot.bottom'],
margin_top=1-rcParams['figure.subplot.top'],
margin_cbar=0.2,
wspace=0.1, hspace=0.25,
cbar_space=0.1, cbar_width=0.05,
**kwargs):
"""Create a figure with 2D scalar data at given time(s) on a color axis
in 2D Cartesian coordinates.
**Arguments:**
- *suffix*: Name of the variable to be plotted (relative to the names
of the subregions)
- *times*: List of times at which the data should be sampled
If multiple times are given, then subfigures will be generated.
- *n_rows*: Number of rows of (sub)plots
- *title*: Title for the figure
- *subtitles*: List of subtitles (i.e., titles for each subplot)
If not provided, "t = xx s" will be used, where xx is the time of
each entry. "(initial)" or "(final)" are appended if appropriate.
- *label*: Label for the figure
This will be used as a base filename if the figure is saved.
- *slice_axis*: Axis normal to the screen or page ('x', 'y', or 'z')
- *slice_index*: Position along slice_axis
- *xlabel*: Label for the x-axes (only shown for the subplots on the
bottom row)
- *xticklabels*: Labels for the x-axis ticks (only shown for the
subplots on the bottom row)
- *xticks*: Positions of the x-axis ticks
- *ylabel*: Label for the y axis (only shown for the subplots on the
left column)
- *yticklabels*: Labels for the y-axis ticks (only shown for the
subplots on the left column)
- *yticks*: Positions of the y-axis ticks
- *clabel*: Label for the color- or c-bar axis
- *cbar_orientation*: Orientation of the colorbar ("vertical" or
"horizontal")
- *margin_left*: Left margin
- *margin_right*: Right margin (ignored if ``cbar_orientation ==
'vertical'``)
- *margin_bottom*: Bottom margin (ignored if ``cbar_orientation ==
'horizontal'``)
- *margin_top*: Top margin
- *margin_cbar*: Margin reserved for the colorbar (right margin if
``cbar_orientation == 'vertical'`` and bottom margin if
``cbar_orientation == 'horizontal'``)
- *wspace*: The amount of width reserved for blank space between
subplots
- *hspace*: The amount of height reserved for white space between
subplots
- *cbar_space*: Space between the subplot rectangles and the colorbar
- *cbar_width*: Width of the colorbar if vertical (or height if
horizontal)
- *\*\*kwargs*: Additional arguments for :meth:`modelicares.base.color`
"""
# The procedure for coordinating the images with a single colorbar was
# copied and modified from
# http://matplotlib.sourceforge.net/examples/pylab_examples/multi_image.html,
# accessed 11/8/10.
# 5/26/11: TODO: This method needs to be updated. Use quiverfig() as a
# reference.
from res import color
# Get the data.
n_plots = len(times)
if slice_axis == 'x':
names = presuffix(self.subregions[slice_index], suffix=suffix)
elif slice_axis == 'y':
names = presuffix(self.subregions[:][slice_index], suffix=suffix)
else:
names = presuffix(self.subregions[:][:][slice_index],
suffix=suffix)
c = self.get_values_at_times(names, times)
# Cast the data into a list of matrices (one at each sample time).
if slice_axis == 'x':
c = [np.array([[c[i_y + self.n_y*i_z][i]
for i_y in range(self.n_y-1,-1,-1)]
for i_z in range(self.n_z)]) for i in range(n_plots)]
elif slice_axis == 'y':
c = [np.array([[c[i_z + self.n_z*i_x][i]
for i_z in range(self.n_z-1,-1,-1)]
for i_x in range(self.n_x)]) for i in range(n_plots)]
else:
c = [np.array([[c[i_y + self.n_y*i_x][i]
for i_z in range(self.n_y-1,-1,-1)]
for i_x in range(self.n_x)]) for i in range(n_plots)]
[start_time, stop_time] = self.get_times('Time', [0,-1])
# Generate xlabel, xticks, and xticklabels.
if not xlabel:
if slice_axis == 'x':
xlabel = 'z-axis index'
elif slice_axis == 'y':
xlabel = 'x-axis index'
elif slice_axis == 'z':
xlabel = 'y-axis index'
if not xticklabels:
if slice_axis == 'x':
xticklabels = [str(i+1) for i in range(self.n_z)]
elif slice_axis == 'y':
xticklabels = [str(i+1) for i in range(self.n_x)]
else:
xticklabels = [str(i+1) for i in range(self.n_x)]
if not xticks:
if slice_axis == 'x':
xticks = range(self.n_z)
elif slice_axis == 'y':
xticks = range(self.n_x)
else:
xticks = range(self.n_x)
# Generate ylabel, yticks, and yticklabels.
if not ylabel:
if slice_axis == 'x':
ylabel = 'y-axis index'
elif slice_axis == 'y':
ylabel = 'z-axis index'
else:
ylabel = 'x-axis index'
if not yticklabels:
if slice_axis == 'x':
yticklabels = [str(i+1) for i in range(self.n_y)]
elif slice_axis == 'y':
yticklabels = [str(i+1) for i in range(self.n_z)]
else:
yticklabels = [str(i+1) for i in range(self.n_y)]
if not yticks:
if slice_axis == 'x':
yticks = range(self.n_y)
elif slice_axis == 'y':
yticks = range(self.n_z)
else:
yticks = range(self.n_y)
# Generate clabel.
if not clabel:
clabels = self.get_description(names)
# If all of the descriptions are the same, use the first one.
if len(set(clabels)) == 1:
clabel = clabels[0]
else:
clabel = "Value"
#units = self.get_unit(names)
units = self.get_unit(names)
if len(set(units)) == 1:
clabel += label_number("", units[0])
else:
raise UserWarning("The variables have inconsistent units. The "
"colorbar unit will not match the units of all of the "
"variables.")
# Set up the subplots.
if not subtitles:
#unit = unit2tex(self.get_unit('Time'))
unit = unit2tex(self.get_unit('Time'))
subtitles = ["t = " + label_quantity(times[i], unit)
for i in n_plots]
for i, time in enumerate(times):
if time == start_time:
subtitle += " (initial)"
elif time == stop_time:
subtitle += " (final)"
ax, cax = setup_subplots(n_plots=n_plots, n_rows=n_rows,
title=title, subtitles=subtitles, label=label,
xlabel=xlabel, xticklabels=xticklabels, xticks=xticks,
ylabel=ylabel, yticklabels=yticklabels, yticks=yticks,
ctype=cbar_orientation, clabel=clabel,
margin_left=margin_left, margin_right=margin_right,
margin_bottom=margin_bottom, margin_top=margin_top,
margin_cbar=margin_cbar, wspace=wspace, hspace=hspace,
cbar_space=cbar_space, cbar_width=cbar_width)
# Create the plots.
profiles = []
c_min = np.inf
c_max = -np.inf
for i, time in enumerate(times):
profiles.append(color(ax[i], c[i], **kwargs))
# Find the minimum and maximum of the color-axis data.
c_min = min(c_min, np.amin(c[i]))
c_max = max(c_max, np.amax(c[i]))
# Set the first image as the master, with all the others observing it
# for changes in norm.
class ImageFollower:
"""Update image in response to changes in clim on another image.
"""
def __init__(self, follower):
self.follower = follower
def __call__(self, leader):
self.follower.set_clim(leader.get_clim())
norm = Normalize(c_min=c_min, c_max=c_max)
for i, profile in enumerate(profiles):
profile.set_norm(norm)
if i > 0:
profiles[0].callbacksSM.connect('changed',
ImageFollower(profile))
# Add the colorbar. (It is also based on the master image.)
cbar = fig.colorbar(images[0], cax, orientation=cbar_orientation)
# Scale the colorbar.
if c_min == c_max:
yticks = [c_min]
for i in range(len(cbar.ax.get_yticks()) - 1):
yticks.append(None)
cbar.set_ticks(yticks)
