def plotPixels(self, xVals, images, title):
figure = matplotlib.figure.Figure((6, 4), dpi = 100,
facecolor = (1, 1, 1))
axes = figure.add_subplot(1, 1, 1)
axes.set_axis_bgcolor('white')
axes.set_title(title)
axes.set_ylabel('Individual pixel value')
axes.set_xlabel('Exposure time (ms)')
# Plot pixels from the centers of each quadrant as well as from
# the center of the image and the average of the image as a whole.
lines = []
labels = []
for cam in range(images.shape[1]):
camImages = images[:, cam]
height, width = camImages[0].shape
cy = height / 2
cx = width / 2
for color, xOffset, yOffset in [('r', -1, -1), ('g', -1, 1),
('b', 1, 1), ('c', 1, -1), ('m', 0, 0)]:
y = cy + yOffset * height / 4
x = cx + xOffset * width / 4
lines.append(axes.plot(xVals, camImages[:, y, x], color))
labels.append("Pixel at %d, %d for cam %d" % (x, y, cam))
lines.append(axes.plot(xVals, map(numpy.mean, camImages), 'k'))
labels.append("Average for cam %d" % cam)
figure.legend(lines, labels, loc = 'upper left')
frame = wx.Frame(None, title = "Linearity plot")
canvas = matplotlib.backends.backend_wxagg.FigureCanvasWxAgg(
frame, -1, figure)
canvas.draw()
# Big enough to be visible, but unlikely to run off the edge of
# the screen.
frame.SetSize((640, 480))
frame.Show()
horizontalalignment='center',
size='small')
figure.text(0.5,
0.1,
'Normalized X coordinate',
horizontalalignment='center',
size='small')
figure.text(0.06,
0.5,
'Ice Speed (m/a)',
rotation='vertical',
verticalalignment='center')
# Create the (three column) legend
prop = matplotlib.font_manager.FontProperties(size='x-small')
Line2D = matplotlib.lines.Line2D([], [], color=(0, 0, 0))
figure.legend([Line2D], ['Model Output'], loc=(0.1, 0.05),
prop=prop).draw_frame(False)
Line2D.set_linestyle(':')
Line2D.set_color((1, 0, 0))
Patch = matplotlib.patches.Patch(edgecolor=None,
facecolor=(1, 0, 0),
alpha=0.25)
figure.legend([Line2D, Patch],
['Full Stokes Mean', 'Full Stokes Std. Dev.'],
loc=(0.3, 0.02),
prop=prop).draw_frame(False)
Line2D.set_color((0, 0, 1))
Patch.set_facecolor((0, 0, 1))
figure.legend(
[Line2D, Patch],
[nonFSmodelType + ' Mean', nonFSmodelType + ' Std. Dev.'],
loc=(0.55, 0.02),
def _create_line(plots, labels, plot_info):
"""\
Given all the data for the metrics, create a line plot.
plots: list of dicts containing the plot data. Each dict contains:
x: list of x-values for the plot
y: list of corresponding y-values
errors: errors for each data point, or None if no error information
available
label: plot title
labels: list of x-tick labels
plot_info: a MetricsPlot
"""
# when we're doing any kind of normalization, all series get put into a
# single plot
single = bool(plot_info.normalize_to)
area_data = []
lines = []
if single:
plot_height = _SINGLE_PLOT_HEIGHT
else:
plot_height = _MULTIPLE_PLOT_HEIGHT_PER_PLOT * len(plots)
figure, height = _create_figure(plot_height)
if single:
subplot = figure.add_subplot(1, 1, 1)
# Plot all the data
for plot_index, (plot, color) in enumerate(zip(plots,
_colors(len(plots)))):
needs_invert = (plot['label'] in plot_info.inverted_series)
# Add a new subplot, if user wants multiple subplots
# Also handle axis inversion for subplots here
if not single:
subplot = figure.add_subplot(len(plots), 1, plot_index + 1)
subplot.set_title(plot['label'])
if needs_invert:
# for separate plots, just invert the y-axis
subplot.set_ylim(1, 0)
elif needs_invert:
# for a shared plot (normalized data), need to invert the y values
# manually, since all plots share a y-axis
plot['y'] = [-y for y in plot['y']]
# Plot the series
subplot.set_xticks(range(0, len(labels)))
subplot.set_xlim(-1, len(labels))
if single:
lines += subplot.plot(plot['x'],
plot['y'],
label=plot['label'],
marker=_MULTIPLE_PLOT_MARKER_TYPE,
markersize=_MULTIPLE_PLOT_MARKER_SIZE)
error_bar_color = lines[-1].get_color()
else:
lines += subplot.plot(plot['x'],
plot['y'],
_SINGLE_PLOT_STYLE,
label=plot['label'])
error_bar_color = _SINGLE_PLOT_ERROR_BAR_COLOR
if plot['errors']:
subplot.errorbar(plot['x'],
plot['y'],
linestyle='None',
yerr=plot['errors'],
color=error_bar_color)
subplot.set_xticklabels([])
# Construct the information for the drilldowns.
# We need to do this in a separate loop so that all the data is in
# matplotlib before we start calling transform(); otherwise, it will return
# incorrect data because it hasn't finished adjusting axis limits.
for line in lines:
# Get the pixel coordinates of each point on the figure
x = line.get_xdata()
y = line.get_ydata()
label = line.get_label()
icoords = line.get_transform().transform(zip(x, y))
# Get the appropriate drilldown query
drill = plot_info.query_dict['__' + label + '__']
# Set the title attributes (hover-over tool-tips)
x_labels = [labels[x_val] for x_val in x]
titles = [
'%s - %s: %f' % (label, x_label, y_val)
for x_label, y_val in zip(x_labels, y)
]
# Get the appropriate parameters for the drilldown query
params = [
dict(query=drill, series=line.get_label(), param=x_label)
for x_label in x_labels
]
area_data += [
dict(left=ix - 5,
top=height - iy - 5,
right=ix + 5,
bottom=height - iy + 5,
title=title,
callback=plot_info.drilldown_callback,
callback_arguments=param_dict)
for (ix, iy), title, param_dict in zip(icoords, titles, params)
]
subplot.set_xticklabels(labels, rotation=90, size=_LINE_XTICK_LABELS_SIZE)
# Show the legend if there are not multiple subplots
if single:
font_properties = matplotlib.font_manager.FontProperties(
size=_LEGEND_FONT_SIZE)
legend = figure.legend(lines, [plot['label'] for plot in plots],
prop=font_properties,
handlelen=_LEGEND_HANDLE_LENGTH,
numpoints=_LEGEND_NUM_POINTS)
# Workaround for matplotlib not keeping all line markers in the legend -
# it seems if we don't do this, matplotlib won't keep all the line
# markers in the legend.
for line in legend.get_lines():
line.set_marker(_LEGEND_MARKER_TYPE)
return (figure, area_data)
def _create_bar(plots, labels, plot_info):
"""\
Given all the data for the metrics, create a line plot.
plots: list of dicts containing the plot data.
x: list of x-values for the plot
y: list of corresponding y-values
errors: errors for each data point, or None if no error information
available
label: plot title
labels: list of x-tick labels
plot_info: a MetricsPlot
"""
area_data = []
bars = []
figure, height = _create_figure(_SINGLE_PLOT_HEIGHT)
# Set up the plot
subplot = figure.add_subplot(1, 1, 1)
subplot.set_xticks(range(0, len(labels)))
subplot.set_xlim(-1, len(labels))
subplot.set_xticklabels(labels, rotation=90, size=_BAR_XTICK_LABELS_SIZE)
# draw a bold line at y=0, making it easier to tell if bars are dipping
# below the axis or not.
subplot.axhline(linewidth=2, color='black')
# width here is the width for each bar in the plot. Matplotlib default is
# 0.8.
width = 0.8 / len(plots)
# Plot the data
for plot_index, (plot, color) in enumerate(zip(plots,
_colors(len(plots)))):
# Invert the y-axis if needed
if plot['label'] in plot_info.inverted_series:
plot['y'] = [-y for y in plot['y']]
adjusted_x = _get_adjusted_bar(plot['x'], width, plot_index + 1,
len(plots))
bar_data = subplot.bar(adjusted_x,
plot['y'],
width=width,
yerr=plot['errors'],
facecolor=color,
label=plot['label'])
bars.append(bar_data[0])
# Construct the information for the drilldowns.
# See comment in _create_line for why we need a separate loop to do this.
for plot_index, plot in enumerate(plots):
adjusted_x = _get_adjusted_bar(plot['x'], width, plot_index + 1,
len(plots))
# Let matplotlib plot the data, so that we can get the data-to-image
# coordinate transforms
line = subplot.plot(adjusted_x, plot['y'], linestyle='None')[0]
label = plot['label']
upper_left_coords = line.get_transform().transform(
zip(adjusted_x, plot['y']))
bottom_right_coords = line.get_transform().transform([
(x + width, 0) for x in adjusted_x
])
# Get the drilldown query
drill = plot_info.query_dict['__' + label + '__']
# Set the title attributes
x_labels = [labels[x] for x in plot['x']]
titles = [
'%s - %s: %f' % (plot['label'], label, y)
for label, y in zip(x_labels, plot['y'])
]
params = [
dict(query=drill, series=plot['label'], param=x_label)
for x_label in x_labels
]
area_data += [
dict(left=ulx,
top=height - uly,
right=brx,
bottom=height - bry,
title=title,
callback=plot_info.drilldown_callback,
callback_arguments=param_dict)
for (ulx, uly), (brx, bry), title, param_dict in zip(
upper_left_coords, bottom_right_coords, titles, params)
]
figure.legend(bars, [plot['label'] for plot in plots])
return (figure, area_data)
def _create_line(plots, labels, plot_info):
"""
Given all the data for the metrics, create a line plot.
plots: list of dicts containing the plot data. Each dict contains:
x: list of x-values for the plot
y: list of corresponding y-values
errors: errors for each data point, or None if no error information
available
label: plot title
labels: list of x-tick labels
plot_info: a MetricsPlot
"""
# when we're doing any kind of normalization, all series get put into a
# single plot
single = bool(plot_info.normalize_to)
area_data = []
lines = []
if single:
plot_height = _SINGLE_PLOT_HEIGHT
else:
plot_height = _MULTIPLE_PLOT_HEIGHT_PER_PLOT * len(plots)
figure, height = _create_figure(plot_height)
if single:
subplot = figure.add_subplot(1, 1, 1)
# Plot all the data
for plot_index, (plot, color) in enumerate(zip(plots, _colors(len(plots)))):
needs_invert = (plot['label'] in plot_info.inverted_series)
# Add a new subplot, if user wants multiple subplots
# Also handle axis inversion for subplots here
if not single:
subplot = figure.add_subplot(len(plots), 1, plot_index + 1)
subplot.set_title(plot['label'])
if needs_invert:
# for separate plots, just invert the y-axis
subplot.set_ylim(1, 0)
elif needs_invert:
# for a shared plot (normalized data), need to invert the y values
# manually, since all plots share a y-axis
plot['y'] = [-y for y in plot['y']]
# Plot the series
subplot.set_xticks(range(0, len(labels)))
subplot.set_xlim(-1, len(labels))
if single:
lines += subplot.plot(plot['x'], plot['y'], label=plot['label'],
marker=_MULTIPLE_PLOT_MARKER_TYPE,
markersize=_MULTIPLE_PLOT_MARKER_SIZE)
error_bar_color = lines[-1].get_color()
else:
lines += subplot.plot(plot['x'], plot['y'], _SINGLE_PLOT_STYLE,
label=plot['label'])
error_bar_color = _SINGLE_PLOT_ERROR_BAR_COLOR
if plot['errors']:
subplot.errorbar(plot['x'], plot['y'], linestyle='None',
yerr=plot['errors'], color=error_bar_color)
subplot.set_xticklabels([])
# Construct the information for the drilldowns.
# We need to do this in a separate loop so that all the data is in
# matplotlib before we start calling transform(); otherwise, it will return
# incorrect data because it hasn't finished adjusting axis limits.
for line in lines:
# Get the pixel coordinates of each point on the figure
x = line.get_xdata()
y = line.get_ydata()
label = line.get_label()
icoords = line.get_transform().transform(zip(x, y))
# Get the appropriate drilldown query
drill = plot_info.query_dict['__' + label + '__']
# Set the title attributes (hover-over tool-tips)
x_labels = [labels[x_val] for x_val in x]
titles = ['%s - %s: %f' % (label, x_label, y_val)
for x_label, y_val in zip(x_labels, y)]
# Get the appropriate parameters for the drilldown query
params = [dict(query=drill, series=line.get_label(), param=x_label)
for x_label in x_labels]
area_data += [dict(left=ix - 5, top=height - iy - 5,
right=ix + 5, bottom=height - iy + 5,
title=title,
callback=plot_info.drilldown_callback,
callback_arguments=param_dict)
for (ix, iy), title, param_dict
in zip(icoords, titles, params)]
subplot.set_xticklabels(labels, rotation=90, size=_LINE_XTICK_LABELS_SIZE)
# Show the legend if there are not multiple subplots
if single:
font_properties = matplotlib.font_manager.FontProperties(
size=_LEGEND_FONT_SIZE)
legend = figure.legend(lines, [plot['label'] for plot in plots],
prop=font_properties,
handlelen=_LEGEND_HANDLE_LENGTH,
numpoints=_LEGEND_NUM_POINTS)
# Workaround for matplotlib not keeping all line markers in the legend -
# it seems if we don't do this, matplotlib won't keep all the line
# markers in the legend.
for line in legend.get_lines():
line.set_marker(_LEGEND_MARKER_TYPE)
return (figure, area_data)
def _create_bar(plots, labels, plot_info):
"""
Given all the data for the metrics, create a line plot.
plots: list of dicts containing the plot data.
x: list of x-values for the plot
y: list of corresponding y-values
errors: errors for each data point, or None if no error information
available
label: plot title
labels: list of x-tick labels
plot_info: a MetricsPlot
"""
area_data = []
bars = []
figure, height = _create_figure(_SINGLE_PLOT_HEIGHT)
# Set up the plot
subplot = figure.add_subplot(1, 1, 1)
subplot.set_xticks(range(0, len(labels)))
subplot.set_xlim(-1, len(labels))
subplot.set_xticklabels(labels, rotation=90, size=_BAR_XTICK_LABELS_SIZE)
# draw a bold line at y=0, making it easier to tell if bars are dipping
# below the axis or not.
subplot.axhline(linewidth=2, color='black')
# width here is the width for each bar in the plot. Matplotlib default is
# 0.8.
width = 0.8 / len(plots)
# Plot the data
for plot_index, (plot, color) in enumerate(zip(plots, _colors(len(plots)))):
# Invert the y-axis if needed
if plot['label'] in plot_info.inverted_series:
plot['y'] = [-y for y in plot['y']]
adjusted_x = _get_adjusted_bar(plot['x'], width, plot_index + 1,
len(plots))
bar_data = subplot.bar(adjusted_x, plot['y'],
width=width, yerr=plot['errors'],
facecolor=color,
label=plot['label'])
bars.append(bar_data[0])
# Construct the information for the drilldowns.
# See comment in _create_line for why we need a separate loop to do this.
for plot_index, plot in enumerate(plots):
adjusted_x = _get_adjusted_bar(plot['x'], width, plot_index + 1,
len(plots))
# Let matplotlib plot the data, so that we can get the data-to-image
# coordinate transforms
line = subplot.plot(adjusted_x, plot['y'], linestyle='None')[0]
label = plot['label']
upper_left_coords = line.get_transform().transform(zip(adjusted_x,
plot['y']))
bottom_right_coords = line.get_transform().transform(
[(x + width, 0) for x in adjusted_x])
# Get the drilldown query
drill = plot_info.query_dict['__' + label + '__']
# Set the title attributes
x_labels = [labels[x] for x in plot['x']]
titles = ['%s - %s: %f' % (plot['label'], label, y)
for label, y in zip(x_labels, plot['y'])]
params = [dict(query=drill, series=plot['label'], param=x_label)
for x_label in x_labels]
area_data += [dict(left=ulx, top=height - uly,
right=brx, bottom=height - bry,
title=title,
callback=plot_info.drilldown_callback,
callback_arguments=param_dict)
for (ulx, uly), (brx, bry), title, param_dict
in zip(upper_left_coords, bottom_right_coords, titles,
params)]
figure.legend(bars, [plot['label'] for plot in plots])
return (figure, area_data)
# Loop over the experiments requested on the command line
for experiment in options.experiments:
print 'ISMIP-HOM', experiment.upper()
# Create the figure on which the plot axes will be placed
figure = pyplot.figure(subplotpars=matplotlib.figure.SubplotParams(top=.85,bottom=.15))
figure.text(0.5,0.92,'ISMIP-HOM Experiment '+experiment.upper(),horizontalalignment='center',size='large')
if options.subtitle:
figure.text(0.5,0.89,options.subtitle,horizontalalignment='center',size='small')
figure.text(0.5,0.1,'Normalized X coordinate',horizontalalignment='center',size='small')
figure.text(0.06,0.5,'Ice Speed (m/a)',rotation='vertical',verticalalignment='center')
# Create the (three column) legend
prop = matplotlib.font_manager.FontProperties(size='x-small')
Line2D = matplotlib.lines.Line2D([],[],color=(0,0,0))
figure.legend([Line2D],['Model Output'],loc=(0.1,0.05),prop=prop).draw_frame(False)
Line2D.set_linestyle(':')
Line2D.set_color((1,0,0))
Patch = matplotlib.patches.Patch(edgecolor=None,facecolor=(1,0,0),alpha=0.25)
figure.legend([Line2D,Patch],['Full Stokes Mean','Full Stokes Std. Dev.'],loc=(0.3,0.02),prop=prop).draw_frame(False)
Line2D.set_color((0,0,1))
Patch.set_facecolor((0,0,1))
figure.legend([Line2D,Patch],[nonFSmodelType+' Mean',nonFSmodelType+' Std. Dev.'],loc=(0.55,0.02),prop=prop).draw_frame(False)
# Loop over the sizes requested on the command line
for i, size in enumerate(map(int,options.sizes)):
try:
if experiment == 'f':
if size != 100 or len(options.sizes) > 1:
print 'NOTE: Experiment f uses a domain size of 100 km only'
def main():
"""
Plot the ISMIP-HOM test results.
"""
if args.all_ps:
nonFSmodelType = 'All Partial Stokes'
else:
nonFSmodelType = 'First Order'
print 'NOTE: The category being used for models approximating Full Stokes is: ' + nonFSmodelType
print 'For more information, see details of option -a by invoking: python plotISMIP_HOM.py --help \n'
# =========================================================
# First generate the standard ascii files defined by ISMIP-HOM
# (This used to be in the run script, but is more useful and natural here.)
# Note: It is not strictly necessary to generate these files
# just to plot the results, but this approach is left in
# to use existing code, and on the off-chance it is ever
# necessary to generate these standard ascii ISMIP-HOM files...
# =========================================================
pattern = get_file_pattern()
experiments, sizes = get_experiments_and_sizes(pattern)
if args.sizes and (set(args.sizes) <= set(map(int, sizes))):
sizes = args.sizes
elif args.sizes:
print("ERROR: No output files found for that size!")
print(
" Run `ls " + pattern.replace('-?', '-[a-e]') +
"`\non the command line to see the set of output files. (Note, test f's size is ignored.)"
)
sys.exit(1)
# sort experiments and sizes
sizes.sort()
experiments.sort()
# Loop over the experiments
for experiment in experiments:
# Loop over the sizes
for size in map(int, sizes):
try:
# Extract the output data for comparison to the other models
# NOTE: The script now assumes that uvel_extend and vvel_extend are ALWAYS present.
# Those fields contain the ice velocity computed at the upper right corner of each grid cell.
# They appear to be on the x1,y1 grid in their metadata but are actually on the x0,y0 grid.
# The additional row/column include the first halo value past ewn/nsn.
# That value is valid at both 0.0 and 1.0 on the non-dimensional coordinate system.
# Matrix manipulations for each test case below are done to create a larger matrix that goes from 0.0 to 1.0, inclusive.
# NOTE: The cases below are only writing [x,y,u,v] to the text file. This is the minimum needed to compare to other models.
# In the future, the other additional fields specified in section 4 of http://homepages.ulb.ac.be/~fpattyn/ismip/ismiphom.pdf
# can be added. wvel and the stresses are on the x1,y1 grid, so they would need to be interpolated to the x0,y0 grid
# since we are using that as the coordinate system in the text files.
# Open the netCDF file that was written by CISM
# Standard filename format used by both scripts
if experiment == 'f':
size = 100
out_file = pattern.replace('-?', '-' + experiment).replace(
'????',
str(size).zfill(4))
netCDFfile = NetCDFFile(out_file, 'r')
if netCDF_module == 'Scientific.IO.NetCDF':
velscale = netCDFfile.variables['uvel_extend'].scale_factor
else:
velscale = 1.0
if experiment in [
'f',
]:
# Convert CISM output data to the rotated coord system used by the problem setup
alpha = -3.0 * pi / 180 # defined in run script
if netCDF_module == 'Scientific.IO.NetCDF':
thkscale = netCDFfile.variables['thk'].scale_factor
wvelscale = netCDFfile.variables[
'wvel_ho'].scale_factor
else:
thkscale = 1.0
wvelscale = 1.0
usurf = netCDFfile.variables['usurf'][
-1, :, :] * thkscale # get last time level
usurfStag = (usurf[1:, 1:] + usurf[1:, :-1] +
usurf[:-1, :-1] + usurf[:-1, :-1]) / 4.0
uvelS = netCDFfile.variables['uvel'][
-1, 0, :, :] * velscale # top level of last time
vvelS = netCDFfile.variables['vvel'][
-1, 0, :, :] * velscale # top level of last time
wvelS = netCDFfile.variables['wvel_ho'][
-1, 0, :, :] * wvelscale # top level of last time
wvelStag = (wvelS[1:, 1:] + wvelS[1:, :-1] +
wvelS[:-1, :-1] + wvelS[:-1, :-1]) / 4.0
x0 = netCDFfile.variables['x0'][:]
y0 = netCDFfile.variables['y0'][:]
# calculate rotated xprime coordinates along the surface - xx, yy are used by code below to write the output
xx = x0 * cos(alpha) + (usurfStag[20, :] -
7000.0) * sin(alpha)
xx = xx / 1000.0 - 50.0
yy = y0 / 1000.0 - 50.0
# calculate rotated uvel/vvel at surface
uvelSprime = uvelS[:, :] * cos(
alpha) + wvelStag[:, :] * sin(alpha)
wvelSprime = -uvelS[:, :] * sin(
alpha) + wvelStag[:, :] * cos(alpha)
nan = np.ones(
uvelSprime.shape
) * -999.0 # create a dummy matrix for uncalculated values.
# ===========================================
# optional bit of code to plot out vertical velocity on the rotated grid.
# This can be compared to Fig. 14b in the tc-2007-0019-sp3.pdf document
figure = pyplot.figure(subplotpars=matplotlib.figure.
SubplotParams(top=.85, bottom=.15))
axes = figure.add_subplot(111)
pc = axes.pcolor(wvelSprime)
pyplot.colorbar(pc)
cntr = axes.contour(wvelSprime, [
-0.5, -0.4, -0.3, -0.2, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4
],
colors='k')
axes.clabel(cntr)
pyplot.title('Compare to Fig. 14b of tc-2007-0019-sp3.pdf')
pyplot.draw()
pyplot.show()
# ===========================================
else: # all other tests
# Make x,y position arrays that can be used by all test cases.
# Want x/y positions to include the periodic edge at both the beginning and end
xx = netCDFfile.variables['x0'][:] / (1000.0 * float(size))
xx = np.concatenate(([0.0], xx, [1.0]))
yy = netCDFfile.variables['y0'][:] / (1000.0 * float(size))
yy = np.concatenate(([0.0], yy, [1.0]))
if experiment in ('b', 'd'):
yy = (
yy[len(yy) / 2],
) # for the 2-d experiments, just use the middle y-index
# Figure out u,v since all experiments needs at least one of them (avoids duplicate code in each case below
# Want to use last time level. Most experiments should only have a single time level, but F may have many in the file.
# Apparently some older versions of netCDF4 give an error when using the -1 dimension if the size is 1, hence this bit of seemingly unnecessary logic...
if netCDFfile.variables['uvel_extend'][:].shape[0] == 1:
t = 0
else:
t = -1
us = netCDFfile.variables['uvel_extend'][
t, 0, :, :] * velscale # top level of last time
us = np.concatenate(
(us[:, -1:], us),
axis=1) # copy the column at x=1.0 to x=0.0
us = np.concatenate(
(us[-1:, :], us),
axis=0) # copy the row at y=1.0 to y=0.0
vs = netCDFfile.variables['vvel_extend'][
t, 0, :, :] * velscale # top level of last time
vs = np.concatenate(
(vs[:, -1:], vs),
axis=1) # copy the column at x=1.0 to x=0.0
vs = np.concatenate(
(vs[-1:, :], vs),
axis=0) # copy the row at y=1.0 to y=0.0
ub = netCDFfile.variables['uvel_extend'][
t, -1, :, :] * velscale # bottom level of last time
ub = np.concatenate(
(ub[:, -1:], ub),
axis=1) # copy the column at x=1.0 to x=0.0
ub = np.concatenate(
(ub[-1:, :], ub),
axis=0) # copy the row at y=1.0 to y=0.0
vb = netCDFfile.variables['vvel_extend'][
t, -1, :, :] * velscale # bottom level of last time
vb = np.concatenate(
(vb[:, -1:], vb),
axis=1) # copy the column at x=1.0 to x=0.0
vb = np.concatenate(
(vb[-1:, :], vb),
axis=0) # copy the row at y=1.0 to y=0.0
#nan = ub*np.NaN # create a dummy matrix for uncalculated values.
nan = np.ones(
ub.shape
) * -999.0 # create a dummy matrix for uncalculated values.
# make arrays of the variables needed for each experiment
# the extended-grid velocities have the periodic edge in the last x-position. We also want it in the first x-position.
# After building the 2-d array as needed for each variable from the raw file data, then build a list called 'data'.
if experiment == 'a':
# This is supposed to be: [('uvel',0),('vvel',0),('wvel',0),('tau_xz',-1),('tau_yz',-1),[deltap]]
data = (us, vs, nan, nan, nan, nan)
elif experiment == 'b':
# This is supposed to be: uvel(0), wvel(0), tau_xz(-1), deltaP
data = (us, nan, nan, nan)
elif experiment == 'c':
# This is supposed to be: [uvel',0),('vvel',0),('wvel',0),('uvel',-1),('vvel',-1),('tau_xz',-1),('tau_yz',-1), deltap]
data = (us, vs, nan, ub, vb, nan, nan, nan)
elif experiment == 'd':
# This is supposed to be: [('uvel',0),('wvel',0),('uvel',-1),('tau_xz',-1), deltap]
data = (us, nan, nan, nan, nan)
elif experiment == 'f': # should be: x, y, zs, vx, vy, vz
# This is supposed to be: [('usurf',None),('uvel',0),('vvel',0),('wvel',0)]
data = (nan, uvelSprime, vvelS, nan)
# Write a "standard" ISMIP-HOM file (example file name: "cis1a020.txt") in the "output" subdirectory
ISMIP_HOMfilename = out_file.replace('.out.nc', '.txt')
ISMIP_HOMfile = open(ISMIP_HOMfilename, 'w')
for i, x in enumerate(xx):
for j, y in enumerate(yy):
if experiment in ('a', 'c',
'f'): # include x and y positions
ISMIP_HOMfile.write('\t'.join(
map(str, [x, y] + [v[j, i]
for (v) in data])) + '\n')
else: # only include x position
ISMIP_HOMfile.write('\t'.join(
map(str, [x] + [v[j, i]
for (v) in data])) + '\n')
ISMIP_HOMfile.close()
netCDFfile.close()
except:
print 'Warning: The CISM output file for experiment ' + experiment + ' at size ' + str(
size) + ' could NOT be read successfully!'
raise
# =========================================================
# Now actually analyze the results.
# =========================================================
# Loop over the experiments requested on the command line
for experiment in experiments:
print 'ISMIP-HOM', experiment.upper()
# Create the figure on which the plot axes will be placed
figure = pyplot.figure(
subplotpars=matplotlib.figure.SubplotParams(top=.85, bottom=.15))
figure.text(0.5,
0.92,
'ISMIP-HOM Experiment ' + experiment.upper(),
horizontalalignment='center',
size='large')
if args.subtitle:
figure.text(0.5,
0.89,
args.subtitle,
horizontalalignment='center',
size='small')
figure.text(0.5,
0.1,
'Normalized X coordinate',
horizontalalignment='center',
size='small')
figure.text(0.06,
0.5,
'Ice Speed (m/a)',
rotation='vertical',
verticalalignment='center')
# Create the (three column) legend
prop = matplotlib.font_manager.FontProperties(size='x-small')
Line2D = matplotlib.lines.Line2D([], [], color=(0, 0, 0))
figure.legend([Line2D], ['Model Output'], loc=(0.1, 0.05),
prop=prop).draw_frame(False)
Line2D.set_linestyle(':')
Line2D.set_color((1, 0, 0))
Patch = matplotlib.patches.Patch(edgecolor=None,
facecolor=(1, 0, 0),
alpha=0.25)
figure.legend([Line2D, Patch],
['Full Stokes Mean', 'Full Stokes Std. Dev.'],
loc=(0.3, 0.02),
prop=prop).draw_frame(False)
Line2D.set_color((0, 0, 1))
Patch.set_facecolor((0, 0, 1))
figure.legend(
[Line2D, Patch],
[nonFSmodelType + ' Mean', nonFSmodelType + ' Std. Dev.'],
loc=(0.55, 0.02),
prop=prop).draw_frame(False)
# Loop over the sizes requested on the command line
for i, size in enumerate(map(int, sizes)):
try:
if experiment == 'f':
if size != 100 or len(sizes) > 1:
print 'NOTE: Experiment f uses a domain size of 100 km only'
size = 100
out_file = pattern.replace('-?', '-' + experiment).replace(
'????',
str(size).zfill(4))
# Create the plot axes for this domain size
if len(sizes) == 1:
axes = figure.add_subplot(111)
else:
axes = figure.add_subplot(2, 3, i + 1)
for tick in axes.xaxis.get_major_ticks():
tick.label1.set_fontsize('xx-small')
for tick in axes.yaxis.get_major_ticks():
tick.label1.set_fontsize('xx-small')
axes.set_title('%d km' % size, size='medium')
# Get the Glimmer output data
glimmerData = read(out_file.replace('.out.nc', '.txt'),
experiment)
# The Glimmer data is on a staggered grid;
# Interpolate to obtain the value at x=0 and x=1
# using periodic boundary conditions
#v = (glimmerData[0][1] + glimmerData[-1][1])/2
#glimmerData = [(0.0,v)]+glimmerData+[(1.0,v)]
# Plot the Glimmer data
axes.plot([row[0] for row in glimmerData],
[row[1] for row in glimmerData],
color='black')
# Get the data from other models for comparison
firstOrder = 0
fullStokes = 1
count = [0, 0]
sumV = [[0.0 for v in glimmerData], [0.0 for v in glimmerData]]
sumV2 = [[0.0 for v in glimmerData],
[0.0 for v in glimmerData]]
for (path, directories, filenames) in os.walk('ismip_all'):
for filename in filenames:
modelName = filename[0:4]
modelExperiment = filename[4]
modelSize = filename[5:8]
#print 'name, exp, size', modelName, modelExperiment, modelSize
if modelName == 'aas1':
# Skip the 'aas1' model because its output files in the tc-2007-0019-sp2.zip file do not follow the proper naming convention. MJH 11/5/13
continue
if (modelExperiment != experiment) or (modelExperiment != 'f' and int(modelSize) != size) \
or (not args.all_ps and not modelName in lmlaModels + fullStokesModels):
continue # continue next loop iteration if not the size or not the experiment desired or if we just want FO comparison and this model is not FO or FS.
elif (modelExperiment == 'f'):
if (modelSize == '001'):
continue # ignore the sliding version for now
if modelName == 'cma1':
continue # the cma1 'f' experiments made the x,y coords dimensionless instead of dimensional - ignore for convenience
print 'Using data from file:', os.path.join(
path, filename)
data = read(os.path.join(path, filename), experiment)
if modelName in fullStokesModels:
index = fullStokes
else:
index = firstOrder
count[index] += 1
#axes.plot([row[0] for row in data], [row[1] for row in data] ) ## OPTIONAL: print out every individual model in its native x-coordinates.
# Interpolate onto the x values from the Glimmer model run
for (i, target) in enumerate(
[row[0] for row in glimmerData]):
below = -99999.0
above = 99999.0
for (j, x) in enumerate([row[0] for row in data]):
if below < x <= target: b, below = j, x
if target <= x < above: a, above = j, x
if above == below:
v = data[a][1]
else:
if below == -99999.0: # Use the periodic boundary condition at x = 0
xBelow = data[-1][0] - 1
vBelow = data[-1][1]
else:
xBelow, vBelow = data[b]
if above == 99999.0: # Use the periodic boundary condition at x = 1
xAbove = data[0][0] + 1
vAbove = data[0][1]
else:
xAbove, vAbove = data[a]
if xAbove == xBelow:
print 'Surprise!', above, below, xAbove, xBelow, vAbove, vBelow
v = (vAbove + vBelow) / 2
else:
alpha = (target - xBelow) / (xAbove -
xBelow)
v = alpha * vAbove + (1 - alpha) * vBelow
sumV[index][i] += v
sumV2[index][i] += v * v
# Calculate statistics of the other model results
if sum(count) == 0:
print 'To compare with other models you need to download the ISMIP-HOM results from: http://www.the-cryosphere.net/2/95/2008/tc-2-95-2008-supplement.zip and unzip the contained file tc-2007-0019-sp2.zip into a directory named ismip_all. The ismip_all directory must be in the directory from which you are running this script.'
else:
# Find the mean and standard deviation of the velocities at each x
for index in (firstOrder, fullStokes):
if count[index] == 0:
continue
mean = list()
standardDeviation = list()
for i in range(len(glimmerData)):
mean.append(sumV[index][i] / count[index])
standardDeviation.append(
sqrt(sumV2[index][i] / count[index] -
mean[-1]**2))
# Plot the mean using a dotted line
color = (
index, 0, 1 - index
) # blue for first order (index=0); red for full Stokes (index=1)
x = [row[0] for row in glimmerData]
axes.plot(x, mean, ':', color=color)
# Plot a filled polygon showing the mean plus and minus one standard deviation
meanMinusSD = [
m - sd for (m, sd) in zip(mean, standardDeviation)
]
meanPlusSD = [
m + sd for (m, sd) in zip(mean, standardDeviation)
]
x = x + list(reversed(x))
y = meanPlusSD + list(reversed(meanMinusSD))
axes.fill(x,
y,
facecolor=color,
edgecolor=color,
alpha=0.25)
if index == firstOrder:
# Calculate some statistics comparing the Glimmer data with the other models
pcterror = [
100.0 * abs(glimmer - others) / others
for (glimmer, others
) in zip([row[1]
for row in glimmerData], mean)
]
abserror = [
abs(glimmer - others)
for (glimmer, others
) in zip([row[1]
for row in glimmerData], mean)
]
maximum = max(pcterror)
position = glimmerData[pcterror.index(maximum)][0]
total = sum([e for e in pcterror])
compare = sum([
(s / m)
for (s, m) in zip(standardDeviation, mean)
])
n = len(glimmerData)
#print '\t'.join([str(size)+' km',str(total/n),str(compare/n),str(position)])
print 'Size=' + str(size) + ' km'
print ' Mean percent error along flowline of CISM relative to mean of first-order models=' + str(
total / float(n)) + '%'
print ' Mean COD (stdev/mean) along flowline of mean of first-order models (excluding CISM)=' + str(
compare / float(n) * 100.0) + '%'
print ' Max. CISM percent error=' + str(
maximum) + '% at x-position ' + str(position)
print ' Max. CISM absolute error=' + str(
max(abserror)) + ' m/yr at x-position ' + str(
glimmerData[abserror.index(
max(abserror))][0])
except:
print "Error in analyzing/plotting experiment ", experiment, " at size ", size, " km"
if savePlotInFile:
plot_file = pattern.replace('-?', '-' + experiment).replace(
'.????', '').replace('.out.nc', plotType)
print 'Writing:', plot_file
pyplot.savefig(plot_file)
# Experiment f can also have a surface profile plotted
if experiment == 'f':
# rather than getting the data from the text file, we are going to read it directly.
# this is because the velocities and usrf are on different grids, so it is difficult to include them
# both in the standard ISMIP-HOM text file format that has a single x,y coord. system
size = 100
out_file = pattern.replace('-?', '-' + experiment).replace(
'????',
str(size).zfill(4))
netCDFfile = NetCDFFile(out_file, 'r')
if netCDF_module == 'Scientific.IO.NetCDF':
thkscale = netCDFfile.variables['thk'].scale_factor
else:
thkscale = 1.0
usurf = netCDFfile.variables['usurf'][
-1, :, :] * thkscale # get last time level
x1 = netCDFfile.variables['x1'][:]
y1 = netCDFfile.variables['y1'][:]
# Create the usurf figure
ufigure = pyplot.figure(
subplotpars=matplotlib.figure.SubplotParams(top=.85,
bottom=.15))
ufigure.text(0.5,
0.92,
'ISMIP-HOM Experiment F: Surface elevation',
horizontalalignment='center',
size='large')
if args.subtitle:
ufigure.text(0.5,
0.89,
args.subtitle,
horizontalalignment='center',
size='small')
ufigure.text(0.5,
0.1,
'X coordinate',
horizontalalignment='center',
size='small')
ufigure.text(0.06,
0.5,
'upper surface (m)',
rotation='vertical',
verticalalignment='center')
# Create the (three column) legend
prop = matplotlib.font_manager.FontProperties(size='x-small')
Line2D = matplotlib.lines.Line2D([], [], color=(0, 0, 0))
ufigure.legend([Line2D], ['Model Output'],
loc=(0.1, 0.05),
prop=prop).draw_frame(False)
Line2D.set_linestyle(':')
Line2D.set_color((1, 0, 0))
Patch = matplotlib.patches.Patch(edgecolor=None,
facecolor=(1, 0, 0),
alpha=0.25)
ufigure.legend([Line2D, Patch],
['Full Stokes Mean', 'Full Stokes Std. Dev.'],
loc=(0.3, 0.02),
prop=prop).draw_frame(False)
Line2D.set_color((0, 0, 1))
Patch.set_facecolor((0, 0, 1))
ufigure.legend(
[Line2D, Patch],
[nonFSmodelType + ' Mean', nonFSmodelType + ' Std. Dev.'],
loc=(0.55, 0.02),
prop=prop).draw_frame(False)
# Create the plot axes
axes2 = ufigure.add_subplot(111)
axes2.set_title('%d km' % size, size='medium')
# Convert CISM output data to the rotated coord system used by the problem setup
alpha = -3.0 * pi / 180 # defined in run script
# use integer floor division operator to get an index close to the center TODO should be interpolating if needed...
yp = len(y1) // 2
# calculate rotated xprime, zprime coordinates along the surface (this is the coord. sys. used for this test case)
xprime = x1 * cos(alpha) + (usurf[yp, :] - 7000.0) * sin(alpha)
xprime = xprime / 1000.0 - 50.0
zprime = -x1 * sin(alpha) + (usurf[yp, :] - 7000.0) * cos(alpha)
# Plot CISM output
axes2.plot(xprime, zprime, color='black')
# create glimmerData so we can re-use the code from above
glimmerData = list()
for i in range(len(xprime)):
glimmerData.append(tuple([xprime[i], zprime[i]]))
# Now plot the other models - yucky code copied from above
# Get the data from other models for comparison
firstOrder = 0
fullStokes = 1
count = [0, 0]
sumV = [[0.0 for v in glimmerData], [0.0 for v in glimmerData]]
sumV2 = [[0.0 for v in glimmerData], [0.0 for v in glimmerData]]
for (path, directories, filenames) in os.walk('ismip_all'):
for filename in filenames:
modelName = filename[0:4]
modelExperiment = filename[4]
modelSize = filename[5:8]
#print 'name, exp, size', modelName, modelExperiment, modelSize
if modelName == 'aas1':
# Skip the 'aas1' model because its output files in the tc-2007-0019-sp2.zip file do not follow the proper naming convention. MJH 11/5/13
continue
if (modelExperiment != experiment) or (modelExperiment != 'f' and int(modelSize) != size) \
or (not args.all_ps and not modelName in lmlaModels + fullStokesModels):
continue # continue next loop iteration if not the size or not the experiment desired or if we just want FO comparison and this model is not FO or FS.
elif (modelExperiment == 'f'):
if (modelSize == '001'):
continue # ignore the sliding version for now
if modelName == 'cma1':
continue # the cma1 'f' experiments made the x,y coords dimensionless instead of dimensional - ignore for convenience
print 'Using data from file:', os.path.join(path, filename)
data = read(os.path.join(path, filename),
experiment='f-elevation')
if modelName in fullStokesModels:
index = fullStokes
else:
index = firstOrder
count[index] += 1
#axes2.plot([row[0] for row in data], [row[1] for row in data] ) ## OPTIONAL: print out every individual model in its native x-coordinates.
# Interpolate onto the x values from the Glimmer model run
for (i,
target) in enumerate([row[0] for row in glimmerData]):
below = -99999.0
above = 99999.0
for (j, x) in enumerate([row[0] for row in data]):
if below < x <= target: b, below = j, x
if target <= x < above: a, above = j, x
if above == below:
v = data[a][1]
else:
if below == -99999.0: # Use the periodic boundary condition at x = 0
xBelow = data[-1][0] - 1
vBelow = data[-1][1]
else:
xBelow, vBelow = data[b]
if above == 99999.0: # Use the periodic boundary condition at x = 1
xAbove = data[0][0] + 1
vAbove = data[0][1]
else:
xAbove, vAbove = data[a]
if xAbove == xBelow:
print 'Surprise!', above, below, xAbove, xBelow, vAbove, vBelow
v = (vAbove + vBelow) / 2
else:
alpha = (target - xBelow) / (xAbove - xBelow)
v = alpha * vAbove + (1 - alpha) * vBelow
sumV[index][i] += v
sumV2[index][i] += v * v
# Calculate statistics of the other model results
if sum(count) == 0:
print 'To compare with other models you need to download the ISMIP-HOM results from: http://www.the-cryosphere.net/2/95/2008/tc-2-95-2008-supplement.zip and unzip the contained file tc-2007-0019-sp2.zip into a directory named ismip_all. The ismip_all directory must be in the directory from which you are running this script.'
else:
# Find the mean and standard deviation of the velocities at each x
for index in (firstOrder, fullStokes):
if count[index] == 0:
continue
mean = list()
standardDeviation = list()
for i in range(len(glimmerData)):
mean.append(sumV[index][i] / count[index])
standardDeviation.append(
sqrt(sumV2[index][i] / count[index] -
mean[-1]**2))
# Plot the mean using a dotted line
color = (
index, 0, 1 - index
) # blue for first order (index=0); red for full Stokes (index=1)
x = [row[0] for row in glimmerData]
axes2.plot(x, mean, ':', color=color)
# Plot a filled polygon showing the mean plus and minus one standard deviation
meanMinusSD = [
m - sd
for (m, sd) in zip(mean, standardDeviation)
]
meanPlusSD = [
m + sd
for (m, sd) in zip(mean, standardDeviation)
]
x = x + list(reversed(x))
y = meanPlusSD + list(reversed(meanMinusSD))
axes2.fill(x,
y,
facecolor=color,
edgecolor=color,
alpha=0.25)
if savePlotInFile:
plot_file = pattern.replace(
'-?', '-' + experiment + '-SurfaceElevation').replace(
'.????', '').replace('.out.nc', plotType)
print 'Writing:', plot_file
pyplot.savefig(plot_file)
# Experiment f should be run for one size (100 km) only
if experiment == 'f':
break
if not savePlotInFile:
pyplot.show()
horizontalalignment='center',
size='small')
figure.text(0.5,
0.1,
'Normalized X coordinate',
horizontalalignment='center',
size='small')
figure.text(0.06,
0.5,
'Ice Speed (m/a)',
rotation='vertical',
verticalalignment='center')
# Create the (three column) legend
prop = matplotlib.font_manager.FontProperties(size='x-small')
Line2D = matplotlib.lines.Line2D([], [], color=(0, 0, 0))
figure.legend([Line2D], ['Model Output'], loc=(0.1, 0.05),
prop=prop).draw_frame(False)
Line2D.set_linestyle(':')
Line2D.set_color((1, 0, 0))
Patch = matplotlib.patches.Patch(edgecolor=None,
facecolor=(1, 0, 0),
alpha=0.25)
figure.legend([Line2D, Patch],
['Full Stokes Mean', 'Full Stokes Std. Dev.'],
loc=(0.3, 0.02),
prop=prop).draw_frame(False)
Line2D.set_color((0, 0, 1))
Patch.set_facecolor((0, 0, 1))
figure.legend([Line2D, Patch],
['First Order Mean', 'First Order Std. Dev.'],
loc=(0.55, 0.02),
prop=prop).draw_frame(False)
def main():
"""
Plot the ISMIP-HOM test results.
"""
if args.all_ps:
nonFSmodelType='All Partial Stokes'
else:
nonFSmodelType='First Order'
print 'NOTE: The category being used for models approximating Full Stokes is: '+nonFSmodelType
print 'For more information, see details of option -a by invoking: python plotISMIP_HOM.py --help \n'
# =========================================================
# First generate the standard ascii files defined by ISMIP-HOM
# (This used to be in the run script, but is more useful and natural here.)
# Note: It is not strictly necessary to generate these files
# just to plot the results, but this approach is left in
# to use existing code, and on the off-chance it is ever
# necessary to generate these standard ascii ISMIP-HOM files...
# =========================================================
pattern = get_file_pattern()
experiments, sizes = get_experiments_and_sizes(pattern)
if args.sizes and (set(args.sizes) <= set(map(int,sizes))):
sizes = args.sizes
elif args.sizes:
print("ERROR: No output files found for that size!")
print(" Run `ls "+pattern.replace('-?','-[a-e]')+"`\non the command line to see the set of output files. (Note, test f's size is ignored.)")
sys.exit(1)
# sort experiments and sizes
sizes.sort()
experiments.sort()
# Loop over the experiments
for experiment in experiments:
# Loop over the sizes
for size in map(int,sizes):
try:
# Extract the output data for comparison to the other models
# NOTE: The script now assumes that uvel_extend and vvel_extend are ALWAYS present.
# Those fields contain the ice velocity computed at the upper right corner of each grid cell.
# They appear to be on the x1,y1 grid in their metadata but are actually on the x0,y0 grid.
# The additional row/column include the first halo value past ewn/nsn.
# That value is valid at both 0.0 and 1.0 on the non-dimensional coordinate system.
# Matrix manipulations for each test case below are done to create a larger matrix that goes from 0.0 to 1.0, inclusive.
# NOTE: The cases below are only writing [x,y,u,v] to the text file. This is the minimum needed to compare to other models.
# In the future, the other additional fields specified in section 4 of http://homepages.ulb.ac.be/~fpattyn/ismip/ismiphom.pdf
# can be added. wvel and the stresses are on the x1,y1 grid, so they would need to be interpolated to the x0,y0 grid
# since we are using that as the coordinate system in the text files.
# Open the netCDF file that was written by CISM
# Standard filename format used by both scripts
if experiment == 'f':
size = 100
out_file = pattern.replace('-?','-'+experiment).replace('????',str(size).zfill(4))
netCDFfile = NetCDFFile(out_file,'r')
if netCDF_module == 'Scientific.IO.NetCDF':
velscale = netCDFfile.variables['uvel_extend'].scale_factor
else:
velscale = 1.0
if experiment in ['f',]:
# Convert CISM output data to the rotated coord system used by the problem setup
alpha = -3.0 * pi/180 # defined in run script
if netCDF_module == 'Scientific.IO.NetCDF':
thkscale = netCDFfile.variables['thk'].scale_factor
wvelscale = netCDFfile.variables['wvel_ho'].scale_factor
else:
thkscale = 1.0
wvelscale = 1.0
usurf = netCDFfile.variables['usurf'][-1,:,:] * thkscale # get last time level
usurfStag = (usurf[1:,1:] + usurf[1:,:-1] + usurf[:-1,:-1] + usurf[:-1, :-1]) / 4.0
uvelS = netCDFfile.variables['uvel'][-1,0,:,:] * velscale # top level of last time
vvelS = netCDFfile.variables['vvel'][-1,0,:,:] * velscale # top level of last time
wvelS = netCDFfile.variables['wvel_ho'][-1,0,:,:] * wvelscale # top level of last time
wvelStag = (wvelS[1:,1:] + wvelS[1:,:-1] + wvelS[:-1,:-1] + wvelS[:-1, :-1]) / 4.0
x0 = netCDFfile.variables['x0'][:]
y0 = netCDFfile.variables['y0'][:]
# calculate rotated xprime coordinates along the surface - xx, yy are used by code below to write the output
xx = x0 * cos(alpha) + (usurfStag[20,:]-7000.0) * sin(alpha)
xx = xx/1000.0 - 50.0
yy = y0/1000.0 - 50.0
# calculate rotated uvel/vvel at surface
uvelSprime = uvelS[:,:] * cos(alpha) + wvelStag[:,:] * sin(alpha)
wvelSprime = -uvelS[:,:] * sin(alpha) + wvelStag[:,:] * cos(alpha)
nan = np.ones(uvelSprime.shape)*-999.0 # create a dummy matrix for uncalculated values.
# ===========================================
# optional bit of code to plot out vertical velocity on the rotated grid.
# This can be compared to Fig. 14b in the tc-2007-0019-sp3.pdf document
figure = pyplot.figure(subplotpars=matplotlib.figure.SubplotParams(top=.85,bottom=.15))
axes = figure.add_subplot(111)
pc = axes.pcolor(wvelSprime)
pyplot.colorbar(pc)
cntr=axes.contour(wvelSprime, [-0.5, -0.4, -0.3, -0.2, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4],colors='k')
axes.clabel(cntr)
pyplot.title('Compare to Fig. 14b of tc-2007-0019-sp3.pdf')
pyplot.draw()
pyplot.show()
# ===========================================
else: # all other tests
# Make x,y position arrays that can be used by all test cases.
# Want x/y positions to include the periodic edge at both the beginning and end
xx = netCDFfile.variables['x0'][:]/(1000.0*float(size))
xx = np.concatenate(([0.0],xx,[1.0]))
yy = netCDFfile.variables['y0'][:]/(1000.0*float(size))
yy = np.concatenate(([0.0],yy,[1.0]))
if experiment in ('b','d'):
yy = (yy[len(yy)/2],) # for the 2-d experiments, just use the middle y-index
# Figure out u,v since all experiments needs at least one of them (avoids duplicate code in each case below
# Want to use last time level. Most experiments should only have a single time level, but F may have many in the file.
# Apparently some older versions of netCDF4 give an error when using the -1 dimension if the size is 1, hence this bit of seemingly unnecessary logic...
if netCDFfile.variables['uvel_extend'][:].shape[0] == 1:
t = 0
else:
t = -1
us = netCDFfile.variables['uvel_extend'][t,0,:,:] * velscale # top level of last time
us = np.concatenate( (us[:,-1:], us), axis=1) # copy the column at x=1.0 to x=0.0
us = np.concatenate( (us[-1:,:], us), axis=0) # copy the row at y=1.0 to y=0.0
vs = netCDFfile.variables['vvel_extend'][t,0,:,:] * velscale # top level of last time
vs = np.concatenate( (vs[:,-1:], vs), axis=1) # copy the column at x=1.0 to x=0.0
vs = np.concatenate( (vs[-1:,:], vs), axis=0) # copy the row at y=1.0 to y=0.0
ub = netCDFfile.variables['uvel_extend'][t,-1,:,:] * velscale # bottom level of last time
ub = np.concatenate( (ub[:,-1:], ub), axis=1) # copy the column at x=1.0 to x=0.0
ub = np.concatenate( (ub[-1:,:], ub), axis=0) # copy the row at y=1.0 to y=0.0
vb = netCDFfile.variables['vvel_extend'][t,-1,:,:] * velscale # bottom level of last time
vb = np.concatenate( (vb[:,-1:], vb), axis=1) # copy the column at x=1.0 to x=0.0
vb = np.concatenate( (vb[-1:,:], vb), axis=0) # copy the row at y=1.0 to y=0.0
#nan = ub*np.NaN # create a dummy matrix for uncalculated values.
nan = np.ones(ub.shape)*-999.0 # create a dummy matrix for uncalculated values.
# make arrays of the variables needed for each experiment
# the extended-grid velocities have the periodic edge in the last x-position. We also want it in the first x-position.
# After building the 2-d array as needed for each variable from the raw file data, then build a list called 'data'.
if experiment == 'a':
# This is supposed to be: [('uvel',0),('vvel',0),('wvel',0),('tau_xz',-1),('tau_yz',-1),[deltap]]
data = (us, vs, nan, nan, nan, nan)
elif experiment == 'b':
# This is supposed to be: uvel(0), wvel(0), tau_xz(-1), deltaP
data = (us, nan, nan, nan)
elif experiment == 'c':
# This is supposed to be: [uvel',0),('vvel',0),('wvel',0),('uvel',-1),('vvel',-1),('tau_xz',-1),('tau_yz',-1), deltap]
data = (us, vs, nan, ub, vb, nan, nan, nan)
elif experiment == 'd':
# This is supposed to be: [('uvel',0),('wvel',0),('uvel',-1),('tau_xz',-1), deltap]
data = (us, nan, nan, nan, nan)
elif experiment == 'f': # should be: x, y, zs, vx, vy, vz
# This is supposed to be: [('usurf',None),('uvel',0),('vvel',0),('wvel',0)]
data = (nan, uvelSprime, vvelS, nan)
# Write a "standard" ISMIP-HOM file (example file name: "cis1a020.txt") in the "output" subdirectory
ISMIP_HOMfilename = out_file.replace('.out.nc','.txt')
ISMIP_HOMfile = open(ISMIP_HOMfilename,'w')
for i, x in enumerate(xx):
for j, y in enumerate(yy):
if experiment in ('a','c','f'): # include x and y positions
ISMIP_HOMfile.write('\t'.join(map(str,[x,y]+[v[j,i] for (v) in data]))+'\n')
else: # only include x position
ISMIP_HOMfile.write('\t'.join(map(str,[x]+[v[j,i] for (v) in data]))+'\n')
ISMIP_HOMfile.close()
netCDFfile.close()
except:
print 'Warning: The CISM output file for experiment '+experiment+' at size '+str(size)+' could NOT be read successfully!'
raise
# =========================================================
# Now actually analyze the results.
# =========================================================
# Loop over the experiments requested on the command line
for experiment in experiments:
print 'ISMIP-HOM', experiment.upper()
# Create the figure on which the plot axes will be placed
figure = pyplot.figure(subplotpars=matplotlib.figure.SubplotParams(top=.85,bottom=.15))
figure.text(0.5,0.92,'ISMIP-HOM Experiment '+experiment.upper(),horizontalalignment='center',size='large')
if args.subtitle:
figure.text(0.5,0.89,args.subtitle,horizontalalignment='center',size='small')
figure.text(0.5,0.1,'Normalized X coordinate',horizontalalignment='center',size='small')
figure.text(0.06,0.5,'Ice Speed (m/a)',rotation='vertical',verticalalignment='center')
# Create the (three column) legend
prop = matplotlib.font_manager.FontProperties(size='x-small')
Line2D = matplotlib.lines.Line2D([],[],color=(0,0,0))
figure.legend([Line2D],['Model Output'],loc=(0.1,0.05),prop=prop).draw_frame(False)
Line2D.set_linestyle(':')
Line2D.set_color((1,0,0))
Patch = matplotlib.patches.Patch(edgecolor=None,facecolor=(1,0,0),alpha=0.25)
figure.legend([Line2D,Patch],['Full Stokes Mean','Full Stokes Std. Dev.'],loc=(0.3,0.02),prop=prop).draw_frame(False)
Line2D.set_color((0,0,1))
Patch.set_facecolor((0,0,1))
figure.legend([Line2D,Patch],[nonFSmodelType+' Mean',nonFSmodelType+' Std. Dev.'],loc=(0.55,0.02),prop=prop).draw_frame(False)
# Loop over the sizes requested on the command line
for i, size in enumerate(map(int,sizes)):
try:
if experiment == 'f':
if size != 100 or len(sizes) > 1:
print 'NOTE: Experiment f uses a domain size of 100 km only'
size = 100
out_file = pattern.replace('-?','-'+experiment).replace('????',str(size).zfill(4))
# Create the plot axes for this domain size
if len(sizes) == 1:
axes = figure.add_subplot(111)
else:
axes = figure.add_subplot(2,3,i+1)
for tick in axes.xaxis.get_major_ticks():
tick.label1.set_fontsize('xx-small')
for tick in axes.yaxis.get_major_ticks():
tick.label1.set_fontsize('xx-small')
axes.set_title('%d km' % size, size='medium')
# Get the Glimmer output data
glimmerData = read(out_file.replace('.out.nc','.txt'),experiment)
# The Glimmer data is on a staggered grid;
# Interpolate to obtain the value at x=0 and x=1
# using periodic boundary conditions
#v = (glimmerData[0][1] + glimmerData[-1][1])/2
#glimmerData = [(0.0,v)]+glimmerData+[(1.0,v)]
# Plot the Glimmer data
axes.plot([row[0] for row in glimmerData],
[row[1] for row in glimmerData],color='black')
# Get the data from other models for comparison
firstOrder = 0
fullStokes = 1
count = [0,0]
sumV = [[0.0 for v in glimmerData],[0.0 for v in glimmerData]]
sumV2 = [[0.0 for v in glimmerData],[0.0 for v in glimmerData]]
for (path,directories,filenames) in os.walk('ismip_all'):
for filename in filenames:
modelName = filename[0:4]
modelExperiment = filename[4]
modelSize = filename[5:8]
#print 'name, exp, size', modelName, modelExperiment, modelSize
if modelName == 'aas1':
# Skip the 'aas1' model because its output files in the tc-2007-0019-sp2.zip file do not follow the proper naming convention. MJH 11/5/13
continue
if (modelExperiment != experiment) or (modelExperiment != 'f' and int(modelSize) != size) \
or (not args.all_ps and not modelName in lmlaModels + fullStokesModels):
continue # continue next loop iteration if not the size or not the experiment desired or if we just want FO comparison and this model is not FO or FS.
elif (modelExperiment == 'f'):
if (modelSize == '001'):
continue # ignore the sliding version for now
if modelName == 'cma1':
continue # the cma1 'f' experiments made the x,y coords dimensionless instead of dimensional - ignore for convenience
print 'Using data from file:',os.path.join(path,filename)
data = read(os.path.join(path,filename),experiment)
if modelName in fullStokesModels:
index = fullStokes
else:
index = firstOrder
count[index] += 1
#axes.plot([row[0] for row in data], [row[1] for row in data] ) ## OPTIONAL: print out every individual model in its native x-coordinates.
# Interpolate onto the x values from the Glimmer model run
for (i,target) in enumerate([row[0] for row in glimmerData]):
below = -99999.0
above = 99999.0
for (j,x) in enumerate([row[0] for row in data]):
if below < x <= target: b,below = j,x
if target <= x < above: a,above = j,x
if above == below:
v = data[a][1]
else:
if below == -99999.0: # Use the periodic boundary condition at x = 0
xBelow = data[-1][0] - 1
vBelow = data[-1][1]
else:
xBelow,vBelow = data[b]
if above == 99999.0: # Use the periodic boundary condition at x = 1
xAbove = data[0][0] + 1
vAbove = data[0][1]
else:
xAbove,vAbove = data[a]
if xAbove == xBelow:
print 'Surprise!',above,below,xAbove,xBelow,vAbove,vBelow
v = (vAbove+vBelow)/2
else:
alpha = (target-xBelow)/(xAbove-xBelow)
v = alpha*vAbove + (1-alpha)*vBelow
sumV [index][i] += v
sumV2[index][i] += v*v
# Calculate statistics of the other model results
if sum(count) == 0:
print 'To compare with other models you need to download the ISMIP-HOM results from: http://www.the-cryosphere.net/2/95/2008/tc-2-95-2008-supplement.zip and unzip the contained file tc-2007-0019-sp2.zip into a directory named ismip_all. The ismip_all directory must be in the directory from which you are running this script.'
else:
# Find the mean and standard deviation of the velocities at each x
for index in (firstOrder,fullStokes):
if count[index] == 0:
continue
mean = list()
standardDeviation = list()
for i in range(len(glimmerData)):
mean.append(sumV[index][i]/count[index])
standardDeviation.append(sqrt(sumV2[index][i]/count[index]-mean[-1]**2))
# Plot the mean using a dotted line
color = (index,0,1-index) # blue for first order (index=0); red for full Stokes (index=1)
x = [row[0] for row in glimmerData]
axes.plot(x,mean,':',color=color)
# Plot a filled polygon showing the mean plus and minus one standard deviation
meanMinusSD = [m-sd for (m,sd) in zip(mean,standardDeviation)]
meanPlusSD = [m+sd for (m,sd) in zip(mean,standardDeviation)]
x = x + list(reversed(x))
y = meanPlusSD + list(reversed(meanMinusSD))
axes.fill(x,y,facecolor=color,edgecolor=color,alpha=0.25)
if index == firstOrder:
# Calculate some statistics comparing the Glimmer data with the other models
pcterror = [100.0*abs(glimmer-others)/others for (glimmer,others) in zip([row[1] for row in glimmerData],mean)]
abserror = [abs(glimmer-others) for (glimmer,others) in zip([row[1] for row in glimmerData],mean)]
maximum = max(pcterror)
position = glimmerData[pcterror.index(maximum)][0]
total = sum([e for e in pcterror])
compare = sum([(s/m) for (s,m) in zip(standardDeviation,mean)])
n = len(glimmerData)
#print '\t'.join([str(size)+' km',str(total/n),str(compare/n),str(position)])
print 'Size='+str(size)+' km'
print ' Mean percent error along flowline of CISM relative to mean of first-order models='+str(total/float(n))+'%'
print ' Mean COD (stdev/mean) along flowline of mean of first-order models (excluding CISM)='+str(compare/float(n)*100.0)+'%'
print ' Max. CISM percent error='+str(maximum)+'% at x-position '+str(position)
print ' Max. CISM absolute error='+str(max(abserror))+' m/yr at x-position '+str(glimmerData[abserror.index(max(abserror))][0])
except:
print "Error in analyzing/plotting experiment ",experiment," at size ",size," km"
if savePlotInFile:
plot_file = pattern.replace('-?','-'+experiment).replace('.????','').replace('.out.nc',plotType)
print 'Writing:', plot_file
pyplot.savefig(plot_file)
# Experiment f can also have a surface profile plotted
if experiment == 'f':
# rather than getting the data from the text file, we are going to read it directly.
# this is because the velocities and usrf are on different grids, so it is difficult to include them
# both in the standard ISMIP-HOM text file format that has a single x,y coord. system
size = 100
out_file = pattern.replace('-?','-'+experiment).replace('????',str(size).zfill(4))
netCDFfile = NetCDFFile(out_file,'r')
if netCDF_module == 'Scientific.IO.NetCDF':
thkscale = netCDFfile.variables['thk'].scale_factor
else:
thkscale = 1.0
usurf = netCDFfile.variables['usurf'][-1,:,:] * thkscale # get last time level
x1 = netCDFfile.variables['x1'][:]
y1 = netCDFfile.variables['y1'][:]
# Create the usurf figure
ufigure = pyplot.figure(subplotpars=matplotlib.figure.SubplotParams(top=.85,bottom=.15))
ufigure.text(0.5,0.92,'ISMIP-HOM Experiment F: Surface elevation',horizontalalignment='center',size='large')
if args.subtitle:
ufigure.text(0.5,0.89,args.subtitle,horizontalalignment='center',size='small')
ufigure.text(0.5,0.1,'X coordinate',horizontalalignment='center',size='small')
ufigure.text(0.06,0.5,'upper surface (m)',rotation='vertical',verticalalignment='center')
# Create the (three column) legend
prop = matplotlib.font_manager.FontProperties(size='x-small')
Line2D = matplotlib.lines.Line2D([],[],color=(0,0,0))
ufigure.legend([Line2D],['Model Output'],loc=(0.1,0.05),prop=prop).draw_frame(False)
Line2D.set_linestyle(':')
Line2D.set_color((1,0,0))
Patch = matplotlib.patches.Patch(edgecolor=None,facecolor=(1,0,0),alpha=0.25)
ufigure.legend([Line2D,Patch],['Full Stokes Mean','Full Stokes Std. Dev.'],loc=(0.3,0.02),prop=prop).draw_frame(False)
Line2D.set_color((0,0,1))
Patch.set_facecolor((0,0,1))
ufigure.legend([Line2D,Patch],[nonFSmodelType+' Mean',nonFSmodelType+' Std. Dev.'],loc=(0.55,0.02),prop=prop).draw_frame(False)
# Create the plot axes
axes2 = ufigure.add_subplot(111)
axes2.set_title('%d km' % size, size='medium')
# Convert CISM output data to the rotated coord system used by the problem setup
alpha = -3.0 * pi/180 # defined in run script
# use integer floor division operator to get an index close to the center TODO should be interpolating if needed...
yp = len(y1)//2
# calculate rotated xprime, zprime coordinates along the surface (this is the coord. sys. used for this test case)
xprime = x1 * cos(alpha) + (usurf[yp,:]-7000.0) * sin(alpha)
xprime = xprime/1000.0 - 50.0
zprime = -x1 * sin(alpha) + (usurf[yp,:]-7000.0) * cos(alpha)
# Plot CISM output
axes2.plot(xprime, zprime, color='black')
# create glimmerData so we can re-use the code from above
glimmerData = list()
for i in range(len(xprime)):
glimmerData.append(tuple([xprime[i], zprime[i]]))
# Now plot the other models - yucky code copied from above
# Get the data from other models for comparison
firstOrder = 0
fullStokes = 1
count = [0,0]
sumV = [[0.0 for v in glimmerData],[0.0 for v in glimmerData]]
sumV2 = [[0.0 for v in glimmerData],[0.0 for v in glimmerData]]
for (path,directories,filenames) in os.walk('ismip_all'):
for filename in filenames:
modelName = filename[0:4]
modelExperiment = filename[4]
modelSize = filename[5:8]
#print 'name, exp, size', modelName, modelExperiment, modelSize
if modelName == 'aas1':
# Skip the 'aas1' model because its output files in the tc-2007-0019-sp2.zip file do not follow the proper naming convention. MJH 11/5/13
continue
if (modelExperiment != experiment) or (modelExperiment != 'f' and int(modelSize) != size) \
or (not args.all_ps and not modelName in lmlaModels + fullStokesModels):
continue # continue next loop iteration if not the size or not the experiment desired or if we just want FO comparison and this model is not FO or FS.
elif (modelExperiment == 'f'):
if (modelSize == '001'):
continue # ignore the sliding version for now
if modelName == 'cma1':
continue # the cma1 'f' experiments made the x,y coords dimensionless instead of dimensional - ignore for convenience
print 'Using data from file:',os.path.join(path,filename)
data = read(os.path.join(path,filename), experiment='f-elevation')
if modelName in fullStokesModels:
index = fullStokes
else:
index = firstOrder
count[index] += 1
#axes2.plot([row[0] for row in data], [row[1] for row in data] ) ## OPTIONAL: print out every individual model in its native x-coordinates.
# Interpolate onto the x values from the Glimmer model run
for (i,target) in enumerate([row[0] for row in glimmerData]):
below = -99999.0
above = 99999.0
for (j,x) in enumerate([row[0] for row in data]):
if below < x <= target: b,below = j,x
if target <= x < above: a,above = j,x
if above == below:
v = data[a][1]
else:
if below == -99999.0: # Use the periodic boundary condition at x = 0
xBelow = data[-1][0] - 1
vBelow = data[-1][1]
else:
xBelow,vBelow = data[b]
if above == 99999.0: # Use the periodic boundary condition at x = 1
xAbove = data[0][0] + 1
vAbove = data[0][1]
else:
xAbove,vAbove = data[a]
if xAbove == xBelow:
print 'Surprise!',above,below,xAbove,xBelow,vAbove,vBelow
v = (vAbove+vBelow)/2
else:
alpha = (target-xBelow)/(xAbove-xBelow)
v = alpha*vAbove + (1-alpha)*vBelow
sumV [index][i] += v
sumV2[index][i] += v*v
# Calculate statistics of the other model results
if sum(count) == 0:
print 'To compare with other models you need to download the ISMIP-HOM results from: http://www.the-cryosphere.net/2/95/2008/tc-2-95-2008-supplement.zip and unzip the contained file tc-2007-0019-sp2.zip into a directory named ismip_all. The ismip_all directory must be in the directory from which you are running this script.'
else:
# Find the mean and standard deviation of the velocities at each x
for index in (firstOrder,fullStokes):
if count[index] == 0:
continue
mean = list()
standardDeviation = list()
for i in range(len(glimmerData)):
mean.append(sumV[index][i]/count[index])
standardDeviation.append(sqrt(sumV2[index][i]/count[index]-mean[-1]**2))
# Plot the mean using a dotted line
color = (index,0,1-index) # blue for first order (index=0); red for full Stokes (index=1)
x = [row[0] for row in glimmerData]
axes2.plot(x,mean,':',color=color)
# Plot a filled polygon showing the mean plus and minus one standard deviation
meanMinusSD = [m-sd for (m,sd) in zip(mean,standardDeviation)]
meanPlusSD = [m+sd for (m,sd) in zip(mean,standardDeviation)]
x = x + list(reversed(x))
y = meanPlusSD + list(reversed(meanMinusSD))
axes2.fill(x,y,facecolor=color,edgecolor=color,alpha=0.25)
if savePlotInFile:
plot_file = pattern.replace('-?','-'+experiment+'-SurfaceElevation').replace('.????','').replace('.out.nc',plotType)
print 'Writing:', plot_file
pyplot.savefig(plot_file)
# Experiment f should be run for one size (100 km) only
if experiment == 'f':
break
if not savePlotInFile:
pyplot.show()
# Loop over the experiments requested on the command line
for experiment in options.experiments:
print 'ISMIP-HOM', experiment.upper()
# Create the figure on which the plot axes will be placed
figure = pyplot.figure(subplotpars=matplotlib.figure.SubplotParams(top=.85,bottom=.15))
figure.text(0.5,0.92,'ISMIP-HOM Experiment '+experiment.upper(),horizontalalignment='center',size='large')
if options.subtitle:
figure.text(0.5,0.89,options.subtitle,horizontalalignment='center',size='small')
figure.text(0.5,0.1,'Normalized X coordinate',horizontalalignment='center',size='small')
figure.text(0.06,0.5,'Ice Speed (m/a)',rotation='vertical',verticalalignment='center')
# Create the (three column) legend
prop = matplotlib.font_manager.FontProperties(size='x-small')
Line2D = matplotlib.lines.Line2D([],[],color=(0,0,0))
figure.legend([Line2D],['Model Output'],loc=(0.1,0.05),prop=prop).draw_frame(False)
Line2D.set_linestyle(':')
Line2D.set_color((1,0,0))
Patch = matplotlib.patches.Patch(edgecolor=None,facecolor=(1,0,0),alpha=0.25)
figure.legend([Line2D,Patch],['Full Stokes Mean','Full Stokes Std. Dev.'],loc=(0.3,0.02),prop=prop).draw_frame(False)
Line2D.set_color((0,0,1))
Patch.set_facecolor((0,0,1))
figure.legend([Line2D,Patch],['First Order Mean','First Order Std. Dev.'],loc=(0.55,0.02),prop=prop).draw_frame(False)
# Loop over the sizes requested on the command line
for i, size in enumerate(map(int,options.sizes)):
if experiment == 'f':
if size != 100 or len(options.sizes) > 1:
print 'NOTE: Experiment f uses a domain size of 100 km only'
size = 100
