def plot_slice(filename, start, count, output, loc, name):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
fields = ['b', 'u', 'v', 'w', 'ωz']
scale = 2.5
dpi = 100
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: '{}_{:06}.png'.format(name, write)
# Layout
if loc[0] == 'x':
nrows, ncols = 5, 1
image = plot_tools.Box(6, 1)
image_axes = (2, 3)
data_slices = (0, slice(None), slice(None))
elif loc[0] == 'y':
nrows, ncols = 5, 1
image = plot_tools.Box(6, 1)
image_axes = (1, 3)
data_slices = (slice(None), 0, slice(None))
elif loc[0] == 'z':
nrows, ncols = 2, 3
image = plot_tools.Box(2, 2)
image_axes = (1, 2)
data_slices = (slice(None), slice(None), 0)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start + count):
for n, field in enumerate(fields):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call plotting helper (dset axes: [t, x, y, z])
task = f"interp({field}, {loc})"
dset = file['tasks'][task]
plot_tools.plot_bot(dset,
image_axes, (index, ) + data_slices,
axes=axes,
title=task,
even_scale=True)
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def integral(filename, start, count, output):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
tasks = ['ZF']
scale = 2.5
dpi = 100
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'ZF_{:06}.png'.format(write)
# Layout
nrows, ncols = 1, 1
image = plot_tools.Box(1, 1)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
with h5py.File(filename, mode='r') as file:
time = file['scales']['sim_time']
xaxis = file['scales']['x']['1.0']
dset = file['tasks']['ZF']
f = open('zf_set', 'w')
for j in range(0, len(time)):
for k in range(0, len(xaxis)):
f.write('%f %f %f\n' % (time[j], xaxis[k], dset[j][k][0]))
f.write('\n')
f.close()
return
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start + count):
for n, task in enumerate(tasks):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call plotting helper (dset axes: [t, x, y, z])
dset = file['tasks'][task]
logger.info('dset %s length %s' % (dset, len(dset.shape)))
image_axes = (1, 1)
data_slices = (index, slice(None), slice(None), 0)
plot_tools.plot_bot(dset, (1, 2), (slice(None), slice(None)))
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def integral(filename, start, count, output):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
tasks = ['ZF']
scale = 2.5
dpi = 100
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'ZF_{:06}.png'.format(write)
# Layout
nrows, ncols = 1, 1
image = plot_tools.Box(1, 1)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
with h5py.File(filename, mode='r') as file:
time = file['scales']['sim_time']
xaxis = file['scales']['x']['1.0']
dset = file['tasks']['ZF']
f = open('zf_set','w')
for j in range(0,len(time)):
for k in range(0,len(xaxis)):
f.write('%f %f %f\n' %( time[j], xaxis[k] ,dset[j][k][0] ))
f.write('\n')
f.close()
return
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start+count):
for n, task in enumerate(tasks):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call plotting helper (dset axes: [t, x, y, z])
dset = file['tasks'][task]
logger.info('dset %s length %s' %(dset,len(dset.shape)))
image_axes = (1, 1)
data_slices = (index, slice(None), slice(None), 0)
plot_tools.plot_bot(dset,(1,2),(slice(None),slice(None)))
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def vertical(filename, start, count, output):
"""Slices in y-z at edge (x=0)."""
# Plot settings
tasks = [
'b vertical', 'q vertical', 'temp vertical', 'u vertical',
'v vertical', 'w vertical'
]
even_scale = [False, False, False, True, True, True]
scale = 2.5
dpi = 100
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'vert_{:06}.png'.format(write)
# Layout
nrows, ncols = 2, 3
image = plot_tools.Box(2, 2)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start + count):
for n, task in enumerate(tasks):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call plotting helper (dset axes: [t, x, y, z])
dset = file['tasks'][task]
image_axes = (2, 3)
data_slices = (index, 0, slice(None), slice(None))
plot_tools.plot_bot(dset,
image_axes,
data_slices,
axes=axes,
title=task,
even_scale=even_scale[n])
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def main(filename, start, count, output):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
tasks = ['u', 'v', 'vorticity']
scale = 2.5
dpi = 400
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'ucvc_{:06}.png'.format(write)
# Layout
nrows, ncols = 1, 3
image = plot_tools.Box(1, 1)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
current_cmap = matplotlib.cm.viridis
current_cmap.set_bad(color='white')
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start + count):
for n, task in enumerate(tasks):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call 3D plotting helper, slicing in time
dset = file['tasks'][task]
image_axes = (1, 2)
data_slices = [index, slice(None), slice(None)]
plot_tools.plot_bot(dset,
image_axes,
data_slices,
axes=axes,
title=task,
cmap=current_cmap) #, clim=[-1,1])
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def plot_spectra(filename, start, count, output):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
tasks = ['b', 'u', 'v', 'w']
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'spec_{:06}.png'.format(write)
# Layout
nrows = 3
ncols = len(tasks)
image = plot_tools.Box(4, 4)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
scale = 2
dpi = 100
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start + count):
for j, task in enumerate(tasks):
dset = file['tasks'][task]
# Load field in grid layout
field.set_scales(1)
field['g'] = dset[index]
for i in range(3):
data = np.log10(integrated_spectra(field, axis=i).T)
image_axes = [0, 1, 2]
image_axes.pop(i)
# Build subfigure axes
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
plot_tools.plot_bot(field,
image_axes,
data=data,
axes=axes,
title=task,
even_scale=False,
cmap='viridis')
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def main(filename, start, count, output):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
#with h5py.File(filename, mode='r') as file:
# tasks = sorted(file['tasks'].keys())
tasks = ['u', 'v', 'w']
scale = 4
dpi = 100
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'write_{:06}.png'.format(write)
# Layout
nrows, ncols = 1, 3
image = plot_tools.Box(2, 1)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
plt.style.use('prl')
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start + count):
for n, task in enumerate(tasks):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call 3D plotting helper, slicing in time
dset = file['tasks'][task]
#plot_axes = [3,1]
new_slices = [index] + run_slices
plot_tools.plot_bot(dset,
plot_axes,
new_slices,
axes=axes,
title=task,
even_scale=True)
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def main(prefix, start, count, output, task):
"""Save plot of specified task at given time for six (6) runs."""
task = str(task)
scale = 4
dpi = 100
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'write_{:06}.png'.format(write)
# Layout
nrows, ncols = 3, 2
image = plot_tools.Box(2, 1)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
plt.style.use('prl')
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
for folder in glob.glob(prefix):
filename = folder + "snapshots/snapshots_s1.h5"
with h5py.File(filename, mode='r') as file:
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call 3D plotting helper, slicing in time
dset_title = 'tasks/' + task
dset = file[dset_title][10, ]
new_slices = [index] + run_slices
plot_tools.plot_bot(dset,
plot_axes,
new_slices,
axes=axes,
title=task,
even_scale=True)
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def integral(filename, start, count, output):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
tasks = ['b integral x4']
scale = 2.5
dpi = 100
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'int_{:06}.png'.format(write)
# Layout
nrows, ncols = 1, 1
image = plot_tools.Box(2, 2)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start+count):
for n, task in enumerate(tasks):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call plotting helper (dset axes: [t, x, y, z])
dset = file['tasks'][task]
image_axes = (2, 1)
data_slices = (index, slice(None), slice(None), 0)
plot_tools.plot_bot(dset, image_axes, data_slices, axes=axes, title=task, even_scale=True, cmap='Greys')
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def planes(filename, start, count, output):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
tasks = ['ze bot','tf bot', 'ze mid', 'tf mid', 'ze top', 'tf top']
scale = 2.0
dpi = 200
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: 'mid_{:06}.png'.format(write)
# Layout
nrows, ncols = 3, 2
image = plot_tools.Box(2, 2)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start+count):
for n, task in enumerate(tasks):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call plotting helper (dset axes: [t, x, y, z])
dset = file['tasks'][task]
image_axes = (2, 1)
data_slices = (index, slice(None), slice(None), 0)
plot_tools.plot_bot(dset, image_axes, data_slices, axes=axes, title=task, even_scale=True)
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
image = plot_tools.Box(2, 2)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
tasks = ['b', 'q', 'temp', 'u', 'v', 'w']
even_scale = [False, False, False, True, True, True]
with h5py.File(filename, 'r') as df:
for n, task in enumerate(tasks):
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
dset = df['tasks'][task]
image_axes = (0, 3)
data_slices = (slice(None), 0, 0, slice(None))
plot_tools.plot_bot(dset,
image_axes,
data_slices,
axes=axes,
title=task,
even_scale=even_scale[n])
for f in filename.parts:
if f.startswith('rainy_benard'):
plot_base = f
plot_file_name = Path(plot_base + '_profiles.png')
plt.savefig(outbase / plot_file_name, dpi=300)
def main(file_path, output_path):
"""Save plot of specified tasks for given range of analysis writes."""
# Plot settings
# with h5py.File(file_path, mode='r') as file:
# tasks = sorted(file['tasks'].keys())
# tasks = tasks[MPI.COMM_WORLD.rank::MPI.COMM_WORLD.size]
#tasks = ["P1(cz)"]
dpi = 128
savename = lambda task: str(output_path.joinpath('hov_{:}.png'.format(task)))
# Selection settings
keep_axis = A = 3
index = 0
# Plot Hovmoller
image_axes = [0, A]
image_scales = ['sim_time', 0]
data_slices = (slice(None), 0, 0, slice(None))
with h5py.File(file_path, 'r') as file:
sim_time = file['scales']['sim_time'][:]
fig = plt.figure(figsize=(16,8))
data_slices = (slice(None), 0, 0, slice(None))
for task_name in file['tasks']:
dset = file['tasks'][task_name]
# Plot Hovmoller
axes = fig.add_axes([0.05, 0.4, 0.75, 0.45])
plot_tools.plot_bot(dset, image_axes, data_slices, image_scales, even_scale=True, axes=axes, title=task_name)
# Plot average vs time
axes = fig.add_axes([0.05+0.03*0.75, 0.1, 0.75*0.94, 0.2])
x = sim_time[data_slices[0]]
y = np.mean(dset[data_slices], axis=1)
plt.plot(x, y, '-k')
plt.xlabel(image_scales[0])
plt.title('<'+task_name+'>')
plt.xlim(min(x), max(x))
# Plot average vs height
y = file['scales']['y1']['1.0']
#t1 = np.max(sim_time) * 0.25
#x1 = np.mean(dset[data_slices][sim_time < t1], axis=0)
t1 = sim_time[data_slices[0]][0]
x1 = dset[data_slices][0]
#t2 = np.max(sim_time) * 0.75
#x2 = np.mean(dset[data_slices][sim_time > t2], axis=0)
t2 = sim_time[data_slices[0]][-1]
x2 = dset[data_slices][-1]
axes = fig.add_axes([0.85, 0.4, 0.1, 0.45*0.94])
plt.plot(x1, y, '-k')
plt.plot(x2, y, '--k')
#plt.title(' t < %.1f (solid) \n t > %.1f (dashed)' %(t1, t2))
plt.title('t = %.1f (solid) \n t = %.1f (dashed)' %(t1, t2))
plt.ylabel('y')
plt.ylim(min(y), max(y))
plt.setp(plt.gca().get_xticklabels(), rotation=90)
# Print double average
fig.text(0.8, 0.2, ' <%s> \n t < %.1f = %.3e +- %.3e \n t > %.1f = %.3e +- %.3e ' %(task_name, t1, np.mean(x1), np.std(x1), t2, np.mean(x2), np.std(x2)))
# Save figure
#fig.suptitle('Reynolds = %.2e, Prandtl = %.2e, Schmidt = %.2e' %(param.Reynolds, param.Prandtl, param.Schmidt))
fig.savefig(savename(task_name), dpi=dpi)
fig.clear()
plt.close(fig)
nrows, ncols = 2, 1
image = plot_tools.Box(2, 1)
pad = plot_tools.Frame(0.2, 0.1, 0.1, 0.1)
margin = plot_tools.Frame(0.1, 0.1, 0.1, 0.1)
scale = 4
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
axes = mfig.add_axes(0, 0, [0, 0, 1, 1])
# DNS
print("dns_data shape", dns_data['tasks/<u>_x'].shape)
print('ce2 data shape', ce2_data['tasks/cu'].shape)
pax, cax = plot_tools.plot_bot(dns_data['tasks/<u>_x'], dns_image_axes, dns_data_slices, image_scales, axes=axes, title=r'$\left<u\right>$ DNS', even_scale=True, clim=limits)
#pax.set_xlim(0,0.125)
pax.xaxis.set_label_text("time")
pax.yaxis.set_label_text("$y$")
pax.yaxis.label.set_rotation('horizontal')
pax.text(0.95,0.85, 'a',
bbox={'facecolor': 'grey', 'alpha': 0.5, 'boxstyle':'round'},transform=pax.transAxes, fontsize=18)
pax.set_rasterized(True)
axes = mfig.add_axes(1, 0, [0, 0, 1, 1])
# CE2
pax, cax = plot_tools.plot_bot(ce2_data['tasks/cu'], ce2_image_axes, ce2_data_slices, image_scales, axes=axes, title=r'$\left<u\right>$ CE2', even_scale=True, clim=limits)
pax.xaxis.set_label_text("time")
pax.yaxis.set_label_text("$y_1$")
pax.yaxis.label.set_rotation('horizontal')
datadir.joinpath(x) for x in
['data_snapshots_s1.h5', 'data_snapshots_s3.h5', 'data_snapshots_s101.h5']
]
title = r'$c_{\theta\theta}$' #(\xi, y_1, y_2=0.5)$'
for n, fn in enumerate(filenames):
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
with h5py.File(fn, 'r') as df:
dset = df['tasks/interp(ctt, y1=0.500)']
image_axes = [1, 2]
data_slices = [0, slice(None), slice(None), 0]
pax, cax = plot_tools.plot_bot(dset,
image_axes,
data_slices,
axes=axes,
title=title,
even_scale=True,
clim=limits)
pax.set_rasterized(True)
time = df['scales/sim_time'][0]
pax.text(0.1,
0.8,
't = {:3.2f}'.format(time),
bbox={
'facecolor': 'grey',
'alpha': 0.5,
'boxstyle': 'round'
},
transform=axes.transAxes,
fontsize=18)
y_ticks = (0.2, 0.4, 0.6, 0.8, 1.0)
scale = 2
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
axes = mfig.add_axes(0, 0, [0, 0, 1, 1])
# DNS
print("dns_data shape", dns_data['tasks/<u>_x'].shape)
print('ce2 data shape', ce2_data['tasks/cu'].shape)
pax, cax = plot_tools.plot_bot(dns_data['tasks/<u>_x'],
dns_image_axes,
dns_data_slices,
image_scales,
axes=axes,
title=r'$c_u$',
even_scale=True,
clim=limits,
cmap=cc.cm['bwy'])
#pax.set_xlim(0,0.125)
pax.xaxis.set_label_text(r"$t$")
pax.yaxis.set_label_text("$y$")
pax.set_yticks(y_ticks)
pax.yaxis.label.set_rotation('horizontal')
pax.text(0.95,
0.85,
'a',
bbox={
'facecolor': 'grey',
'alpha': 0.5,
power_axis_label = r"$\log_{10} |\hat{\zeta}|^2$"
elif field == 'theta':
power_axis_label = r"$\log_{10} |\hat{\theta}|^2$"
else:
raise NotImplementedError(
"Field can only be theta or zeta, not {}".format(field))
# DNS
dns_image_axes = (2, 1)
dns_image_slices = (dns_tidx, slice(None), slice(None))
dns_kspace_data = dns_data['tasks/{}_kspace'.format(field)]
dns_time = dns_data['scales/sim_time'][dns_tidx]
pax, cax = plot_tools.plot_bot(dns_kspace_data,
dns_image_axes,
dns_image_slices,
func=calc_dns_power,
clim=climits,
cmap='viridis',
axes=dns_axes)
pax.xaxis.set_major_locator(MaxNLocator(integer=True))
#pax.yaxis.set_major_locator(MaxNLocator(integer=True))
pax.set_xlim(xlimits)
pax.set_ylim(ylimits)
cax.set_xlabel(power_axis_label)
pax.set_xlabel(r"$k_x$")
pax.set_ylabel(r"$k_{y}$")
pax.text(0.9,
0.85,
'a',
bbox={
'facecolor': 'grey',
def abs_sqr(xmesh, ymesh, data):
newdata = np.abs(data)**2
return xmesh, ymesh, newdata
folders = sorted(glob.glob(prefix),
key=lambda folder: int(folder.split("_")[-1]))
for folder in folders:
filename = folder + "/spectra/spectra_s1.h5"
datafile = h5py.File(filename, 'r')
coeff = datafile['tasks/vc']
print(coeff.shape)
plot_axes = [1, 2]
slices = [-1, slice(None), slice(None), 0]
plot_tools.plot_bot(coeff, plot_axes, slices, func=abs_sqr)
plt.tight_layout()
plt.savefig("spectral_plot_" + str(folder.split("_")[-1]) + ".png",
dpi=300)
"""
# Create plot
plt.imshow(v_tot)
plt.gca().invert_yaxis()
plt.legend()
plt.tight_layout()
plot_file_name = 'v_tot_timeseries.png'
plt.savefig(plot_file_name, dpi=300)
"""
def main(filename, start, count, output="./img_snapshots", slice_type="side"):
"""Save plot of specified tasks for given range of analysis writes."""
filename = pathlib.Path(filename)
stem = filename.stem.split("_")[0]
if (slice_type == "top"):
nrows, ncols = 2,2
image_axes = [2, 3]
data_slices = [0, 0,slice(None), slice(None)]
tasks = ['Pressure(z=+1)', 'Ux(z=+1)', 'Uy(z=+1)']
elif (slice_type == "mid"):
nrows, ncols = 2,2
image_axes = [2, 3]
data_slices = [0, 0,slice(None), slice(None)]
tasks = ['W(z=+0.5)', 'T(z=+0.5)']
elif (slice_type == "side"):
nrows, ncols = 2,2
image_axes = [2, 1]
data_slices = [0, slice(None), slice(None),0]
tasks = ['Temperature(y=+1)', 'Pressure(y=+1)', 'Ox(y=+1)', 'Oz(y=+1)']
elif (slice_type == 'small'):
nrows, ncols = 2,2
image_axes = [2, 1]
data_slices = [0, slice(None), slice(None),0]
tasks = ['Temperature(y=+1)', 'Pressure(y=+1)', 'Oz(y=+1)']
else:
raise ValueError("must be side, mid or top slice. got {}".format(str(filename)))
# Plot settings
scale = 4
dpi = 100
title_func = lambda sim_time: 't = {:.3f}'.format(sim_time)
savename_func = lambda write: '{}_{:06}.png'.format(stem, write)
# Layout
image = plot_tools.Box(3, 1)
pad = plot_tools.Frame(0.2, 0.2, 0.1, 0.1)
margin = plot_tools.Frame(0.3, 0.2, 0.1, 0.1)
# Create multifigure
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
# Plot writes
with h5py.File(filename, mode='r') as file:
for index in range(start, start+count):
for n, task in enumerate(tasks):
# Build subfigure axes
i, j = divmod(n, ncols)
axes = mfig.add_axes(i, j, [0, 0, 1, 1])
# Call 3D plotting helper, slicing in time
dset = file['tasks'][task]
data_slices[0] = index
plot_tools.plot_bot(dset, image_axes, data_slices, axes=axes, title=task, even_scale=True)
# Add time title
title = title_func(file['scales/sim_time'][index])
title_height = 1 - 0.5 * mfig.margin.top / mfig.fig.y
fig.suptitle(title, x=0.48, y=title_height, ha='left')
# Save figure
savename = savename_func(file['scales/write_number'][index])
savepath = output.joinpath(savename)
fig.savefig(str(savepath), dpi=dpi)
fig.clear()
plt.close(fig)
def plot_power_spectra(ce2_filename,
start,
count,
output='write_',
field='zeta',
climits=None,
xlimits=None,
ylimits=None,
dns_tidx=-1,
ce2_tidx=-1,
label=None,
dns_filename=None):
with h5py.File(dns_filename,
'r') as dns_data, h5py.File(ce2_filename, 'r') as ce2_data:
nrows, ncols = 1, 3
image = plot_tools.Box(1, 1)
pad = plot_tools.Frame(0.2, 0.1, 0.1, 0.1)
margin = plot_tools.Frame(0.1, 0.1, 0.1, 0.1)
scale = 4
mfig = plot_tools.MultiFigure(nrows, ncols, image, pad, margin, scale)
fig = mfig.figure
dns_axes = mfig.add_axes(0, 0, [0, 0, 1, 1])
if field == 'zeta':
power_axis_label = r"$\log_{10} |\hat{\zeta}|^2$"
elif field == 'theta':
power_axis_label = r"$\log_{10} |\hat{\theta}|^2$"
else:
raise NotImplementedError(
"Field can only be theta or zeta, not {}".format(field))
# DNS
dns_image_axes = (2, 1)
dns_image_slices = (dns_tidx, slice(None), slice(None))
dns_kspace_data = dns_data['tasks/{}_kspace'.format(field)]
dns_time = dns_data['scales/sim_time'][dns_tidx]
pax, cax = plot_tools.plot_bot(dns_kspace_data,
dns_image_axes,
dns_image_slices,
func=calc_dns_power,
clim=climits,
cmap='viridis',
axes=dns_axes)
pax.xaxis.set_major_locator(MaxNLocator(integer=True))
#pax.yaxis.set_major_locator(MaxNLocator(integer=True))
pax.set_xlim(xlimits)
pax.set_ylim(ylimits)
cax.set_xlabel(power_axis_label)
pax.set_xlabel(r"$k_x$")
pax.set_ylabel(r"$k_{y}$")
pax.text(0.9,
0.85,
'a',
bbox={
'facecolor': 'grey',
'alpha': 0.5,
'boxstyle': 'round'
},
transform=pax.transAxes,
fontsize=18,
color='white')
pax.set_rasterized(True)
# CE2
ce2_axes = mfig.add_axes(0, 1, [0, 0, 1, 1])
ce2_image_axes = (1, 3)
ce2_image_slices = (ce2_tidx, slice(None), 0, slice(None))
ce2_kspace_data = ce2_data['tasks/{}_power'.format(field)]
ce2_time = ce2_data['scales/sim_time'][ce2_tidx]
ce2_write = ce2_data['scales/write_number'][ce2_tidx]
pax, cax = plot_tools.plot_bot(ce2_kspace_data,
ce2_image_axes,
ce2_image_slices,
func=calc_ce2_power,
clim=climits,
cmap='viridis',
axes=ce2_axes)
cax.set_xlabel(power_axis_label)
pax.xaxis.set_major_locator(MaxNLocator(integer=True))
pax.set_xlim(xlimits)
pax.set_ylim(ylimits)
pax.set_xlabel(r"$k_x$")
pax.set_ylabel(r"$k_{y}$")
pax.text(0.9,
0.85,
'b',
bbox={
'facecolor': 'grey',
'alpha': 0.5,
'boxstyle': 'round'
},
transform=pax.transAxes,
fontsize=18,
color='white')
pax.set_rasterized(True)
# plot only kx = 0 mode
kx0_axes = mfig.add_axes(0, 2, [0.03, 0, 0.94, 0.94])
kx, ky, data = plot_tools.get_plane(ce2_kspace_data, ce2_image_axes[0],
ce2_image_axes[1],
ce2_image_slices)
ce2_kx, ce2_ky, ce2_power = calc_ce2_power(kx, ky, data)
kx, ky, data = plot_tools.get_plane(dns_kspace_data, dns_image_axes[0],
dns_image_axes[1],
dns_image_slices)
dns_kx, dns_ky, dns_power = calc_dns_power(kx, ky, data)
kx0_axes.plot(dns_power[:, 0],
label='DNS t = {:5.2e}'.format(dns_time))
kx0_axes.plot(ce2_power[:, 0],
label='CE2 t = {:5.2e}'.format(ce2_time))
kx0_axes.set_xlabel(r"$k_{y}$")
kx0_axes.set_ylabel(power_axis_label)
#kx0_axes.set_xlim(1,15)
kx0_axes.text(0.9,
0.85,
'c',
bbox={
'facecolor': 'grey',
'alpha': 0.5,
'boxstyle': 'round'
},
transform=kx0_axes.transAxes,
fontsize=18,
color='black')
kx0_axes.xaxis.set_major_locator(MaxNLocator(integer=True))
kx0_axes.legend(loc='lower right')
outputdir = pathlib.Path(output)
for ext in ['png']: #,'pdf']:
outfilen = "power_spectra_dns_ce2_{}_field_{}_write_{:05d}.{}".format(
label, field, ce2_write, ext)
fig.savefig(str(outputdir / outfilen), dpi=400)
