Start by loading some boiler plate: matplotlib, numpy, scipy, json, functools, and a convenience class.
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%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)
import matplotlib.pyplot as plt
import numpy as np
import json
from functools import partial
class Foo: pass
And some more specialized dependencies:
Slict provides a convenient slice-able dictionary interfaceChest is an out-of-core dictionary that we'll hook directly to a globus remote using...glopen is an open-like context manager for remote globus files
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from chest import Chest
from slict import CachedSlict
from glopen import glopen, glopen_many
Helper routines
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def load_from_archive(names, arch):
cs = []
for name in names:
cs.append(Chest(path = "{:s}-results".format(name),
open = partial(glopen, endpoint=arch),
open_many = partial(glopen_many, endpoint=arch),
available_memory = 1e12))
scs = [CachedSlict(c) for c in cs]
ps = []
for name in names:
with glopen(
"{:s}.json".format(name), mode='r',
endpoint = arch,
) as f:
ps.append(json.load(f))
if len(names) == 1:
return cs[0], scs[0], ps[0]
return cs, scs, ps
Configuration for this figure. config.ref points to a simulation with periodic boundaries.
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config = Foo()
config.names = [
# "Wilk/Wilk_kmin_2.5/Wilk_kmin_2.5",
"Wilk/Wilk_kmin_3.5/Wilk_kmin_3.5",
# "Wilk/Wilk_kmin_4.5/Wilk_kmin_4.5",
# "Wilk/Wilk_long/Wilk_long",
# "Wilk/Wilk_per/Wilk_per",
]
config.arch_end = "maxhutch#alpha-admin/~/pub/"
#config.arch_end = "alcf#dtn_mira/projects/alpha-nek/experiments/"
#config.arch_end = "alcf#dtn_mira/projects/PetaCESAR/maxhutch/"
Open a chest located on a remote globus endpoint and load a remote json configuration file.
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c, sc, p = load_from_archive(config.names, config.arch_end);
We want to plot the overall spike depth, which is the H_exp field in the chest, and the depth for individual spikes, H_exp_cell.
H_exp = max(H_exp_cell)
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c.prefetch(sc[:,'w_xy'].full_keys() + sc[:,'z_z'].full_keys() )
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from IPython.html.widgets import interact, IntSlider
def plot_profile(frame, index):
T = sc[:,'w_xy'].keys()[frame]
data = sc[T,'w_xy'][index,:]
x = sc[0.0,'z_z'][-sc[T,'w_xy'].shape[0]:]
plt.imshow(sc[T,'w_xy'])
plt.axhline(index, color='k')
plt.figure()
plt.plot(x,data[:])
plt.xlabel("y")
plt.ylabel("w")
plt.figure()
scalex = np.sqrt(np.abs(data[1]/x[1]) * p['viscosity']) / p['viscosity']
scaley = 1./np.sqrt(np.abs(data[1]/x[1]) * p['viscosity'])
plt.plot(scalex*x[:50],scaley*data[:50], 'x-')
plt.grid(True)
plt.xlabel("y+")
plt.ylabel("w+")
plt.figure()
scalex = np.sqrt(np.abs(data[-1]/(x[-1]-1)) * p['viscosity']) / p['viscosity']
scaley = 1./np.sqrt(np.abs(data[-1]/(x[-1]-1)) * p['viscosity'])
plt.plot(scalex*(x[-50:]-1),scaley*data[-50:], 'x-')
plt.grid(True)
plt.xlabel("y+")
plt.ylabel("w+")
interact(plot_profile,
frame=(0,64),
index=IntSlider(min=0,max=sc[0.0,'w_xy'].shape[0]-1,step=8,value=int(sc[0.0,'w_xy'].shape[0]*7/9))
)
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%install_ext http://raw.github.com/jrjohansson/version_information/master/version_information.py
%load_ext version_information
%version_information numpy, matplotlib, slict, chest, glopen, globussh
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