if oldDAfilename != DAfilename:
1 / 0 # create an error to force reload
print("Using old data")
except:
print("loading {f}".format(f=DAfilename))
da = DA(DAfilename)
oldDAfilename = DAfilename
da.extract(locals(), "shot,phases,beta,freq,frlow,frhigh,t_mid,amp,a12")
print("loading {f}".format(f=clusterfile))
x = np.load(clusterfile)
for k in x.keys():
exec("{v}=x['{k}']".format(v=k, k=k))
start_mem = report_mem(msg="cluster_phases")
w5 = np.where((dists(subset[clinds[cl][0]], phases[:, sel]) < d_big) & (bw(freq, frlow, frhigh)) & (shot == shot))[0]
print(len(w5), len(unique(shot[w5])))
ph5 = phases[w5]
wc = np.where(dists(subset[clinds[cl][0]], ph5[:, sel]) < d_med)[0]
wcc = np.where(dists(subset[clinds[cl][0]], ph5[:, sel]) < d_sml)[0]
sl_red = compact_str(np.unique(shot[w5[wcc]]))
sl_green = compact_str(np.unique(shot[w5[wc]]))
titl = "red:d<{d_sml:.1g}:{slr}".format(slr=sl_red, d_sml=d_sml)
suptitl = "green:d<{d_med:.1g}:{slr}".format(slr=sl_green, d_med=d_med)
pl.figure(num="cl[{cl}] delta phase".format(cl=cl))
if clearfigs:
pl.clf()
for (i, ph) in enumerate(decimate(ph5, limit=1000)):
1 / 0 # create an error to force reload
print('Using old data')
except:
print('loading {f}'.format(f=DAfilename))
da = DA(DAfilename)
oldDAfilename = DAfilename
da.extract(locals(), 'shot,phases,beta,freq,frlow,frhigh,t_mid,amp,a12')
print('loading {f}'.format(f=clusterfile))
x = np.load(clusterfile)
for k in x.keys():
exec("{v}=x['{k}']".format(v=k, k=k))
start_mem = report_mem(msg='cluster_phases')
w5 = np.where((dists(subset[clinds[cl][0]], phases[:, sel]) < d_big)
& (bw(freq, frlow, frhigh)) & (shot == shot))[0]
print(len(w5), len(unique(shot[w5])))
ph5 = phases[w5]
wc = np.where(dists(subset[clinds[cl][0]], ph5[:, sel]) < d_med)[0]
wcc = np.where(dists(subset[clinds[cl][0]], ph5[:, sel]) < d_sml)[0]
sl_red = compact_str(np.unique(shot[w5[wcc]]))
sl_green = compact_str(np.unique(shot[w5[wc]]))
titl = 'red:d<{d_sml:.1g}:{slr}'.format(slr=sl_red, d_sml=d_sml)
suptitl = 'green:d<{d_med:.1g}:{slr}'.format(slr=sl_green, d_med=d_med)
pl.figure(num='cl[{cl}] delta phase'.format(cl=cl))
if clearfigs: pl.clf()
for (i, ph) in enumerate(decimate(ph5, limit=1000)):
pl.plot(ph[sel], 'k', linewidth=.03, hold=i > 0)
# this contrivance allows us to test on uniformly distributed phases
if uniform_random is not None:
print('evaluating {ur}'.format(ur=uniform_random))
phases = eval(uniform_random)
shot = np.array(np.shape(phases)[0]*[shot[0]])
freq = np.array(np.shape(phases)[0]*[freq[0]])
frlow = np.array(np.shape(phases)[0]*[0])
frhigh = np.array(np.shape(phases)[0]*[9e9])
t_mid = np.array(np.shape(phases)[0]*[t_mid[-1]])
cc5 = [] # cluster centres
for cl in cls:
start_mem = report_mem(msg='cluster_phases')
# used to where all at once - but as the distance is most expensive, do
# shot and freq first
w5_shot_freq = np.where((bw(freq,frlow,frhigh)) & (shot==shot))[0]; print(len(w5_shot_freq),len(np.unique(shot[w5_shot_freq])))
# the [:,sel] below is to avoid gather ops on two indices at once.
w5=np.where(dists(subset[clinds[cl][0]], phases[w5_shot_freq][:,sel])<d_big)[0];
w5 = w5_shot_freq[w5] # refer back to the original array
# old "all at once" way
# w5=np.where((dists(subset[clinds[cl][0]], phases[:,sel])<d_big) & (bw(freq,frlow,frhigh)) & (shot==shot))[0]; print(len(w5),len(np.unique(shot[w5])))
print(len(w5),len(np.unique(shot[w5])))
ph5=phases[w5]
wc=np.where(dists(subset[clinds[cl][0]], ph5[:,sel])<d_med)[0]
if len(wc)<1: raise ValueError('no points within avg radians of {dm} '.format(dm = d_med))
wcc=np.where(dists(subset[clinds[cl][0]], ph5[:,sel])<d_sml)[0]
sl_red = compact_str(np.unique(shot[w5[wcc]]))
sl_green = compact_str(np.unique(shot[w5[wc]]))
xlab='clind {cl}: {f}'.format(f=da.name, cl=cl)
# this contrivance allows us to test on uniformly distributed phases
if uniform_random is not None:
print('evaluating {ur}'.format(ur=uniform_random))
phases = eval(uniform_random)
shot = np.array(np.shape(phases)[0]*[shot[0]])
freq = np.array(np.shape(phases)[0]*[freq[0]])
frlow = np.array(np.shape(phases)[0]*[0])
frhigh = np.array(np.shape(phases)[0]*[9e9])
t_mid = np.array(np.shape(phases)[0]*[t_mid[-1]])
cc5 = [] # cluster centres
for cl in cls:
start_mem = report_mem(msg='cluster_phases')
# used to where all at once - but as the distance is most expensive, do
# shot and freq first
w5_shot_freq = np.where((bw(freq,frlow,frhigh)) & (shot==shot))[0]; print(len(w5_shot_freq),len(np.unique(shot[w5_shot_freq])))
# the [:,sel] below is to avoid gather ops on two indices at once.
w5=np.where(dists(subset[clinds[cl][0]], phases[w5_shot_freq][:,sel])<d_big)[0];
w5 = w5_shot_freq[w5] # refer back to the original array
# old "all at once" way
# w5=np.where((dists(subset[clinds[cl][0]], phases[:,sel])<d_big) & (bw(freq,frlow,frhigh)) & (shot==shot))[0]; print(len(w5),len(np.unique(shot[w5])))
print(len(w5),len(np.unique(shot[w5])))
ph5=phases[w5]
wc=np.where(dists(subset[clinds[cl][0]], ph5[:,sel])<d_med)[0]
if len(wc)<1: raise ValueError('no points within avg radians of {dm} '.format(dm = d_med))
wcc=np.where(dists(subset[clinds[cl][0]], ph5[:,sel])<d_sml)[0]
sl_red = compact_str(np.unique(shot[w5[wcc]]))
sl_green = compact_str(np.unique(shot[w5[wc]]))
xlab='clind {cl}: {f}'.format(f=da.name, cl=cl)
