def plotHPCP(signal,
signalname,
hpcp,
w,
speaks,
frameSize,
hopSize,
figtitle,
show=False):
hpcp_, _, _, hpcpmean, hpcpmedian = extractHPCP(signal, frameSize, hopSize,
w, speaks, hpcp, signal)
fig = figure(num=None,
figsize=(8, 5),
dpi=80,
facecolor='w',
edgecolor='k')
gs = GridSpec(4, 4)
ax1 = subplot(gs[:, :-1])
ax2 = subplot(gs[:, -1])
ax1.imshow(hpcp_[::-1, :], aspect='auto')
ax1.set_title(figtitle)
ax2.plot(hpcpmean, xrange(36))
yticks(np.arange(36), np.arange(35, -1, -1))
locator_params(axis='x', nbins=4)
tight_layout()
if show:
show()
figname = figtitle + '.png'
fig.savefig(figname, bbox_inches='tight')
html = "<p><img src=\"" + figname + "\"></p>\n"
return html
def subplot2grid(shape, loc, rowspan=1, colspan=1, **kwargs):
from matplotlib.pyplot import GridSpec, gcf
fig = gcf()
s1,s2 = shape
subplotspec = GridSpec(s1, s2).new_subplotspec(loc, rowspan=rowspan, colspan=colspan)
a = fig.add_subplot(subplotspec, **kwargs)
bbox = a.bbox
byebye = []
for other in fig.axes:
if other==a: continue
if bbox.fully_overlaps(other.bbox):
byebye.append(other)
for ax in byebye: delaxes(ax)
return a
def __init__(self):
# 공통 그래프
self.fig = plt.figure()
self.gs = GridSpec(3, 1, figure=self.fig)
self.fig_db = {
'PZRPres': [],
'PZRLevl': [],
'FV122': [],
'HV142': [],
'PZRSpray': [],
'PZRTemp': [],
'ExitCoreT': [],
}
self.axes = [
# Left
self.fig.add_subplot(self.gs[0:1, :]),
self.fig.add_subplot(self.gs[1:2, :]),
self.fig.add_subplot(self.gs[2:3, :]),
]
noise = np.random.random(x.size) - 0.5
if i < 4:
swipe.append(x + noise/(i+1))
else:
swipe.append(x)
swipe = np.array(swipe)
# First trying to find median value
average = np.median(swipe,axis=0)
# Then see how coherent each signal is comparing to median
fig1=figure(1)
gs1=GridSpec(5,6)
gs1.update(left=0.01,right=0.01)
ax1=subplot2grid((5,6),(0,0),rowspan=4)
for i in xrange(swipe.shape[0]):
ax1.plot(t,swipe[i,:]+i)
ax1.set_ylabel('RF number')
ax2=subplot2grid((5,6),(4,0),colspan=1,rowspan=2)
ax2.plot(t,average)
ax2.set_title('Median RF')
ax3=subplot2grid((5,6),(0,2),colspan=4,rowspan=3)
for i in xrange(swipe.shape[0]):
def plot_and_save_results(ana, line_id="young_plume", date_fmt="%H:%M"):
# plot colors for different optical flow retrievals
cmap = get_cmap("Oranges")
c_optflow_hybrid = cmap(255)
c_optflow_histo = cmap(175)
c_optflow_raw = cmap(100)
fig = figure(figsize=(16, 12))
gs = GridSpec(4, 1, height_ratios=[.6, .2, .2, .2], hspace=0.05)
ax3 = fig.add_subplot(gs[3])
ax0 = fig.add_subplot(gs[0], sharex=ax3)
ax1 = fig.add_subplot(gs[1], sharex=ax3)
ax2 = fig.add_subplot(gs[2], sharex=ax3)
ax1.yaxis.tick_right()
ax1.yaxis.set_label_position("right")
ax3.yaxis.tick_right()
ax3.yaxis.set_label_position("right")
# Get emission rate results for the PCS line
res0 = ana.get_results(line_id=line_id, velo_mode="glob")
res1 = ana.get_results(line_id=line_id, velo_mode="flow_raw")
res2 = ana.get_results(line_id=line_id, velo_mode="flow_histo")
res3 = ana.get_results(line_id=line_id, velo_mode="flow_hybrid")
res0.save_txt(join(SAVE_DIR, "ex12_flux_velo_glob.txt"))
res1.save_txt(join(SAVE_DIR, "ex12_flux_flow_raw.txt"))
res2.save_txt(join(SAVE_DIR, "ex12_flux_flow_histo.txt"))
res3.save_txt(join(SAVE_DIR, "ex12_flux_flow_hybrid.txt"))
# Plot emission rates for the different plume speed retrievals
res0.plot(yerr=True,
date_fmt=date_fmt,
ls="-",
ax=ax0,
color="c",
ymin=0,
alpha_err=0.08)
res1.plot(yerr=False, ax=ax0, ls="-", color=c_optflow_raw, ymin=0)
res2.plot(yerr=False, ax=ax0, ls="--", color=c_optflow_histo, ymin=0)
res3.plot(yerr=True, ax=ax0, lw=3, ls="-", color=c_optflow_hybrid, ymin=0)
# ax[0].set_title("Retrieved emission rates")
ax0.legend(loc='best', fancybox=True, framealpha=0.5, fontsize=12)
ax0.grid()
# Plot effective velocity retrieved from optical flow histogram analysis
res3.plot_velo_eff(ax=ax1, date_fmt=date_fmt, color=c_optflow_hybrid)
# ax[1].set_title("Effective plume speed
# (from optflow histogram analysis)")
ax1.set_ylim([0, ax1.get_ylim()[1]])
# Plot time series of predominant plume direction (retrieved from optical
# flow histogram analysis and stored in object of type LocalPlumeProperties
# which is part of plumespeed.py module
ana.pcs_lines[line_id].plume_props.plot_directions(ax=ax2,
date_fmt=date_fmt,
color=c_optflow_hybrid)
ax2.set_ylim([-180, 180])
pyplis.helpers.rotate_xtick_labels(ax=ax2)
ax0.set_xticklabels([])
ax1.set_xticklabels([])
ax2.set_xticklabels([])
# tight_layout()
ax3 = ana.plot_bg_roi_vals(ax=ax3, date_fmt="%H:%M")
# gs.tight_layout(fig, h_pad=0)#0.03)
gs.update(hspace=0.05, top=0.97, bottom=0.07)
return fig
def plot_all_clusters(group_cats, outdir, plot_dir, wells=None, **kwargs):
"""
Plot clusters in map view, xsection and stress orientations
:param group_cat: Catalog of events in the cluster
:param outdir: Arnold-Townend output directory
:param plot_dir: Output directory for plots
:param kwargs: kwargs passed to plot_well_seismicity
:return:
"""
# Just big loop over all clusters
for i, cat in enumerate(group_cats):
# Do some setting up of the plots based on wells
if wells == 'Rotokawa':
profile = [(176.185, -38.60), (176.21, -38.62)]
xsection = [(176.185, -38.60), (176.21, -38.62)]
else:
# Figure out which wells to plot from median event latitude
med_lat = np.median([ev.preferred_origin().latitude for ev in cat])
if med_lat > -38.55: # NgaN
wells = ['NM08', 'NM09']
elif med_lat < -38.55:
wells = ['NM06', 'NM10']
profile = 'EW'
xsection = None
if len(cat) < 10:
print('Fewer than 10 events. Not plotting')
continue
clust_name = '{}_0'.format(i)
# Set up subplots with gridspec
fig = plt.figure(figsize=(12, 12))
gs = GridSpec(4, 4) #, hspace=0.1, wspace=0.1)
ax_map = fig.add_subplot(gs[0:2, 0:2])
ax_xsec = fig.add_subplot(gs[2:, :])
ax_strs = fig.add_subplot(gs[0:2, 2:], polar=True)
ax_map = plot_well_seismicity(cat,
wells=wells,
profile='map',
ax=ax_map,
xsection=xsection,
color=False,
**kwargs)
ax_xsec = plot_well_seismicity(cat,
wells=wells,
profile=profile,
ax=ax_xsec,
color=False,
**kwargs)
try:
ax_strs = plot_arnold_density(outdir=outdir,
clust_name=clust_name,
ax=ax_strs)
except:
print('Inversion output doesnt exist. Moving on.')
continue
plt.tight_layout()
plt.savefig('{}/Cluster_{}.png'.format(plot_dir, clust_name),
dpi=300,
bbox='tight')
plt.close('all')
return
if color:
Xc = tf.split(X, 3, axis=3)
X = tf.concat([_new_phase_shift(x, r) for x in Xc], axis=3)
else:
X = _new_phase_shift(X, r)
return X
# Feature map with shape [1, 8, 8, 4] with each feature map i having value i
x = np.ones((1, 8, 8, 4)) * np.arange(4)[None, None, None, :]
# Convert to a [1, 16, 16, 1] Tensor
y = tf.depth_to_space(tf.constant(x), 2)
# Plot results
figure(figsize=(12, 4.5))
gs = GridSpec(2, 5, width_ratios=[1, 1, 2, 2, 2])
for i in xrange(4):
plt.subplot(gs[i // 2, i % 2])
plt.imshow(x[:, :, :, i].squeeze(), cmap=cm.jet, vmin=0, vmax=4, interpolation='nearest')
# Add ticks at pixels, annoyingly have to offset by 0.5 to line up with pixels
xticks(0.5 + np.arange(8))
yticks(0.5 + np.arange(8))
plt.gca().set_xticklabels([])
gca().set_yticklabels([])
plt.title('feature %d' % i)
subplot(gs[:, 2])
print x.shape
out_ps = PS(tf.constant(x), 2)
imshow(tf.squeeze(out_ps).numpy(), cmap=cm.jet, vmin=0, vmax=4, interpolation='nearest')
axis('off')