yinches = len(r)/float(dpi)
figsize = (xinches + 1.5, yinches + 1)
fig = plt.figure(figsize=figsize)
ax = plt.subplot(111)
el.make_axes_fixed(ax, xinches, yinches)
img = el.rtiplot(z, t, r, ax=ax, csize=0.0625, cpad=0.05, **kwargs)
plt.tight_layout(0.1)
return fig
basedir = 'figures'
if not os.path.exists(basedir):
os.makedirs(basedir)
cmap = copy.copy(plt.cm.coolwarm)
cmap.set_bad(cmap(0))
rslc = el.slice_by_value(cs.r, 97000, 113500)
for pslc in [slice(0, None, 5), slice(1, None, 5), slice(2, None, 5),
slice(3, None, 5), slice(4, None, 5), slice(None)]:
fig1 = plotter(20*np.log10(cs.h_sig[pslc, rslc]/cs.noise_sigma),
cs.t[pslc], cs.r[rslc]/1e3,
exact_ticks=False, vmin=0, vmax=40, cmap=cmap,
ylabel='Range (km)', clabel='SNR (dB)')
path = os.path.join(basedir, basefilename + '_recovered_rti_{0}.pdf')
if pslc.start is not None:
path = path.format(pslc.start)
else:
path = path.format('all')
fig1.savefig(path, dpi=savedpi, bbox_inches='tight', pad_inches=0.05,
transparent=True)
fig2 = plotter(20*np.log10(np.abs(cs.h_noise[pslc, rslc])/cs.noise_sigma),
return np.exp(2*np.pi*1j*(0.5*slope*t - bandwidth_hz/2.)*t)
barker13 = np.array([1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1]).astype(np.float_)
msl = np.array([-1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1]).astype(np.float_)
uncoded = np.ones(40)
lfm = make_lfm(51, 1e-6, 1e6)
psrnd = np.array([-1, 1, 1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1]).astype(np.float_)
codes = [barker13, msl, uncoded, lfm, psrnd]
code_delays = [0]*len(codes)
files = el.jicamarca.find_files(datadir)
vlt_o = el.jicamarca.voltage_reader(files[565])
rall = vlt_o.r
rslc = el.slice_by_value(rall, 80000, 120000)
r = rall[rslc]
vlt1 = vlt_o.read_from_block(22, 150, 0, sample_idx=rslc, chan_idx=0)
vlt2 = vlt_o.read_from_block(23, 0, 150, sample_idx=rslc, chan_idx=0)
vlt = np.concatenate((vlt1, vlt2), axis=0)
t = vlt_o.block_times[22] + (150 + np.arange(vlt.shape[0]))*vlt_o.ipp
ts = vlt_o.ts
ipp = vlt_o.ipp
f0 = 49920000.0
noise_vlt = vlt_o.read_from_block(22, 0, 0, sample_idx=el.slice_by_value(rall, None, 80000),
chan_idx=0)
noise_sigma = np.sqrt(np.mean(noise_vlt.real**2 + noise_vlt.imag**2))
store = Bunch(vlt=vlt, r=r, t=t, codes=codes, code_delays=code_delays,
ts=ts, ipp=ipp, f0=f0, noise_sigma=noise_sigma)
yinches = len(r)/float(dpi)
figsize = (xinches + 1.5, yinches + 1)
fig = plt.figure(figsize=figsize)
ax = plt.subplot(111)
el.make_axes_fixed(ax, xinches, yinches)
img = el.rtiplot(z, t, r, ax=ax, csize=0.0625, cpad=0.05, **kwargs)
plt.tight_layout(0.1)
return fig
basedir = 'figures'
if not os.path.exists(basedir):
os.makedirs(basedir)
cmap = copy.copy(plt.cm.coolwarm)
cmap.set_bad(cmap(0))
rslc = el.slice_by_value(mf.r, 85000, 120000)
pend = -100
for pslc in [slice(0, pend, 5), slice(1, pend, 5), slice(2, pend, 5),
slice(3, pend, 5), slice(4, pend, 5), slice(0, pend)]:
fig4 = plotter(20*np.log10(np.abs(mf.vlt[pslc, rslc])/mf.noise_sigma),
mf.t[pslc], mf.r[rslc]/1e3,
exact_ticks=False, vmin=0, vmax=40,
ylabel='Range (km)', clabel='SNR (dB)')
path = os.path.join(basedir, basefilename + '_mf_rti_{0}.pdf')
if pslc.start is not None:
path = path.format(pslc.start)
else:
path = path.format('all')
fig4.savefig(path, dpi=savedpi, bbox_inches='tight', pad_inches=0.05,
transparent=True)
return fig
basefilename = 'ejet_two_heads_trail'
with open(basefilename + '.pkl', 'rb') as f:
data = cPickle.load(f)
with open(basefilename + '_recovered_range.pkl', 'rb') as f:
cs = cPickle.load(f)
dpi = 75*2 # should be sized to match font size
savedpi = dpi*2 # should be a multiple of dpi
pixelaspect = 1
basedir = 'figures'
if not os.path.exists(basedir):
os.makedirs(basedir)
cmap = copy.copy(plt.cm.coolwarm)
cmap.set_bad(cmap(0))
rslc = el.slice_by_value(cs.r, 90000, 110000)
fig = plot_block(20*np.log10(np.abs(cs.h[:, rslc])/cs.noise_sigma),
cs.t, cs.r[rslc],
dpi=dpi, pixelaspect=pixelaspect,
vmin=0, vmax=25,
csize=0.0625, cpad=0.05)
fpath = os.path.join(basedir, basefilename + '_recovered_rti_block.pdf')
fig.savefig(fpath, dpi=savedpi, bbox_inches='tight', pad_inches=0, transparent=True)
plt.close(fig)
plt.show()
import echolect as el
import radarmodel
mfblksize = 5
mfvoters = [1, 2, 4]
basefilename = 'ejet_head_flare'
with open(basefilename + '.pkl', 'rb') as f:
data = cPickle.load(f)
n = 1
freqs = np.fft.fftfreq(int(n), data.ts/np.timedelta64(1, 's'))
v = freqs/data.f0*sp.constants.c/2
rslc = el.slice_by_value(data.r, 80000, 140000)
r = data.r[rslc]
m = r.shape[0]
filts = []
for code, delay in zip(data.codes, data.code_delays):
s = (code/np.linalg.norm(code)).astype(data.vlt.dtype)
filt = el.filtering.MatchedDoppler(s, n, data.vlt.shape[-1], xdtype=data.vlt.dtype)
filt.nodelay = slice(filt.L - 1 - delay, filt.L - 1 - delay + filt.M)
filts.append(filt)
vlt_mf_all = np.zeros((mfblksize, n, m), data.vlt.dtype)
vlt_mf = np.zeros((data.vlt.shape[0], m), data.vlt.dtype)
freq = np.zeros(data.vlt.shape[0])
for kp in xrange(data.vlt.shape[0]):
y = data.vlt[kp]
basefilename = 'head_and_flare'
with open(basefilename + '.pkl', 'rb') as f:
data = cPickle.load(f)
with open(basefilename + '_mf.pkl', 'rb') as f:
mf = cPickle.load(f)
dpi = 75*4 # should be sized to match font size
savedpi = dpi*1 # should be a multiple of dpi
pixelaspect = 4
basedir = 'figures'
if not os.path.exists(basedir):
os.makedirs(basedir)
cmap = copy.copy(plt.cm.coolwarm)
cmap.set_bad(cmap(0))
rslc = el.slice_by_value(mf.r, 86000, 97000)
fig = plot_block(20*np.log10(np.abs(mf.vlt[:, rslc])/mf.noise_sigma),
20*np.log10(np.abs(data.vlt[2::5, rslc])/data.noise_sigma),
mf.t, mf.r[rslc],
dpi=dpi, pixelaspect=pixelaspect,
vmin=0, vmax=40,
csize=0.0625, cpad=0.05)
fpath = os.path.join(basedir, basefilename + '_mf_rti_block.pdf')
fig.savefig(fpath, dpi=savedpi, bbox_inches='tight', pad_inches=0, transparent=True)
plt.close(fig)
plt.show()
return np.exp(2*np.pi*1j*(0.5*slope*t - bandwidth_hz/2.)*t)
barker13 = np.array([1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1]).astype(np.float_)
msl = np.array([-1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1]).astype(np.float_)
uncoded = np.ones(40)
lfm = make_lfm(51, 1e-6, 1e6)
psrnd = np.array([-1, 1, 1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1]).astype(np.float_)
codes = [barker13, msl, uncoded, lfm, psrnd]
code_delays = [0]*len(codes)
files = el.jicamarca.find_files(datadir)
vlt_o = el.jicamarca.voltage_reader(files[598])
r = vlt_o.r
vlt1 = vlt_o.read_from_block(8, 700, 0, chan_idx=0)
vlt2 = vlt_o.read_from_block(9, 0, 700, chan_idx=0)
vlt = np.concatenate((vlt1, vlt2), axis=0)
t = vlt_o.block_times[8] + (700 + np.arange(vlt.shape[0]))*vlt_o.ipp
ts = vlt_o.ts
ipp = vlt_o.ipp
f0 = 49920000.0
noise_vlt = vlt_o.read_from_block(8, 0, 0, sample_idx=el.slice_by_value(r, None, 80000),
chan_idx=0)
noise_sigma = np.sqrt(np.mean(noise_vlt.real**2 + noise_vlt.imag**2))
store = Bunch(vlt=vlt, r=r, t=t, codes=codes, code_delays=code_delays,
ts=ts, ipp=ipp, f0=f0, noise_sigma=noise_sigma)
with open('ejet_head_flare.pkl', 'wb') as f:
cPickle.dump(store, f, protocol=-1)