def custom_demodulation(ts, myrecording, lenghtBits):
# this custom ASK modulation (amplitude-shift modulation)
# is to support CDMA, in this case: 0, -2, 2. Each number will have certain amplitude
lenghtOneChunk = int(len(ts) / lenghtBits)
bitsReceived = []
currentChunk = 0
for i in range(lenghtBits):
allDataInCurrentChunk = []
for j in range(lenghtOneChunk):
currentData = myrecording[j + (currentChunk * lenghtOneChunk)]
positiveValue = abs(currentData)
allDataInCurrentChunk.append(positiveValue)
median = pyl.median(allDataInCurrentChunk)
print(median)
currentBit = 0
# WARNING: This values depends on device types
# and volume used.
minForBit2 = 0.09
minForBit_2 = 0.03
if (median >= minForBit2):
currentBit = 2
elif (median >= minForBit_2 and median < minForBit2):
currentBit = -2
else:
currentBit = 0
bitsReceived.append(currentBit)
currentChunk += 1
return bitsReceived
def self_resp_gate(navdata, tr, resp_dur):
print("Generating respiratory gates from self-gated FIDs...")
compi = N.array((0. + 1.j), N.complex)
sizes = N.shape(navdata)
ref_base = N.zeros(sizes, N.complex)
diff_navdata = N.zeros(sizes, N.complex)
ref_width = 512
n_fids_perblock = 128
num_blocks = int(N.ceil(sizes[0] / float(n_fids_perblock)))
for j in range(num_blocks):
fid_start = j * n_fids_perblock
fid_end = (j + 1) * n_fids_perblock
if (fid_end > sizes[0]):
fid_end = sizes[0]
start_pt = {
0: 0,
1: fid_start - (ref_width - n_fids_perblock) / 2
}[fid_start - (ref_width - n_fids_perblock) / 2 > 0]
end_pt = {
0: sizes[0],
1: fid_end + (ref_width - n_fids_perblock) / 2 + 1
}[fid_end + (ref_width - n_fids_perblock) / 2 < sizes[0] - 1]
if start_pt == 0:
end_pt = ref_width
if end_pt == sizes[0]:
start_pt = sizes[0] - ref_width
n_pts = end_pt - start_pt
reference_fid = N.sort( navdata[start_pt:end_pt,:].real, axis=0)[n_pts/4:3*n_pts/4+1:n_pts/4,:] + \
compi * N.sort( navdata[start_pt:end_pt,:].imag, axis=0)[n_pts/4:3*n_pts/4+1:n_pts/4,:]
ref_base[fid_start:fid_end, :] = reference_fid[1, :]
diff_navdata[fid_start:fid_end, :] = (
navdata[fid_start:fid_end, :] -
reference_fid[1, :]) / (reference_fid[2, :] - reference_fid[0, :])
resp_sig = N.sum(abs(diff_navdata[:, 0:5]) / 4.0, axis=-1)
testfig = N.ravel(resp_sig)
ref_val = median(testfig)
close_testfigs = N.compress(N.less(testfig, 20.0), testfig)
numbins = 400
[binfreq, binloc, patches] = hist(close_testfigs, numbins)
max_freq = N.maximum.reduce(binfreq)
testfig_mode = binloc[N.argmax(binfreq)]
quiet_cutoff = binloc[N.maximum.reduce(
N.nonzero(N.greater(binfreq, max_freq / 8))[0])]
if (quiet_cutoff < 2 * testfig_mode):
quiet_cutoff = 2 * testfig_mode
print(quiet_cutoff)
resp_gate_sig = N.less(resp_sig, quiet_cutoff)
resp_gate_sig_orig = resp_gate_sig.copy()
medfiltwindow = 2 * (int(resp_dur / tr)) + 1
resp_gate_sig = medfilt(resp_gate_sig_orig, medfiltwindow)
return resp_gate_sig, resp_sig
color='black',
label=label,
zorder=10)
if name in winners.values():
i2 += 1
label = None if i2 != 1 else "Tournament win (top 15 only)"
yy = [y for y in years if winners[y] == name]
P.scatter(yy,
P.array(C.get_y_hat(yy)),
marker="*",
color="green",
label=label,
zorder=15)
else:
P.plot(t, theta_hat, linewidth=1, color='grey', alpha=0.5, zorder=1)
years, med_theta = P.array([(year, P.median(abils[year]))
for year in range(1937, 2019)
if len(abils[year]) > 0]).T
P.plot(years,
med_theta,
linewidth=2,
color='red',
zorder=20,
label="Median ability")
P.legend()
# P.title("Trajectories for abilities of Masters Tournament attendees")
P.xlabel("Year")
P.ylabel("Ability")
P.ylim([-3, 3])
# P.savefig('../writeup/figs/overall_golf_trajectories.pdf')
P.show()
def phot1(r,
imlist,
plot=True,
names=None,
panel=None,
debug=None,
showmask="both"):
"""
give me a region and an imlist and tell me whether to plot cutouts
"""
nim = len(imlist)
# TODO alg for how many subpanels.
if panel == None: panel = [5, 4, 1] # for vertical page
f = []
df = []
raw = []
for j in range(nim):
im = imlist[j]
# if plotting, need to make image cutouts; even if not, this tells
# us if the source is off the edge
xtents = r.imextents(imlist[j])
minsize = 5
if (xtents[3] - xtents[1]) < minsize or (xtents[2] -
xtents[0]) < minsize:
raw0, f0, df0 = 0, 0, 0
print "phot region too small - %f,%f less than %d pixels" % (
(xtents[3] - xtents[1]), (xtents[2] - xtents[0]), minsize)
print xtents, r.imcoords(im, reg="bg1")
else:
if plot:
pl.subplot(panel[0], panel[1], panel[2])
im = hextract(imlist[j], xtents)[0]
## ROTATE
rotate = True
if rotate:
from astropy import wcs
w = wcs.WCS(im.header)
from scipy.ndimage.interpolation import rotate
if w.wcs.has_crota():
t0 = w.wcs.crota[1]
else:
t0 = pl.arctan2(w.wcs.cd[0, 1],
-w.wcs.cd[0, 0]) * 180 / pl.pi
theta = -1 * t0
im.data = rotate(im.data, theta, reshape=False)
ct = pl.cos(pl.pi * theta / 180)
st = pl.sin(pl.pi * theta / 180)
if w.wcs.has_crota():
w.wcs.crota[1] = w.wcs.crota[1] + theta
im.header['CROTA2'] = w.wcs.crota[1]
print "rotating crota by " + str(theta)
else:
w.wcs.cd = pl.matrix(w.wcs.cd) * pl.matrix([[ct, -st],
[st, ct]])
im.header['CD1_1'] = w.wcs.cd[0, 0]
im.header['CD1_2'] = w.wcs.cd[0, 1]
im.header['CD2_1'] = w.wcs.cd[1, 0]
im.header['CD2_2'] = w.wcs.cd[1, 1]
print "rotating cd by " + str(theta)
#pdb.set_trace()
# ugh need minmax of aperture region...
# estimate as inner 1/2 for now
s = im.shape
z = im.data[int(s[0] * 0.25):int(s[0] * 0.75),
int(s[1] * 0.25):int(s[1] * 0.75)]
if len(z[0]) <= 0:
print z
z = pl.where(pl.isnan(im.data))
if len(z[0]) > 0:
z = pl.where(pl.isnan(im.data) == False)
std = im.data[z[0], z[1]].std()
else:
std = im.data.std()
rg = pl.median(im.data) + pl.array([-0.5, 5]) * std
# marta wants them less saturated
rg[1] = pl.nanmax(im.data)
if rg[0] < 0: rg[0] = 0
if rg[1] <= rg[0]:
rg = [pl.nanmin(z), pl.nanmax(z)]
if showmask == False or showmask == "both": # show the jet one
pl.imshow(im.data,
origin="bottom",
interpolation="nearest",
vmin=rg[0],
vmax=rg[1])
elif showmask == True: # only show the mask
pl.imshow(im.data,
origin="bottom",
interpolation="nearest",
vmin=rg[0],
vmax=rg[1],
cmap="YlGn")
ax = pl.gca()
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
if names:
pl.text(0.05,
0.99,
names[j],
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes,
bbox=dict(facecolor='white', alpha=0.5))
pl.xlim([-0.5, s[1] - 0.5])
pl.ylim([-0.5, s[0] - 0.5])
if showmask == False:
r.plotimx(im) # overplot the apertures
if showmask == "both":
panel[2] = panel[2] + 1
pl.subplot(panel[0], panel[1], panel[2])
rg = pl.median(im.data) + pl.array([-0.5, 5]) * std
if rg[0] < 0: rg[0] = 0
if rg[1] <= rg[0]:
rg = [pl.nanmin(z), pl.nanmax(z)]
pl.imshow(im.data,
origin="bottom",
interpolation="nearest",
vmin=rg[0],
vmax=rg[1],
cmap="YlGn")
ax = pl.gca()
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
if names:
pl.text(0.05,
0.99,
names[j],
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes,
bbox=dict(facecolor='white', alpha=0.5))
pl.xlim([-0.5, s[1] - 0.5])
pl.ylim([-0.5, s[0] - 0.5])
if debug:
# r.debug=True
if names:
print names[j]
raw0, bg, f0, df0 = r.phot(im)
raw.append(raw0)
f.append(f0)
df.append(df0)
panel[2] = panel[2] + 1
if plot:
pl.subplots_adjust(wspace=0.02,
hspace=0.02,
left=0.1,
right=0.97,
top=0.95,
bottom=0.05)
return f, df, raw