def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10, 3))
ax = fig.add_subplot((111))
x = x.round(2)
#n = y_lin[0] # zero at the begining
n = y_lin[0] + (y_lin[-1] - y_lin[0]) / 2. # zero at the middle
plt.plot(x, y - n, linewidth=3)
#plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin - n, 'r')
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m * 100), loc=1)
viz.add_inner_title(ax, title, loc=2)
plt.ylim(-.3, .3)
plt.xlim(2003.5, 2010.1)
#plt.xlabel('years')
plt.ylabel('Elevation change (m)')
#fig.autofmt_xdate()
if savefig is not None:
pass
#plt.savefig(savefig+'_ts.png')
#np.savetxt(savefig+'_ts.txt', np.column_stack((x, y)))
return fig
def plot_ts(table):
"""
Plot dh and dAGC time series and the correlation dAGC x dh.
"""
sys.path.append('/Users/fpaolo/code/misc')
from util import poly_fit
# load data from Table
time2 = table.cols.time2[:]
month = table.cols.month[:]
dh_mean = table.cols.dh_mean[:]
dh_error = table.cols.dh_error[:]
dg_mean = table.cols.dg_mean[:]
dg_error = table.cols.dg_error[:]
dates = [dt.datetime(y, m, 15) for y, m in zip(time2, month)]
# plot TS
fig = plt.figure()
plt.subplot(211)
plt.errorbar(dates, dh_mean, yerr=dh_error, linewidth=2)
plt.ylabel('dh (m)')
plt.subplot(212)
plt.errorbar(dates, dg_mean, yerr=dg_error, linewidth=2)
plt.ylabel('dAGC (dB)')
fig.autofmt_xdate()
# plot correlation
dg_fit, dh_fit, _ = poly_fit(dg_mean, dh_mean)
plt.figure()
plt.plot(dg_mean, dh_mean, 'o')
plt.plot(dg_fit, dh_fit, linewidth=2.5)
plt.xlabel('dAGC (dB)')
plt.ylabel('dh (m)')
corr = np.corrcoef(dg_mean, dh_mean)[0,1]
print 'correlation = %.2f' % corr
def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10,3))
ax = fig.add_subplot((111))
x = x.round(2)
#n = y_lin[0] # zero at the begining
n = y_lin[0] + (y_lin[-1] - y_lin[0])/2. # zero at the middle
plt.plot(x, y-n, linewidth=3)
#plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n, 'r')
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m*100), loc=1)
viz.add_inner_title(ax, title, loc=2)
plt.ylim(-.3, .3)
plt.xlim(2003.5, 2010.1)
#plt.xlabel('years')
plt.ylabel('Elevation change (m)')
#fig.autofmt_xdate()
if savefig is not None:
pass
#plt.savefig(savefig+'_ts.png')
#np.savetxt(savefig+'_ts.txt', np.column_stack((x, y)))
return fig
def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10,3))
ax = fig.add_subplot((111))
x = x.round(2)
#n = y_lin[0] # reference to zero
nmin = y_lin.min() # reference to center
nmax = y_lin.max()
n = nmin + (nmax - nmin)/2.
plt.plot(x, y-n, linewidth=3)
plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n)
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m*100), loc=4)
#viz.add_inner_title(ax, 'dAGC/dt = %.1f dB/yr' % (m*100), loc=4)
viz.add_inner_title(ax, title, loc=1)
#plt.ylim(-3.5, 3.5)
plt.xlim(1992, 2012.7)
ax.xaxis.set_ticks(range(1992, 2013, 2))
#plt.xlabel('time')
plt.ylabel('dh [m]')
#plt.ylabel('dAGC [dB]')
fig.autofmt_xdate()
if savefig is not None:
plt.savefig(savefig)
plt.show()
return fig
def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10,3))
ax = fig.add_subplot((111))
x = x.round(2)
n = y_lin[0] # reference to zero
plt.plot(x, y-n, linewidth=3)
#plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n, 'r')
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m*100), loc=4)
viz.add_inner_title(ax, title, loc=2)
#plt.ylim(-0.1, 0.1)
plt.xlim(2003.5, 2009.6)
#plt.xlabel('time')
plt.ylabel('dh (m)')
#fig.autofmt_xdate()
if savefig is not None:
plt.savefig(savefig)
plt.show()
return fig
def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10, 3))
ax = fig.add_subplot((111))
x = x.round(2)
#n = y_lin[0] # reference to zero
nmin = y_lin.min() # reference to center
nmax = y_lin.max()
n = nmin + (nmax - nmin) / 2.
plt.plot(x, y - n, linewidth=3)
plt.plot(x_pol, y_pol - n)
plt.plot(x_lin, y_lin - n)
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m * 100), loc=4)
#viz.add_inner_title(ax, 'dAGC/dt = %.1f dB/yr' % (m*100), loc=4)
viz.add_inner_title(ax, title, loc=1)
#plt.ylim(-3.5, 3.5)
plt.xlim(1992, 2012.7)
ax.xaxis.set_ticks(range(1992, 2013, 2))
#plt.xlabel('time')
plt.ylabel('dh [m]')
#plt.ylabel('dAGC [dB]')
fig.autofmt_xdate()
if savefig is not None:
plt.savefig(savefig)
plt.show()
return fig
def plot_ts(table):
"""
Plot dh and dAGC time series and the correlation dAGC x dh.
"""
sys.path.append('/Users/fpaolo/code/misc')
from util import poly_fit
# load data from Table
time2 = table.cols.time2[:]
month = table.cols.month[:]
dh_mean = table.cols.dh_mean[:]
dh_error = table.cols.dh_error[:]
dg_mean = table.cols.dg_mean[:]
dg_error = table.cols.dg_error[:]
dates = [dt.datetime(y, m, 15) for y, m in zip(time2, month)]
# plot TS
fig = plt.figure()
plt.subplot(211)
plt.errorbar(dates, dh_mean, yerr=dh_error, linewidth=2)
plt.ylabel('dh (m)')
plt.subplot(212)
plt.errorbar(dates, dg_mean, yerr=dg_error, linewidth=2)
plt.ylabel('dAGC (dB)')
fig.autofmt_xdate()
# plot correlation
dg_fit, dh_fit, _ = poly_fit(dg_mean, dh_mean)
plt.figure()
plt.plot(dg_mean, dh_mean, 'o')
plt.plot(dg_fit, dh_fit, linewidth=2.5)
plt.xlabel('dAGC (dB)')
plt.ylabel('dh (m)')
corr = np.corrcoef(dg_mean, dh_mean)[0, 1]
print 'correlation = %.2f' % corr
def plot_ts(ax, x, y, num=1):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
x = x.round(2)
n = y_lin[0] # reference to zero
ax.plot(x, y-n, linewidth=2.5)
#ax.plot(x_pol, y_pol-n)
ax.plot(x_lin, y_lin-n, 'r')
if num != 7:
viz.add_inner_title(ax, '(%d)' % num, loc=2)
viz.add_inner_title(ax, 'trend = %.1f cm/yr' % (m*100), loc=3)
return ax
def plot_ts(x, y):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
x = x.round(2)
n = y_lin[0] # reference to zero
plt.plot(x, y-n, linewidth=2)
plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m*100), loc=2)
#plt.ylim(-0.8, 0.8)
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.show()
return fig
def plot_ts(x, y):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
x = x.round(2)
n = y_lin[0] # reference to zero
plt.plot(x, y - n, linewidth=2)
plt.plot(x_pol, y_pol - n)
plt.plot(x_lin, y_lin - n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m * 100), loc=2)
#plt.ylim(-0.8, 0.8)
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.show()
return fig
def plot_ts_mean(t, Y):
y = np.ma.masked_invalid(Y)
ts = np.mean(np.mean(y, axis=1), axis=1)
#x_pol, y_pol = spline_interp(t, ts, smooth=0.1)
x_pol, y_pol = util.poly_fit(t, ts, order=3)
x_lin, y_lin = linear_fit(t, ts)
m, c = linear_fit(t, ts, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
n = y_lin[0] # shift
plt.plot(t, ts-n, linewidth=2)
plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m*100), loc=2)
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.title('Mean TS')
plt.show()
return fig
def plot_ts_mean(t, Y):
y = np.ma.masked_invalid(Y)
ts = np.mean(np.mean(y, axis=1), axis=1)
#x_pol, y_pol = spline_interp(t, ts, smooth=0.1)
x_pol, y_pol = util.poly_fit(t, ts, order=3)
x_lin, y_lin = linear_fit(t, ts)
m, c = linear_fit(t, ts, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
n = y_lin[0] # shift
plt.plot(t, ts - n, linewidth=2)
plt.plot(x_pol, y_pol - n)
plt.plot(x_lin, y_lin - n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m * 100), loc=2)
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.title('Mean TS')
plt.show()
return fig
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.title('Mean TS')
plt.show()
return fig
def plot_ts_full(t, Y, (i,j)):
y = np.ma.masked_invalid(Y)
mean_t = np.mean(np.mean(y, axis=1), axis=1)
mean_xy = np.mean(y, axis=0)
ts = mean_t + mean_xy[i,j] + Y[:,i,j]
#x_pol, y_pol = spline_interp(t, ts, smooth=0.1)
x_pol, y_pol = util.poly_fit(t, ts, order=3)
x_lin, y_lin = linear_fit(t, ts)
m, c = linear_fit(t, ts, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
n = y_lin[0] # shift
plt.plot(t, ts-n, linewidth=2)
plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m*100), loc=2)
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
def get_arterial_compliance(
flow, low_low_pass_gage, low_pass_gage, amb_temperature, amb_pressure, MAP=None, SBP=None, DBP=None, cba=None
):
pressure = amb_pressure + low_pass_gage
flow_fit, cba = flow_of_gage(flow, low_low_pass_gage, cba)
corrected_flow = flow_correction(flow_fit, low_low_pass_gage, amb_pressure, amb_temperature)
t = arange(len(corrected_flow)) * defaults["dt"]
wvol = cumsum(flow_fit) * defaults["dt"] / 60.0 ### best fit
t = arange(len(wvol)) * defaults["dt"]
keep = low_low_pass_gage < 30
x = flow / 60.0
y = low_low_pass_gage
k = 3.0
A = x ** k
b = dot(A, y) / dot(A, A)
yfit = A * b
xfit = (y / b) ** (1.0 / k)
t = arange(len(y)) * defaults["dt"]
my_gage = arange(160)
c, b, a = cba
cuff_compl = corrected_flow[:-1] * (defaults["dt"] / diff(low_low_pass_gage)) / 60.0
bpf = low_pass_gage - low_low_pass_gage
v_of_p = interp.interp1d(low_low_pass_gage[::-1], wvol[::-1], bounds_error=False)
peaks, troughs = util.find_pulse_peaks_and_troughs(bpf, 1, 0.005)
if peaks[0] < troughs[0]: # peak follows trough
peaks = peaks[1:]
if troughs[-1] > peaks[-1]: # peak follows trough
troughs = troughs[:-1]
hills = []
for trough, peak in zip(troughs, peaks): ## filter loong pulses
if (peak - trough) * defaults["dt"] < MAX_PULSE:
hills.append((trough, peak))
assert len(peaks) == len(troughs)
hills = array(hills)
troughs = hills[:, 0]
peaks = hills[:, 1]
delta_vs = []
deltav_ps = []
deltav_ts = []
cuff_pressures = []
for t, p in hills:
cuff_pressure = (low_low_pass_gage[p] + low_low_pass_gage[t]) / 2.0
delta_v = ideal_gas_compliance(low_low_pass_gage[p], wvol[-1]) * (bpf[p] - bpf[t])
delta_vs.append(delta_v)
cuff_pressures.append(cuff_pressure)
p = arange(v_of_p.x[0], v_of_p.x[-1], 1)
dv_dp_cuff = diff(v_of_p(p)) / diff(p)
A = 1.0 / p[1:]
y = dv_dp_cuff
c = dot(A, y) / dot(A, A)
### find largest delta for p6 fit.
deltas = bpf[peaks] - bpf[troughs]
candidate_deltas = [d for idx, d in zip(peaks, deltas) if low_low_pass_gage[idx] > 60] ## MAP must be above 60mmhg.
n_peak = argmax(candidate_deltas) * 2 + 1
# n_peak = len(peaks) - 1
if n_peak >= len(peaks):
n_peak = len(peaks)
# n_peak = argmax(bpf[peaks] - bpf[troughs]) * 3
if n_peak < 15:
if len(peaks) < 15:
n_peak = len(peaks) - 1
else:
n_peak = 15
assert n_peak <= len(peaks)
deltas = bpf[peaks] - bpf[troughs]
idx = arange(len(deltas))
# plot(idx, deltas)
if False: ## filter out noisy peaks?
keep = util.filter_deltas(idx, deltas, 0.3)
else: # keep all
keep = idx
# plot(keep, deltas[keep])
hills = hills[keep]
troughs = troughs[keep]
peaks = peaks[keep]
deltas = deltas[keep]
delta_vs = array(delta_vs)[keep]
max_delta_i = argmax(deltas)
if MAP is None:
# MAP = low_pass_gage[hills[max_delta_i][1]]
### compute map6
MAP_ii = argmax(deltas)
MAP_i = hills[MAP_ii][1]
# MAP = low_low_pass_gage[MAP_i]
if True:
p6 = util.poly_fit(troughs[:n_peak] * defaults["dt"], deltas[:n_peak], 6) ### Actual
# p6 = util.poly_fit(troughs[:] * defaults['dt'], deltas[:], 6) ### Test
else:
### put a zero in the first element to encourage a negative leading coeff.
_peaks = zeros(n_peak + 1)
_deltas = zeros(n_peak + 1)
_peaks[1:] = peaks[:n_peak]
_deltas[1:] = deltas[:n_peak]
p6 = util.poly_fit(_peaks * defaults["dt"], _deltas, 6)
figure(620)
ax = subplot(311)
ylabel("Gage mmHG")
plot(arange(len(bpf)) * defaults["dt"], low_pass_gage)
plot(arange(len(bpf)) * defaults["dt"], low_low_pass_gage)
subplot(313, sharex=ax)
ylabel("Bandpass mmHG")
plot(arange(len(bpf)) * defaults["dt"], bpf)
plot(peaks * defaults["dt"], bpf[peaks], "ro")
plot(troughs * defaults["dt"], bpf[troughs], "bo")
subplot(312, sharex=ax)
ylabel("Deltas mmHG")
xlabel("Time Seconds")
plot(peaks * defaults["dt"], deltas)
plot(peaks[:n_peak] * defaults["dt"], deltas[:n_peak], "bd")
ylim(0, 6)
p5 = util.poly_der(p6)
# map_time = util.find_zero(p5, hills[MAP_ii][1] * defaults['dt'])
assert n_peak <= len(peaks), "n_peaks > len(peaks)"
t = arange(peaks[0] * defaults["dt"], peaks[n_peak - 1] * defaults["dt"], 0.1)
y = util.poly_eval(p6, t)
map_time = t[argmax(y)]
plot([map_time], [max(y)], "ro")
# t = arange(0, 3 * map_time, .1)
# y = util.poly_eval(p6, t)
plot(t, y, "r-")
map_idx = int(map_time / defaults["dt"])
MAP6 = low_low_pass_gage[map_idx]
sbp_target = 0.55 * util.poly_eval(p6, map_time)
dbp_target = 0.85 * util.poly_eval(p6, map_time)
plot([map_time, map_time], [0, util.poly_eval(p6, map_time)], "r--")
plot([0, t[-1]], [dbp_target, dbp_target], "r--")
plot([0, t[-1]], [sbp_target, sbp_target], "r--")
#### find SBP time
t = arange(map_time, 0, -0.01) ### time going backwards from MAP
pt = util.poly_eval(p6, t) - sbp_target
sbp_time = t[where(pt < 0)[0][0]]
#### find DBP time
t = arange(map_time, 30 * map_time, 0.01)
pt = util.poly_eval(p6, t) - dbp_target
dbp_time = t[where(pt < 0)[0][0]]
plot([dbp_time, dbp_time], [0, dbp_target], "r--")
plot([sbp_time, sbp_time], [0, sbp_target], "r--")
assert sbp_time < map_time, "sbp_time = %s > %s = MAP" % (sbp_time, map_time)
assert map_time < dbp_time, "dbp_time = %s < %s = MAP" % (dbp_time, map_time)
sbp_idx = int(sbp_time / defaults["dt"])
dbp_idx = int(dbp_time / defaults["dt"])
figure(124)
plot(peaks[:n_peak] * defaults["dt"], deltas[:n_peak])
plot([map_time], [util.poly_eval(p6, map_time)], "go")
plot([dbp_time], [util.poly_eval(p6, dbp_time)], "bo")
plot([sbp_time], [util.poly_eval(p6, sbp_time)], "ro")
# plot(arange(0, peaks[n_peak] * defaults['dt'], 1), util.poly_eval(p6, arange(0, peaks[n_peak] * defaults['dt'], 1)))
plot([0, 25], [dbp_target, dbp_target], "g--")
plot([0, 25], [sbp_target, sbp_target], "r--")
__SBP = low_low_pass_gage[sbp_idx]
__DBP = low_low_pass_gage[dbp_idx]
figure(620)
subplot(311)
plot([sbp_idx * defaults["dt"], sbp_idx * defaults["dt"]], [0, __SBP], "r--")
plot([dbp_idx * defaults["dt"], dbp_idx * defaults["dt"]], [0, __DBP], "r--")
plot([0, sbp_time], [low_low_pass_gage[sbp_idx], low_low_pass_gage[sbp_idx]], "r--")
plot([0, dbp_time], [low_low_pass_gage[dbp_idx], low_low_pass_gage[dbp_idx]], "r--")
figure(620)
subplot(311)
title("%d/%d" % (__SBP, __DBP))
d = os.path.split(filename)[0]
d_bp_fn = os.path.join(d, "derived_BP.png")
savefig(d_bp_fn)
if False:
subplot(312)
plot([sbp_idx * defaults["dt"], sbp_idx * defaults["dt"]], [-3, 3])
plot([dbp_idx * defaults["dt"], dbp_idx * defaults["dt"]], [-3, 3])
# assert abs(util.poly_eval(p6, sbp_time) - sbp_target) < 1e-6
ii = util.edit_deltas(delta_vs)
ii = range(len(delta_vs))
delta_vs = [delta_vs[i] for i in ii]
cuff_pressures = [cuff_pressures[i] for i in ii]
cuff_pressures = array(cuff_pressures)
if MAP is None:
MAP = MAP6
SBP = __SBP
DBP = __DBP
transmuralP = MAP - cuff_pressures
__deltas, __MAP, __SBP, __DBP, HR = util.get_edited_deltas(low_pass_gage, bpf, PEAK_HEIGHT)
if SBP is None:
SBP = __SBP
if DBP is None:
DBP = __DBP
assert SBP > DBP, "%.2f = SBP <= DBP = %.2f" % (SBP, DBP)
arterial_compliance = abs(array(delta_vs) / (SBP - DBP))
figure(7)
plot(transmuralP, arterial_compliance)
return arterial_compliance, transmuralP, SBP, DBP, MAP, HR, cba
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.title('Mean TS')
plt.show()
return fig
def plot_ts_full(t, Y, (i, j)):
y = np.ma.masked_invalid(Y)
mean_t = np.mean(np.mean(y, axis=1), axis=1)
mean_xy = np.mean(y, axis=0)
ts = mean_t + mean_xy[i, j] + Y[:, i, j]
#x_pol, y_pol = spline_interp(t, ts, smooth=0.1)
x_pol, y_pol = util.poly_fit(t, ts, order=3)
x_lin, y_lin = linear_fit(t, ts)
m, c = linear_fit(t, ts, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
n = y_lin[0] # shift
plt.plot(t, ts - n, linewidth=2)
plt.plot(x_pol, y_pol - n)
plt.plot(x_lin, y_lin - n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m * 100), loc=2)
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')