def mrtm_k2p(tac, dt, inputf1, k2p,
linear_phase_start, linear_phase_end, fig, fig_name):
if linear_phase_start is None:
linear_phase_start = 0
if linear_phase_end is None:
linear_phase_end = np.amax(dt)
mft = kt.dt2mft(dt)
mft = np.append(0, mft)
dt_new = np.array([mft[:-1], mft[1:]])
tdur = kt.dt2tdur(dt_new)
input_dt = kt.int2dt(inputf1,dt)
tac = np.append(0, tac)
input_dt = np.append(0, input_dt)
tac_cum = np.cumsum((tac[:-1] + tac[1:]) / 2 * tdur)
input_cum = np.cumsum((input_dt[:-1] + input_dt[1:]) / 2 * tdur)
tac = tac[1:]
input_dt = input_dt[1:]
yy = tac
xx = np.column_stack((input_cum + 1 / k2p * input_dt, tac_cum))
tt = np.logical_and(mft > linear_phase_start, mft < linear_phase_end)
tt = tt[1:]
mft = mft[1:]
reg = LinearRegression(fit_intercept=False).fit(xx[tt, ], yy[tt])
bp = - reg.coef_[0]/reg.coef_[1] - 1
yyf = reg.predict(xx)
if fig:
plt.cla()
plt.plot(mft, yy, '.')
plt.plot(mft, yyf, 'r')
if fig_name is not None:
plt.savefig(fig_name)
plt.show()
kps = {'bp': bp}
return kps
def mrtm(tac, dt, inputf1, linear_phase_start, linear_phase_end, fig):
if linear_phase_start is None:
linear_phase_start = 0
if linear_phase_end is None:
linear_phase_end = np.amax(dt)
mft = kt.dt2mft(dt)
mft = np.append(0, mft)
dt_new = np.array([mft[:-1], mft[1:]])
tdur = kt.dt2tdur(dt_new)
input_dt = kt.int2dt(inputf1, dt)
tac = np.append(0, tac)
input_dt = np.append(0, input_dt)
tac_cum = np.cumsum((tac[:-1] + tac[1:]) / 2 * tdur)
input_cum = np.cumsum((input_dt[:-1] + input_dt[1:]) / 2 * tdur)
tac = tac[1:]
input_dt = input_dt[1:]
yy = tac
xx = np.column_stack((input_cum, tac_cum, input_dt))
tt = np.logical_and(mft > linear_phase_start, mft < linear_phase_end)
tt = tt[1:]
mft = mft[1:]
reg = LinearRegression(fit_intercept=False).fit(xx[tt, ], yy[tt])
bp = -reg.coef_[0] / reg.coef_[1] - 1
k2p = reg.coef_[0] / reg.coef_[2]
# for 1 TC
r1 = reg.coef_[2]
k2 = -reg.coef_[1]
yyf = reg.predict(xx)
if fig:
plt.plot(mft, yy, '.')
plt.plot(mft, yyf, 'r')
plt.show()
kps = {'bp': bp, 'k2p': k2p, 'r1': r1, 'k2': k2}
return kps
def logan_ref_k2p(tac, dt, inputf1, k2p, linear_phase_start, linear_phase_end,
fig):
if linear_phase_start is None:
linear_phase_start = 0
if linear_phase_end is None:
linear_phase_end = np.amax(dt)
mft = kt.dt2mft(dt)
mft = np.append(0, mft)
dt_new = np.array([mft[:-1], mft[1:]])
tdur = kt.dt2tdur(dt_new)
input_dt = kt.int2dt(inputf1, dt)
tac = np.append(0, tac)
input_dt = np.append(0, input_dt)
tac_cum = np.cumsum((tac[:-1] + tac[1:]) / 2 * tdur)
input_cum = np.cumsum((input_dt[:-1] + input_dt[1:]) / 2 * tdur)
tac = tac[1:]
input_dt = input_dt[1:]
yy = np.zeros(tac.shape)
xx = np.zeros(tac.shape)
mask = tac.nonzero()
yy[mask] = tac_cum[mask] / tac[mask]
xx[mask] = (input_cum[mask] + input_dt[mask] / k2p) / tac[mask]
tt = np.logical_and(mft > linear_phase_start, mft < linear_phase_end)
tt = tt[1:]
dvr, inter, _, _, _ = linregress(xx[tt], yy[tt])
bp = dvr - 1
yyf = dvr * xx + inter
if fig:
plt.plot(xx, yy, '.')
plt.plot(xx[tt], yyf[tt], 'r')
plt.show()
kps = {'bp': bp}
return kps
def mrtm(tac, dt, inputf1, linear_phase_start, linear_phase_end, fig):
if linear_phase_start is None:
linear_phase_start = 0
if linear_phase_end is None:
linear_phase_end = np.amax(dt)
# fill the coffee break gap
if kt.dt_has_gaps(dt):
tac, dt = kt.tac_dt_fill_coffee_break(tac, dt)
mft = kt.dt2mft(dt)
mft = np.append(0, mft)
dt_new = np.array([mft[:-1], mft[1:]])
tdur = kt.dt2tdur(dt_new)
input_dt = kt.int2dt(inputf1, dt)
tac = np.append(0, tac)
input_dt = np.append(0, input_dt)
# set negative values to zero
tac[tac < 0] = 0.0
input_dt[input_dt < 0] = 0.0
tac_cum = np.cumsum((tac[:-1] + tac[1:]) / 2 * tdur)
input_cum = np.cumsum((input_dt[:-1] + input_dt[1:]) / 2 * tdur)
tac = tac[1:]
input_dt = input_dt[1:]
yy = tac
xx = np.column_stack((input_cum, tac_cum, input_dt))
# find tt for the linear phase
tt = np.logical_and(mft >= linear_phase_start, mft <= linear_phase_end)
tt = tt[1:]
# select tt for tac > 0
tt = np.logical_and(tt, tac > 0)
# select tt for xx < inf, yy < inf
infinf = 1e10
tt = np.logical_and(tt, np.all(xx < infinf, axis=-1))
tt = np.logical_and(tt, yy < infinf)
mft = mft[1:]
reg = LinearRegression(fit_intercept=False).fit(xx[tt, ], yy[tt])
bp = -reg.coef_[0] / reg.coef_[1] - 1
k2p = reg.coef_[0] / reg.coef_[2]
# for 1 TC
r1 = reg.coef_[2]
k2 = -reg.coef_[1]
yyf = reg.predict(xx)
if fig:
plt.plot(mft, yy, '.')
plt.plot(mft, yyf, 'r')
plt.show()
kps = {'bp': bp, 'k2p': k2p, 'r1': r1, 'k2': k2}
return kps
def logan_ref_k2p(tac, dt, inputf1, k2p, linear_phase_start, linear_phase_end,
fig):
if linear_phase_start is None:
linear_phase_start = 0
if linear_phase_end is None:
linear_phase_end = np.amax(dt)
# fill the coffee break gap
if kt.dt_has_gaps(dt):
tac, dt = kt.tac_dt_fill_coffee_break(tac, dt)
mft = kt.dt2mft(dt)
mft = np.append(0, mft)
dt_new = np.array([mft[:-1], mft[1:]])
tdur = kt.dt2tdur(dt_new)
input_dt = kt.int2dt(inputf1, dt)
tac = np.append(0, tac)
input_dt = np.append(0, input_dt)
# set negative values to zero
tac[tac < 0] = 0.0
input_dt[input_dt < 0] = 0.0
tac_cum = np.cumsum((tac[:-1] + tac[1:]) / 2 * tdur)
input_cum = np.cumsum((input_dt[:-1] + input_dt[1:]) / 2 * tdur)
tac = tac[1:]
input_dt = input_dt[1:]
yy = tac_cum / (tac + 0.0000000000000001)
xx = (input_cum + input_dt / k2p) / (tac + 0.0000000000000001)
# find tt for the linear phase
tt = np.logical_and(mft >= linear_phase_start, mft <= linear_phase_end)
tt = tt[1:]
# select tt for tac > 0
tt = np.logical_and(tt, tac > 0)
# select tt for xx < inf, yy < inf
infinf = 1e10
tt = np.logical_and(tt, xx < infinf)
tt = np.logical_and(tt, yy < infinf)
# do linear regression with selected tt
xx = xx[tt]
yy = yy[tt]
dvr, inter, _, _, _ = linregress(xx, yy)
bp = dvr - 1
yyf = dvr * xx + inter
if fig:
plt.plot(xx, yy, '.')
plt.plot(xx[tt], yyf[tt], 'r')
plt.show()
kps = {'bp': bp}
return kps
def exp_am(tac, dt, idx, fig):
tac[tac < 0] = 0.0
mft = kt.dt2mft(dt)
p0 = (0.1, 1, 0.1)
# p, _ = curve_fit(exp_1_fun_t, mft[idx], tac[idx], p0=p0, bounds=(0.00000001, 2500))
p, _ = curve_fit(exp_1_fun_t, mft[idx], tac[idx], p0=p0)
a0, a1, b1 = p
t1 = np.arange(np.amax(dt))
tac1f = exp_1_fun_t(t1, a0, a1, b1)
if fig:
print(p)
plt.plot(t1, tac1f, 'b', mft, tac, 'go')
plt.show()
return tac1f, p
def feng_srtm(tac, dt, w, fig):
ts_te_w = (dt[0, ], dt[1, ], w)
p0 = [3.90671734e+00, 4.34910151e+02, 9.22189828e+01, 1.35949657e-02, 4.56109635e-02, 4.53841116e-02, 4.54180443e-02, 7.71163349e-04]
p, _ = curve_fit(feng_srtm_fun, ts_te_w, tac*w, p0)
a0, a1, a2, a3, b0, b1, b2, b3 = p
t1 = np.arange(np.amax(dt))
tac1f = feng_srtm_fun_t(t1, a0, a1, a2, a3, b0, b1, b2, b3)
if fig:
print(p)
cp1f = feng_fun_t(t1, a0, a1, a2, b0, b1, b2)
mft = kt.dt2mft(dt)
plt.plot(t1, cp1f, 'r', t1, tac1f, 'b', mft, tac, 'go')
plt.show()
return tac1f, p
def exp_2(tac, dt, idx, w, fig):
if w is None:
w = np.ones_like(tac)
ts_te_w = (dt[0, idx], dt[1, idx], w[idx])
p0 = (1, 1, 1, 0, 0)
p, _ = curve_fit(exp_2_fun, ts_te_w, tac[idx]*w[idx], p0)
a0, a1, a2, b1, b2 = p
t1 = np.arange(np.amax(dt))
tac1f = exp_2_fun_t(t1, a0, a1, a2, b1, b2)
if fig:
print(p)
mft = kt.dt2mft(dt)
plt.plot(t1, tac1f, 'b', mft, tac, 'go')
plt.show()
return tac1f, p
def exp_1(tac, dt, idx, w, fig):
tac[tac < 0] = 0.0
if w is None:
w = np.ones_like(tac)
ts_te_w = (dt[0, idx], dt[1, idx], w[idx])
p0 = (1000, 5000, 10)
p, _ = curve_fit(exp_1_fun, ts_te_w, tac[idx] * w[idx], p0)
a0, a1, b1 = p
t1 = np.arange(np.amax(dt))
tac1f = exp_1_fun_t(t1, a0, a1, b1)
if fig:
print(p)
mft = kt.dt2mft(dt)
plt.plot(t1, tac1f, 'b', mft, tac, 'go')
plt.show()
return tac1f, p
def feng_srtm(tac, dt, w, fig):
tac[tac < 0] = 0.0
if w is None:
w = 1
ts_te_w = (dt[0, ], dt[1, ], w)
# p0 = [3.90671734e+00, 4.34910151e+02, 9.22189828e+01, 1.35949657e-02, 4.56109635e-02, 4.53841116e-02, 4.54180443e-02, 7.71163349e-04]
p0 = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
# p0 = [6.85579165e-05, -2.08643110e+01, -1.81889002e+02, 7.16906660e+00, 4.21217390e-04, 7.23514957e-02, 7.84986975e-02, 8.27340347e-02]
p, _ = curve_fit(feng_srtm_fun, ts_te_w, tac * w, p0=p0)
a0, a1, a2, a3, b0, b1, b2, b3 = p
t1 = np.arange(np.amax(dt))
tac1f = feng_srtm_fun_t(t1, a0, a1, a2, a3, b0, b1, b2, b3)
print(tac1f)
if fig:
print(p)
cp1f = feng_fun_t(t1, a0, a1, a2, b0, b1, b2)
mft = kt.dt2mft(dt)
plt.plot(t1, cp1f, 'r', t1, tac1f, 'b', mft, tac, 'go')
plt.show()
return tac1f, p
'inputf1': ref.inputf1cubic,
'w': None,
'r1': 0.905,
'k2p': 0.00025,
'beta_lim': beta_lim,
'n_beta': n_beta,
'b': b
}
user_inputs_fill_gaps = {'b': b_fill_gaps,
'inputf1_dt': inputf1_dt_fill_gaps
}
km_outputs = ['R1', 'k2', 'BP']
tac = TAC(ref.tac, dt)
for model_name in models:
model_inputs = get_model_inputs(user_inputs, model_name)
tac.run_model(model_name, model_inputs)
# make
tac.km_results.update(user_inputs_fill_gaps)
km_inputs = tac.km_results
model_km_inputs = get_model_inputs(km_inputs, model_name+ '_para2tac')
tac.run_model_para2tac(model_name + '_para2tac', model_km_inputs)
plt.plot(tac.mft, tac.tac, 'b*', label='tac')
plt.plot(dt2mft(dt_fill_gaps), tac.km_results['tacf'], 'r', label='fit')
plt.legend()
plt.show()
def logan_ref(tac, dt, inputf1, linear_phase_start, linear_phase_end, fig):
if linear_phase_start is None:
linear_phase_start = 0
if linear_phase_end is None:
linear_phase_end = np.amax(dt)
# fill the coffee break gap
if kt.dt_has_gaps(dt):
tac, dt = kt.tac_dt_fill_coffee_break(tac, dt)
mft = kt.dt2mft(dt)
mft = np.append(0, mft)
dt_new = np.array([mft[:-1], mft[1:]])
tdur = kt.dt2tdur(dt_new)
input_dt = kt.int2dt(inputf1, dt)
tac = np.append(0, tac)
input_dt = np.append(0, input_dt)
# set negative values to zero
tac[tac < 0] = 0.0
input_dt[input_dt < 0] = 0.0
# calculate integration
tac_cum = np.cumsum((tac[:-1] + tac[1:]) / 2 * tdur)
input_cum = np.cumsum((input_dt[:-1] + input_dt[1:]) / 2 * tdur)
# tac_cum and input_cum are calculated in such a way to match tac
# # # debug
# file_path = '/Users/Himiko/data/amsterdam_data_pib/transfer_162391_files_89f2cba1/'
# savetxt(file_path + 'tac_cum.csv', tac_cum)
# savetxt(file_path + 'input_cum.csv', input_cum)
#
# tac1 = kt.interpt1(mft, tac, dt)
# input1 = kt.interpt1(mft, input_dt, dt)
# tac_cum1 = np.cumsum(tac1)
# input_cum1 = np.cumsum(input1)
# tac_cum1_mft = tac_cum1[mft.astype(int)]
# input_cum1_mft = input_cum1[mft.astype(int)]
# savetxt(file_path + 'tac_cum1_mft.csv', tac_cum1_mft)
# savetxt(file_path + 'input_cum1_mft.csv', input_cum1_mft)
# # # debug
tac = tac[1:]
input_dt = input_dt[1:]
# yy = np.zeros(tac.shape)
# xx = np.zeros(tac.shape)
# mask = tac.nonzero()
# yy[mask] = tac_cum[mask] / tac[mask]
# xx[mask] = input_cum[mask] / tac[mask]
yy = tac_cum / (tac + 0.0000000000000001) # ADDED BY MY 20210616
xx = input_cum / (tac + 0.0000000000000001) # ADDED BY MY 20210616
# find tt for the linear phase
tt = np.logical_and(mft >= linear_phase_start, mft <= linear_phase_end)
tt = tt[1:]
# select tt for tac > 0
tt = np.logical_and(tt, tac > 0)
# select tt for xx < inf, yy < inf
infinf = 1e10
tt = np.logical_and(tt, xx < infinf)
tt = np.logical_and(tt, yy < infinf)
# do linear regression with selected tt
xx = xx[tt]
yy = yy[tt]
dvr, inter, _, _, _ = linregress(xx, yy)
bp = dvr - 1
yyf = dvr * xx + inter
if fig:
plt.plot(xx, yy, '.')
plt.plot(xx, yyf, 'r')
plt.show()
kps = {'bp': bp}
return kps