def gluco_plot(time,
gluco,
hypo=70,
hyper=126,
title="Patiend ID glucose level"):
"""Plot the glucose level on a time span.
Parameters
-------------------
time : array of datetimes, the horizontal axis
gluco : array of float, the correspondent glucose level on the vertical axis
hypo : number, hypoglicaemia threshold in mg/dl (default is 70, i.e. 3.9 mmol/l)
hyper : number, hyperglicaemia threshold in mg/dl (default is 126, i.e. 7 mmol/l)
title : string, the title of the plot (optional)
"""
plt.figure(figsize=(10, 6))
plt.hlines(hypo,
time[0],
time[-1],
linestyles='dashed',
label='hypoglicaemia')
plt.hlines(hyper,
time[0],
time[-1],
linestyles='dotted',
label='hyperglicaemia')
plt.ylim([10, 410])
plt.plot_date(time, gluco, '-', label='glucose level')
plt.title(title)
plt.ylabel('mg/dL')
plt.xticks(rotation='vertical')
plt.legend()
def plot_aucs(test_name, train_aucs, test_aucs):
fig = plt.figure(figsize=(8, 8))
axes = fig.gca()
axes.plot(
np.arange(N_EPOCHS), train_aucs)
axes.plot(
np.arange(N_EPOCHS), test_aucs)
plt.xlabel("epoch")
plt.ylabel("AUC")
plt.xlim(0, 15)
plt.ylim(0.5, 1.0)
plt.legend(["train", "test (%s)" % test_name])
fig.savefig("auc_%s.png" % test_name)
def cgm(df,
gluco_fit=None,
hypo=70,
hyper=126,
title="Patiend ID CGM",
savefig=False):
"""Plot the CGM signal on an input time span.
Parameters
-------------------
df : DataFrame, the output returned by gluco_extract(..., return_df=True)
gluco_fit : array of float, the results of a fitted model (optional)
hypo : number, hypoglicaemia threshold in
mg/dl (default is 70, i.e. 3.9 mmol/l)
hyper : number, hyperglicaemia threshold
in mg/dl (default is 126, i.e. 7 mmol/l)
title : string, the title of the plot (optional)
savefig : bool, if True save title.png
"""
plt.figure(figsize=(10, 6), dpi=300)
plt.hlines(hypo,
df.index[0],
df.index[-1],
linestyles='dashed',
label='hypoglicaemia')
plt.hlines(hyper,
df.index[0],
df.index[-1],
linestyles='dotted',
label='hyperglicaemia')
plt.ylim([10, 410])
plt.plot_date(df.index, df.as_matrix(), '-', label='real CGM')
if gluco_fit is not None:
plt.plot(df.index, gluco_fit, '--', label='predicted CGM')
plt.title(title)
plt.ylabel('mg/dL')
plt.xticks(rotation='vertical')
plt.legend(bbox_to_anchor=(1.1, 1.0))
if savefig: plt.savefig(title + '_fit.png')
# Pold = np.array(Pnew)
# Xold = np.array(Xnew)
y[t] = np.dot(Xnew[t, :], H)
# y[t] = np.dot(Xnew,H)
# ---------------------------------------- #
# In[36]:
# ---------- Plot ------------------------ #
plt.subplot(211)
plt.plot(ts, ys, '-', label='noisy') # noisy measures
plt.plot(ts, y, '--', label='filtered') # filtered measures
plt.grid()
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.)
plt.subplot(212)
residuals = ys.ravel()[0:Ns] - y.ravel()[0:Ns]
plt.plot(ts, residuals)
plt.title('Durbin-Watson: {:.3f}'.format(sm.stats.durbin_watson(residuals)))
plt.tight_layout()
plt.grid()
# ## Translate `pykalman` names into our convention
#
# **Parameter Name** (Notation)
# - initial_state_mean (`X0`)
# - initial_state_covariance (`P0`)
# - transition_matrices (`F`)
obs_errors = [100, 1.0, 1.0, 1.0, 0.001]
levels = [0.95, 0.835, 0.685, 0.51, 0.34, 0.2, 0.095, 0.025]
plt.rc('lines', linewidth=1.0)
plt.figure(figsize=(9, 3), facecolor='white')
dirs = ['double']
labels = ['64 bits']
handles = []
for d, l in zip(dirs, labels):
print(f'Plotting {d}')
handles += plot(d, l)
plt.ylim([0, 3.5])
plt.xlabel('Time')
plt.ylabel(f'Total analysis RMSE')
plt.title('')
# Add legend
leg = plt.legend(handles=handles, frameon=True, ncol=2)
rect = leg.get_frame()
rect.set_linewidth(0.0)
rect.set_alpha(0.7)
plt.savefig(f'total_error_compare.pdf', bbox_inches='tight')
plt.show()
# Get axis limits and equalise
xlim = plt.gca().get_xlim()
ylim = plt.gca().get_ylim()
xlim = ylim = [min(xlim[0], ylim[0]), max(xlim[1], ylim[1])]
plt.xlim(xlim)
plt.ylim(ylim)
# Plot diagonal line
plt.plot(xlim, ylim, ls="--", c=".3")
plt.title('')
plt.xlabel('RMSE')
plt.ylabel('Spread')
# Legend for symbol
for field in fields:
plt.scatter(-1, -1, s=90, marker=field[2], color='.3', label=field[1])
# Legend for color
plot_lines = [plt.plot([-1,-1.5], [-1,-1.5], color=colors[0])[0]]
plot_lines.append(plt.plot([-1,-1.5], [-1,-1.5], color=colors[1])[0])
col_leg = plt.legend(plot_lines, labels, loc=6, fontsize=12)
# Add both legends
plt.gca().add_artist(col_leg)
plt.legend(loc=2, fontsize=12)
plt.savefig(f'scatter_all_fields.pdf', bbox_inches='tight')
plt.show()
# perform moving-window arma
errs, forecast = moving_window_ARIMA(df,
w_size=w_size,
ph=ph,
p=p,
d=d,
q=q,
start_params=None,
verbose=True)
# In[13]:
# plot results
gluco_plot(df.index, df.as_matrix(), title='Patient ' + str(k))
plt.plot(df.index, forecast['ts'], linestyle='dashed', label='forecast')
plt.legend(bbox_to_anchor=(1.2, 1.0))
MAE_6 = np.mean(errs['err_6'])
MAE_12 = np.mean(errs['err_12'])
MAE_18 = np.mean(errs['err_18'])
RMSE_6 = np.linalg.norm(errs['err_6']) / np.sqrt(len(errs['err_6']))
RMSE_12 = np.linalg.norm(errs['err_12']) / np.sqrt(len(errs['err_12']))
RMSE_18 = np.linalg.norm(errs['err_18']) / np.sqrt(len(errs['err_18']))
print("MAE (30') = {:2.3f}\t|\tMAE (60') = {:2.3f}\t|\tMAE (90') = {:2.3f}".
format(MAE_6, MAE_12, MAE_18))
print("RMSE (30') = {:2.3f}\t|\tRMSE (60') = {:2.3f}\t|\tRMSE (90') = {:2.3f}".
format(RMSE_6, RMSE_12, RMSE_18))
# In[14]:
residuals = df.as_matrix()[w_size:-ph].ravel() - forecast['ts'][w_size:-ph]
fig = plt.figure(figsize=(12, 4))
}
vert_secs = [(1.0, 0.5), (0.5, 0.2), (0.2, 0.0)]
field = ' '.join(fields[argv[2]])
plt.figure(figsize=(5, 5), facecolor='white')
dirs = ['double', 'reduced']
labels = ['64 bits', '22 bits']
handles = []
for d, l in zip(dirs, labels):
print(f'Plotting {d}')
handles += plot(d, field, vert_secs[int(argv[3])], l)
plt.xlabel('Latitude')
plt.ylabel(f'{fields[argv[2]][0]} {fields[argv[2]][1]}')
plt.title('')
# Add legend
leg = plt.legend(handles=handles, frameon=True, ncol=2, loc='lower center')
rect = leg.get_frame()
rect.set_linewidth(0.0)
rect.set_alpha(0.7)
plt.savefig(f'latitudinal_error_{argv[2]}_{argv[3]}.pdf', bbox_inches='tight')
plt.show()