def residuals(df,
forecast,
skip_first=0,
skip_last=0,
title="Patiend ID CGM",
savefig=False):
"""Plot the input residuals.
Parameters
-------------------
df : DataFrame, the output returned by gluco_extract(..., return_df=True)
forecast : array of float, the prediction for the given time-series
skip_first : number, the number of initial samples to exclude
skip_last : number, the number of final samples to exclude
title : string, the title of the plot (optional)
savefig : bool, if True save title.png
"""
# Evaluate the residuals (exclude learning and open loop ph samples)
residuals = df.as_matrix()[skip_first:-skip_last].ravel(
) - forecast[skip_first:-skip_last]
plt.figure(figsize=(12, 4), dpi=300)
plt.plot(df.index[skip_first:-skip_last], residuals)
DW = sm.stats.durbin_watson(residuals)
plt.title('Durbin-Watson: {:.3f}'.format(DW))
if savefig: plt.savefig(title + '_residuals.png')
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')
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()
first_guess[:, var + 1],
'--',
label="first guess",
alpha=1.0)
axarr[i].plot(final_guess[:, 0],
final_guess[:, var + 1],
':',
label="final guess")
plt.tight_layout()
leg = plt.figlegend(axarr[len(display_vars) - 1].get_lines(),
['truth', 'observations', 'first guess', 'final guess'],
loc='lower center',
ncol=2)
plt.xlabel('Time (nondimensional units)')
f.subplots_adjust(bottom=0.17)
plt.savefig('vars.pdf', bbox_extra_artists=(leg, ), bbox_inches='tight')
# Plot diagnostics
plt.figure(figsize=(6, 3))
diagn = loadtxt('diagnostics.txt')
length = where(diagn <= 0)[0]
length = diagn.shape[0] if len(length) == 0 else length[0]
plt.semilogy(diagn[:length - 1, 0], diagn[:length - 1, 1])
plt.xlabel('Iterations')
plt.ylabel('Cost function')
plt.tight_layout()
plt.savefig('cost_function.pdf', bbox_inches='tight')
plt.show()
}
plt.rc('lines', linewidth=1.0)
plt.style.use('ggplot')
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, fields[argv[2]][0], l)
plt.ylim([0, 3.5])
plt.xlabel('Time')
plt.ylabel(f'{fields[argv[2]][1]}')
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'error_compare_{argv[2]}.pdf', bbox_inches='tight')
plt.show()
}
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()
'q': ('Specific Humidity [kg/kg]', 'Specific humidity (kg/kg)')
}
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, fields[argv[2]][0], l)
plt.xlabel(f'{fields[argv[2]][1]}')
plt.ylabel('Sigma coordinate')
plt.ylim([1.0, 0.0])
plt.xlim([0.0, max(1.0, plt.gca().get_xlim()[1])])
plt.gca().set_xlim(left=0)
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'vertical_error_{argv[2]}.pdf', bbox_inches='tight')
plt.show()