def main(args):
# Collect data from results
df = pd.read_pickle(path='results/fixed_parameters_std-2/8.pkl')
# Set time range, 1440 = 1 day
N = 1440
x = np.arange(N)
# Prep figure
pu.figure_setup()
fig_size = pu.get_fig_size(10, 5)
fig = plt.figure(figsize=(fig_size))
############################################################################
# Collect glucose data
gut_compartment = df['gt'].values[:N]
plasma_compartment = df['mt'].values[:N]
carb_est = df['carb_ests_'].values[:N]
# carb_est[carb_est==0] = np.nan
# Plot Glucose compartments...
ax = fig.add_subplot(111)
ax.plot(x, gut_compartment, c='b', lw=pu.plot_lw(), label='Gut glucose')
ax.plot(x,
plasma_compartment,
c='r',
lw=pu.plot_lw(),
label='Plasma glucose')
ax.set_xlabel('$t \, (minutes)$')
ax.set_ylabel('$mg/L$')
ax.set_axisbelow(True)
# Add second axis
ax2 = ax.twinx()
color = 'tab:orange'
ax2.set_ylabel('$\hat{m}$', color=color)
ax2.plot(
x,
carb_est, #lw=pu.plot_lw(),
label='Carbs est.',
color=color,
alpha=.5) #, marker='o', markerfacecolor='None',
#markersize=5, linewidth=0, alpha=.2)
ax2.tick_params(axis='y', labelcolor=color)
# Add legends
ax.legend(loc='upper left', prop={'size': 5})
ax2.legend(loc='upper right', prop={'size': 5})
# Finilize plot
plt.grid()
plt.tight_layout()
if args.save:
pu.save_fig(fig, args.save)
else:
plt.show()
def plot(ax, x, y):
# Fit the classifiers
clf = linear_model.LogisticRegression(C=1e5, solver="lbfgs")
clf.fit(x, y)
ols = linear_model.LinearRegression()
ols.fit(x, y)
print("LogisticRegression:",
sum(np.equal(clf.predict(x) > 0.5, y)) / len(y))
print("LinearRegression:", sum(np.equal(ols.predict(x) > 0.5, y)) / len(y))
ax.plot(x,
y,
"o",
markersize=pu.plot_lw() * 2,
zorder=20,
label="Original data")
x_ = x_linspace(x)
loss = model(x_ * clf.coef_ + clf.intercept_).ravel()
ax.plot(x_, loss, color="red", label="Logistic Regression Model")
ax.plot(x_,
ols.coef_ * x_ + ols.intercept_,
label="Linear Regression Model")
ax.axhline(0.5, color=".5")
ax.set_ylabel("$y$")
ax.set_xlabel("$x$")
# ax.set_xticks()
ax.set_yticks([0, 0.5, 1])
ax.set_ylim(-0.25, 1.25)
plt.xlim(min(x_), max(x_))
ax.legend(prop={"size": 6}, loc="lower right")
def main(args):
x = np.linspace(-6, 6, 200)
y = 1/(1 + np.exp(-x))
pu.figure_setup()
fig_size = pu.get_fig_size(10, 5)
fig = plt.figure(figsize=fig_size)
ax = fig.add_subplot(111)
ax.plot(x, y, c='b', lw=pu.plot_lw())
ax.set_xlabel('$x$')
ax.set_ylabel('$\\sigma(x)$')
ax.set_axisbelow(True)
plt.grid()
plt.tight_layout()
if args.save:
pu.save_fig(fig, args.save)
else:
plt.show()
def main(args):
x = np.linspace(-6, 6, 200)
y = 1 / (1 + np.exp(-x))
pu.figure_setup()
fig_size = pu.get_fig_size(10, 5)
fig = plt.figure(figsize=(fig_size))
ax = fig.add_subplot(111)
ax.plot(x, y, c='b', lw=pu.plot_lw())
ax.set_xlabel('$x$')
ax.set_ylabel('$\\sigma(x)$')
ax.set_axisbelow(True)
plt.grid()
plt.tight_layout()
if args.save:
pu.save_fig(fig, args.save)
else:
plt.show()
def main(args):
# Collect data from results
df = pd.read_pickle(path='results/fixed_parameters_std-2/8.pkl')
# Set time range, 1440 = 1 day
N = 1440
x = np.arange(N)
# Prep figure
pu.figure_setup()
fig_size = pu.get_fig_size(10, 5)
fig = plt.figure(figsize=(fig_size))
############################################################################
# Collect insulin data
tissue_compartment = df['s1'].values[:N]
plasma_compartment = df['s2'].values[:N]
bolus_data = df['bolus_inputs_'].values[:N]
# Keep only non-zero bolus data - Set zeros to nan
bolus_data[bolus_data == 0] = np.nan
# Plot Insulin Compartment Data
ax = fig.add_subplot(111)
ax.plot(x,
tissue_compartment,
c='b',
lw=pu.plot_lw(),
label='Subcutaneous tissue')
ax.plot(x,
plasma_compartment,
c='r',
lw=pu.plot_lw(),
label='Blood plasma')
ax.set_xlabel('$t \, (minutes)$')
ax.set_ylabel('$Insulin, \, mg/L$')
ax.set_axisbelow(True)
# Add Bolus Data on second y-axis
ax2 = ax.twinx()
color = 'tab:green'
ax2.set_ylabel('Units', color=color)
ax2.plot(
x,
bolus_data, #lw=pu.plot_lw(),
label='Bolus',
color=color,
marker='D',
markerfacecolor='None',
markersize=5,
linewidth=0)
ax2.tick_params(axis='y', labelcolor=color)
# Show legend
ax.legend(loc='upper left', prop={'size': 5})
ax2.legend(loc='upper right', prop={'size': 5})
plt.grid()
plt.tight_layout()
if args.save:
pu.save_fig(fig, args.save)
else:
plt.show()
def main(args):
# Collect data from results
df = pd.read_pickle(path='results/fixed_parameters_std-2/4.pkl')
# Set time range
S = 540
N = 710
x = np.arange(N - S)
# Prep figure
pu.figure_setup()
fig_size = pu.get_fig_size(10, 5)
fig = plt.figure(figsize=(fig_size))
############################################################################
# Collect CGM and meal data
cgm_data = df['cgm_inputs_'].values[S:N]
cgm_model = df['Gt'].values[S:N]
meals = df['y_meals'].values[S:N]
# Make all 0 target meal NaNs
meals[meals == 0] = np.nan
# Print meal idxs
print('Meal idxs: ', np.where(meals == 1.))
# Plot CGM and Meal data
ax = fig.add_subplot(211)
ax.set_xlim(0, N - S)
ax.set_ylim(0, 250)
ax.axhline(70, linewidth=1, color='#e3e1e1')
ax.axhline(180, linewidth=1, color='#e3e1e1')
# ax.axvspan(125, 170, color='#eeeeee')
ax.plot(x,
cgm_data,
marker='o',
c='g',
linewidth=0,
markersize=1,
label='CGM')
ax.plot(x,
meals * 20,
marker='o',
c='purple',
linewidth=0,
markersize=4,
markerfacecolor='None',
label='Meal')
ax.set_axisbelow(True)
ax.legend(loc='upper left', prop={'size': 6})
ax.set_ylabel('$mg/dl$')
############################################################################
# Collect insulin data
tissue_compartment = df['s1'].values[S:N]
plasma_compartment = df['s2'].values[S:N]
bolus_data = df['bolus_inputs_'].values[S:N]
# Keep only non-zero bolus data - Set zeros to nan
bolus_data[bolus_data == 0] = np.nan
# Print bolus idxs
print('Bolus idxs: ', np.where(bolus_data > 0.))
# Plot Insulin Compartment Data
ax = fig.add_subplot(212, sharex=ax)
ax.set_xlim(0, N - S)
ax.plot(x,
tissue_compartment,
c='b',
lw=pu.plot_lw(),
label='Subcutaneous tissue')
ax.plot(x,
plasma_compartment,
c='r',
lw=pu.plot_lw(),
label='Blood plasma')
ax.set_xlabel('$t \, (minutes)$')
ax.set_ylabel('$Insulin, \, mg/L$')
# plt.grid()
ax.set_axisbelow(True)
# Add Bolus Data on second y-axis
ax2 = ax.twinx()
color = 'tab:green'
ax2.set_ylabel('Units', color=color)
ax2.plot(
x,
bolus_data, #lw=pu.plot_lw(),
label='Bolus',
color=color,
marker='D',
markerfacecolor='None',
markersize=4,
linewidth=0)
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_axisbelow(True)
# Show legend
ax.legend(loc='upper left', prop={'size': 6})
ax2.legend(loc='upper right', prop={'size': 6})
plt.tight_layout()
if args.save:
pu.save_fig(fig, args.save)
else:
plt.show()
def main(args):
# Collect data from results
df = pd.read_pickle(path='results/fixed_parameters_std-2/4.pkl')
# Set time range
S = 540
N = 695
H = 30
x = np.arange(N - S)
# Prep figure
pu.figure_setup()
fig_size = pu.get_fig_size(10, 5)
fig = plt.figure(figsize=(fig_size))
############################################################################
# Collect CGM and meal data
cgm_data = df['cgm_inputs_'].values[S:N]
cgm_model = df['Gt'].values[S:N]
meals = df['y_meals'].values[S:N]
# Make all 0 target meal NaNs
meals[meals == 0] = np.nan
print('Logged Meal idxs:', np.where(meals > 0))
# Get meal predictions
meal_preds = df['meal_preds_'].values[S:N]
meal_preds[meal_preds == 0] = np.nan
# Plot CGM and Meal data
ax = fig.add_subplot(211)
ax.set_xlim(0, N - S)
ax.set_ylim(0, 250)
ax.axhline(70, linewidth=1, color='#ececec')
ax.axhline(180, linewidth=1, color='#ececec')
ax.plot(x,
cgm_data,
marker='o',
c='g',
linewidth=0,
markersize=1,
label='CGM')
ax.plot(x,
meals * 20,
marker='o',
c='purple',
linewidth=0,
markersize=4,
markerfacecolor='None',
label='Meal')
ax.plot(x,
meal_preds * 5,
marker='^',
c='magenta',
linewidth=0,
markersize=4,
label='Predicted meal')
ax.set_axisbelow(True)
ax.legend(loc='upper left', prop={'size': 6})
ax.set_ylabel('$mg/dl$')
############################################################################
# Glucose and Meal Detection
# Get glucose data
carb_est = df['carb_ests_'].values[S:N]
carb_mean = df['carb_mean_'].values[S:N]
carb_stds = df['carb_stds_'].values[S:N]
# Plot
ax = fig.add_subplot(212, sharex=ax)
ax.set_xlim(0, N - S)
for it, m in enumerate(meal_preds):
if m == 1:
print('Meal prediction idx: ', it)
ax.axvline(it, c='magenta')
ax.plot(x, carb_est, c='orange', lw=pu.plot_lw(), label="$\hat{m}_t'$")
ax.plot(x, carb_mean, c='b', lw=.8, label="$\\overline{\hat{m'}}_{0:t}$")
ax.plot(x,
carb_stds,
c='b',
lw=.8,
label="$2 \cdot \sigma$",
linestyle='-.')
ax.legend(loc='upper left', prop={'size': 6})
ax.set_xlabel('$t \, (minutes)$')
ax.set_ylabel('$Carbs, \, mg$')
ax.set_axisbelow(True)
# plt.grid()
plt.tight_layout()
if args.save:
pu.save_fig(fig, args.save)
else:
plt.show()
def main(args):
# Collect data from results
df = pd.read_pickle(path='./../results/2019-06-15_17:48/4.pkl')
# Set time range, 1440 = 1 day
N = 3000
x = np.arange(N)
# Prep figure
pu.figure_setup()
fig_size = pu.get_fig_size(10, 50)
fig = plt.figure(figsize=(fig_size))
############################################################################
# Collect CGM and meal data
cgm_data = df['cgm_inputs_'].values[:N]
cgm_model = df['Gt'].values[:N]
meals = df['y_meals'].values[:N]
# Make all 0 target meal NaNs
meals[meals == 0] = np.nan
# Plot CGM and Meal data
ax = fig.add_subplot(411)
ax.axhspan(70, 180, alpha=0.1, color='black')
ax.set_ylim(0, 350)
ax.plot(x,
cgm_data,
marker='o',
c='g',
linewidth=0,
markersize=1,
label='CGM')
ax.plot(x,
cgm_model,
c='black',
linewidth=1,
linestyle='dashed',
label='Modeled CGM')
ax.plot(x,
meals * 20,
marker='*',
c='purple',
linewidth=0,
markersize=4,
label='Meal')
# ax.set_xlabel('$t (minutes)$')
ax.legend(loc='upper left', prop={'size': 6})
ax.set_ylabel('$mg/dl$')
meal_preds = df['meal_preds_'].values[:N]
meal_preds[meal_preds == 0] = np.nan
for it, m in enumerate(meal_preds):
if m == 1:
ax.axvline(it, c='magenta', alpha=.1)
# Add CGM prediction
# horizon = 45
# start_time = 488
# x_pred = np.arange(start_time,start_time+horizon+1)
# cgm_pred = cgm_preds[start_time]-46
# ax.plot(x_pred, cgm_pred, c='black', linewidth=1, linestyle='dashed')
############################################################################
# Collect insulin data
tissue_compartment = df['s1'].values[:N]
plasma_compartment = df['s2'].values[:N]
bolus_data = df['bolus_inputs_'].values[:N]
# Keep only non-zero bolus data - Set zeros to nan
bolus_data[bolus_data == 0] = np.nan
# Plot Insulin Compartment Data
ax = fig.add_subplot(412)
ax.plot(x,
tissue_compartment,
c='b',
lw=pu.plot_lw(),
label='Subcutaneous tissue')
ax.plot(x,
plasma_compartment,
c='r',
lw=pu.plot_lw(),
label='Blood plasma')
# ax.set_xlabel('$t \, (minutes)$')
ax.set_ylabel('$Insulin, \, mg/L$')
ax.set_axisbelow(True)
# Add Bolus Data on second y-axis
ax2 = ax.twinx()
color = 'tab:green'
ax2.set_ylabel('Units', color=color)
ax2.plot(
x,
bolus_data, #lw=pu.plot_lw(),
label='Bolus',
color=color,
marker='D',
markerfacecolor='None',
markersize=5,
linewidth=0)
ax2.tick_params(axis='y', labelcolor=color)
# Show legend
ax.legend(loc='upper left', prop={'size': 6})
# ax2.legend(loc='upper right', prop={'size': 6})
############################################################################
# Collect glucose data
gut_compartment = df['gt'].values[:N]
plasma_compartment = df['mt'].values[:N]
# Plot Glucose compartments...
ax = fig.add_subplot(413)
ax.plot(x, gut_compartment, c='b', lw=pu.plot_lw(), label='Gut glucose')
ax.plot(x,
plasma_compartment,
c='r',
lw=pu.plot_lw(),
label='Plasma glucose')
# ax.set_xlabel('$t \, (minutes)$')
ax.set_ylabel('$mg/L$')
ax.set_axisbelow(True)
ax.legend(loc='upper left', prop={'size': 6})
############################################################################
# Glucose and Meal Detection
# Get glucose data
carb_est = df['carb_ests_'].values[:N]
carb_mean = df['carb_mean_'].values[:N]
carb_stds = df['carb_stds_'].values[:N]
# Get meal predictions
meal_preds = df['meal_preds_'].values[:N]
meal_preds[meal_preds == 0] = np.nan
# Plot
ax = fig.add_subplot(414)
ax.plot(x, carb_est, c='orange', lw=pu.plot_lw())
# ax.plot(x, carb_mean, c='b', lw=pu.plot_lw(), linestyle='dashed')
# ax.plot(x, carb_mean, c='b', lw=pu.plot_lw(), linestyle='dashed')
ax.plot(x,
meal_preds * carb_est,
marker='*',
c='magenta',
linewidth=0,
markersize=4,
label='Predicted meal')
ax.set_xlabel('$t \, (minutes)$')
ax.set_ylabel('$Carbs, \, mg$')
ax.set_axisbelow(True)
plt.grid()
plt.tight_layout()
if args.save:
pu.save_fig(fig, args.save)
else:
plt.show()
def main(args):
# Collect data from results
df = pd.read_pickle(path='results/fixed_parameters_std-2/4.pkl')
# Set time range
S = 540
N = 695
H = 30
x = np.arange(N - S)
# Prep figure
pu.figure_setup()
fig_size = pu.get_fig_size(10, 8)
fig = plt.figure(figsize=(fig_size))
############################################################################
# Collect CGM and meal data
cgm_data = df['cgm_inputs_'].values[S:N]
cgm_model = df['Gt'].values[S:N]
meals = df['y_meals'].values[S:N]
# Set up prediction line
pred_t = 124
pred = df['cgm_preds_'][pred_t]
x_pred = np.arange(pred_t, pred_t + len(pred))
# Make all 0 target meal NaNs
meals[meals == 0] = np.nan
# Print meal idxs
print('Meal idxs: ', np.where(meals == 1.))
# Plot CGM and Meal data
ax = fig.add_subplot(411)
ax.set_xlim(0, N - S)
ax.set_ylim(0, 250)
ax.axhline(70, linewidth=1, color='#ececec')
ax.axhline(180, linewidth=1, color='#ececec')
ax.axvspan(pred_t, pred_t + H + 1, color='#f6f6f6')
ax.plot(x,
cgm_data,
marker='o',
c='g',
linewidth=0,
markersize=1,
label='CGM')
ax.plot(x_pred,
pred,
c='black',
linewidth=1,
linestyle='dashed',
label='Modeled CGM')
ax.plot(x,
meals * 20,
marker='o',
c='purple',
linewidth=0,
markersize=4,
markerfacecolor='None',
label='Meal')
plt.vlines(x=pred_t + H,
ymin=pred[-1],
ymax=cgm_data[pred_t + H - 1],
color='r',
linewidth=2)
ax.set_axisbelow(True)
ax.legend(loc='upper left', prop={'size': 6})
ax.set_ylabel('$mg/dl$')
############################################################################
# Collect insulin data
tissue_compartment = df['s1'].values[S:N]
plasma_compartment = df['s2'].values[S:N]
bolus_data = df['bolus_inputs_'].values[S:N]
# Keep only non-zero bolus data - Set zeros to nan
bolus_data[bolus_data == 0] = np.nan
# Print bolus idxs
print('Bolus idxs: ', np.where(bolus_data > 0.))
# Plot Insulin Compartment Data
ax = fig.add_subplot(412, sharex=ax)
ax.set_xlim(0, N - S)
ax.axvspan(pred_t, pred_t + H + 1, color='#f6f6f6')
ax.plot(x,
tissue_compartment,
c='b',
lw=pu.plot_lw(),
label='Subcutaneous tissue')
ax.plot(x,
plasma_compartment,
c='r',
lw=pu.plot_lw(),
label='Blood plasma')
ax.set_ylabel('$Insulin, \, mg/L$')
# plt.grid()
ax.set_axisbelow(True)
# Add Bolus Data on second y-axis
ax2 = ax.twinx()
color = 'tab:green'
ax2.set_ylabel('Units', color=color)
ax2.plot(
x,
bolus_data, #lw=pu.plot_lw(),
label='Bolus',
color=color,
marker='D',
markerfacecolor='None',
markersize=4,
linewidth=0)
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_axisbelow(True)
# Show legend
ax.legend(loc='upper left', prop={'size': 6})
ax2.legend(loc='upper right', prop={'size': 6})
############################################################################
# Collect glucose data
gut_compartment = df['gt'].values[S:N - H]
plasma_compartment = df['mt'].values[S:N - H]
xx = np.arange(N - S - H)
# Collect "prediction" data
gut_compartment_ff = df['gt'].values[N - H:N]
plasma_compartment_ff = df['mt'].values[N - H:N]
xx_ff = np.arange(N - S - H, N - S)
# Plot Glucose compartments...
ax = fig.add_subplot(413, sharex=ax)
ax.set_xlim(0, N - S)
ax.axvspan(pred_t, pred_t + H + 1, color='#f6f6f6')
ax.plot(xx, gut_compartment, c='b', lw=pu.plot_lw(), label='Gut glucose')
ax.plot(xx,
plasma_compartment,
c='r',
lw=pu.plot_lw(),
label='Plasma glucose')
ax.plot(xx_ff,
gut_compartment_ff,
c='b',
lw=pu.plot_lw(),
linestyle='dashed')
ax.plot(xx_ff,
plasma_compartment_ff,
c='r',
lw=pu.plot_lw(),
linestyle='dashed')
ax.set_ylabel('$mg/L$')
ax.set_axisbelow(True)
ax.legend(loc='upper left', prop={'size': 6})
############################################################################
# Glucose and Meal Detection
# Get glucose data
carb_est = df['carb_ests_'].values[S:N - H]
carb_mean = df['carb_mean_'].values[S:N - H]
carb_stds = df['carb_stds_'].values[S:N - H]
xx_carbs = np.arange(N - S - H)
# Get "future" glucose data
carb_est_ff = df['carb_ests_'].values[N - H:N]
xx_carbs_ff = np.arange(N - S - H, N - S)
# Get meal predictions
meal_preds = df['meal_preds_'].values[S:N - H]
meal_preds[meal_preds == 0] = np.nan
# Plot
ax = fig.add_subplot(414, sharex=ax)
ax.set_xlim(0, N - S)
ax.axvspan(pred_t, pred_t + H + 1, color='#f6f6f6')
ax.plot(xx_carbs,
carb_est,
c='orange',
lw=pu.plot_lw(),
label='Carbs est.')
ax.plot(xx_carbs_ff,
carb_est_ff,
c='orange',
lw=pu.plot_lw(),
linestyle='dashed')
# ax.plot(x, carb_mean, c='b', lw=0.5, label)
# ax.plot(xx_carbs, meal_preds, marker='^', c='magenta', linewidth=0,
# markersize=3, label='Predicted meal')
ax.legend(loc='upper left', prop={'size': 6})
ax.set_xlabel('$t \, (minutes)$')
ax.set_ylabel('$Carbs, \, mg$')
ax.set_axisbelow(True)
# plt.grid()
plt.tight_layout()
if args.save:
pu.save_fig(fig, args.save)
else:
plt.show()