def _first_and_last(title,
y_label,
key,
indicator,
ticks=None,
with_wilcoxon=False,
patients=None):
firsts, lasts, common, p = first_and_last_data(key, indicator, patients)
fig, ax = plt.subplots()
ax.boxplot([firsts, lasts], labels=['First Training', 'Last Training'])
ax.set_title(title)
ax.yaxis.grid(True,
linestyle='-',
which='major',
color='lightgrey',
alpha=0.5)
ax.set_axisbelow(True)
ax.set_xlabel('Session')
ax.set_ylabel(y_label)
if ticks is not None:
ax.set_yticks(ticks)
if with_wilcoxon:
wx = mpatches.Patch(color='red', label="Wilcoxon p-value={}".format(p))
plt.legend(handles=[wx])
plt.show()
def plot_patient_donning_all_s_first_last_10_14_log_regression():
title = 'Patient Donning'
key = 'Donning'
indicator = 'total'
sessions = [10, 14]
firsts, lasts, common, p = first_and_last_data(key, indicator)
patient_donnings = [p.data[key] for p in PATIENTS]
session_dict = {}
for s in sessions:
session_dict[s] = []
for s in session_dict:
for donnings in patient_donnings:
session_dict[s].extend(
[d['total'] for d in donnings if d['session'] == s])
data = [firsts, lasts, session_dict[10], session_dict[14]]
fig, ax = plt.subplots()
x = [2, 6, 10, 14]
ax.set_xticks(x)
ax.boxplot(data,
positions=x,
labels=['First Training', 'Last Training', '10', '14'])
ax.set_title(title)
ax.yaxis.grid(True,
linestyle='-',
which='major',
color='lightgrey',
alpha=0.5)
ax.set_axisbelow(True)
ax.set_xlabel('Session')
ax.set_ylabel('Time (minutes)')
y_ticks = np.arange(0, 65, 5)
ax.set_yticks(y_ticks[1:])
# logarithmic regression
y = [np.median(t) for t in data]
def func(t, a, b):
return a + b * np.log(t)
import scipy.optimize
popt, pcov = scipy.optimize.curve_fit(func, x, y)
log_a = popt[0]
log_a_round = round(log_a, 3)
log_b = popt[1]
log_b_round = round(log_b, 3)
trialX = np.linspace(x[0], x[-1], 20)
ylog = func(trialX, *popt)
# r2
# https://stackoverflow.com/questions/19189362/getting-the-r-squared-value-using-curve-fit
residuals = y - func(x, *popt)
ss_res = np.sum(residuals**2)
ss_tot = np.sum((y - np.mean(y))**2)
log_r_squared = 1 - (ss_res / ss_tot)
log_r_squared_round = round(log_r_squared, 3)
plt.plot(trialX, ylog, 'r-', ls='--')
# legend
wilcoxson_text = "Wilcoxon p-value={}".format(p)
# wx = mpatches.Patch(color='blue', label=wilcoxson_text)
log = mpatches.Patch(color='red',
label="y=a+bln(x)\na={}, b={}\n$r^2={}$".format(
log_a_round, log_b_round, log_r_squared_round))
plt.legend(handles=[log])
ax.annotate(wilcoxson_text,
xy=(0.2, 0.75),
xytext=(0.2, 0.85),
xycoords='axes fraction',
fontsize=10,
ha='center',
va='bottom',
bbox=dict(boxstyle='square', fc='white'),
arrowprops=dict(arrowstyle='-[, widthB=5.5, lengthB=1',
lw=1.0))
plt.show()
def plot_patient_and_ot_donning_all_s_1_5_10_14_averaged():
title = 'Donning - OTs and Carers'
p_key = 'Donning'
p_indicator = 'total'
sessions = [10, 14]
p_firsts, p_lasts, _p_common, p_p = first_and_last_data(p_key, p_indicator)
ot_key = 'donning'
ot_indicator = 'minutes'
ot_firsts, ot_lasts, _ot_common, _ot_p = first_and_last_data(
ot_key, ot_indicator, OTS)
patient_donnings = [p.data[p_key] for p in PATIENTS]
session_dict = {}
for s in sessions:
session_dict[s] = []
for s in session_dict:
for donnings in patient_donnings:
session_dict[s].extend(
[d['total'] for d in donnings if d['session'] == s])
data = [[ot_firsts, p_firsts], [ot_lasts, p_lasts], [session_dict[10]],
[session_dict[14]]]
print(data)
fig, ax = plt.subplots()
ot_color = 'lightgreen'
patient_color = 'lightblue'
x_ticks = []
patient_x_ticks = []
patient_x_values = [1, 4.43, 10, 14]
position = 0
for pair in data:
if len(pair) == 2:
bp = ax.boxplot(pair,
positions=[position + 1, position + 2],
widths=0.6,
patch_artist=True)
bp['boxes'][0].set_facecolor(ot_color)
bp['boxes'][1].set_facecolor(patient_color)
patient_x_ticks.append(position + 2)
x_ticks.append(position + 1.5)
else:
bp = ax.boxplot(pair,
positions=[position + 1],
widths=0.6,
patch_artist=True)
bp['boxes'][0].set_facecolor(patient_color)
patient_x_ticks.append(position + 1)
x_ticks.append(position + 1)
position = position + 3
ax.set_ylabel('Donning Time (minutes)')
ax.set_title(title)
ax.set_xticks(x_ticks)
ax.set_xticklabels(('First Training', 'Last Training', '10', '14'))
ot_label = mpatches.Patch(color=ot_color, label="OT")
patient_label = mpatches.Patch(color=patient_color, label="Carers")
#ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)
ax.set_axisbelow(True)
# logarithmic regression
x = patient_x_ticks
patient_data = [p_firsts, p_lasts, session_dict[10], session_dict[14]]
y = [np.median(t) for t in patient_data]
def func(t, a, b):
return a + b * np.log(t)
import scipy.optimize
popt, pcov = scipy.optimize.curve_fit(func, patient_x_values, y)
log_a = popt[0]
log_a_round = round(log_a, 3)
log_b = popt[1]
log_b_round = round(log_b, 3)
trialX = np.linspace(x[0], x[-1], 20)
ylog = func(trialX, *popt)
# r2
# https://stackoverflow.com/questions/19189362/getting-the-r-squared-value-using-curve-fit
residuals = y - func(x, *popt)
ss_res = np.sum(residuals**2)
ss_tot = np.sum((y - np.mean(y))**2)
log_r_squared = 1 - (ss_res / ss_tot)
log_r_squared_round = round(log_r_squared, 3)
#plt.plot(trialX, ylog, 'y-', ls='--')
# legend
wilcoxson_text = "p-value={}".format(p_p)
log = mpatches.Patch(linestyle='--',
facecolor='yellow',
label="y=a+bln(x)\na={}, b={}\n$r^2={}$".format(
log_a_round, log_b_round, log_r_squared_round))
plt.legend(handles=[log, ot_label, patient_label])
ax.annotate(wilcoxson_text,
xy=(0.3, 0.75),
xytext=(0.3, 0.90),
xycoords='axes fraction',
fontsize=10,
ha='center',
va='bottom',
bbox=dict(boxstyle='square', fc='lightblue'),
arrowprops=dict(arrowstyle='-[, widthB=5.5, lengthB=1',
lw=1.0))
plt.show()
def plot_carer_combined():
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=(WIDTH_17_CENTIMERES_IN_INCHES,
HEIGHT_9_CENTIMERES_IN_INCHES))
# ------------------------------------------------------------------
# --------------------plot_carer_workload --------------------------
# ------------------------------------------------------------------
x = []
y = []
for id in range(2, 10):
c_name = "c" + str(id)
carer = [c for c in CARERS if c.name == c_name][0]
carer_data = carer.data['NASA_TLX']
p_name = "p" + str(id)
patient = [p for p in PATIENTS if p.name == p_name][0]
patient_data = patient.data['Donning']
for c_ix, c_session in enumerate(carer_data):
p_session = patient_data[c_ix]
donning = p_session['total']
demand = c_session['mental_demand']
if np.isnan(donning) or np.isnan(demand):
continue
else:
y.append(donning)
x.append(demand)
colors = (0, 0, 0)
area = np.pi * 3
ax1.scatter(x, y, s=area, c=colors, alpha=0.5)
ax1.set_xlabel('Mental demand')
ax1.set_ylabel('Donning')
# ------------------------------------------------------------------
# -- plot_patient_and_ot_donning_all_s_1_5_10_14_averaged_no_log ---
# ------------------------------------------------------------------
title = 'Donning - OTs and Carers'
p_key = 'Donning'
p_indicator = 'total'
sessions = [10, 14]
p_firsts, p_lasts, _p_common, p_p = first_and_last_data(p_key, p_indicator)
ot_key = 'donning'
ot_indicator = 'minutes'
ot_firsts, ot_lasts, _ot_common, _ot_p = first_and_last_data(
ot_key, ot_indicator, OTS)
patient_donnings = [p.data[p_key] for p in PATIENTS]
session_dict = {}
for s in sessions:
session_dict[s] = []
for s in session_dict:
for donnings in patient_donnings:
session_dict[s].extend(
[d['total'] for d in donnings if d['session'] == s])
data = [[ot_firsts, p_firsts], [ot_lasts, p_lasts], [session_dict[10]],
[session_dict[14]]]
ot_color = 'lightgreen'
patient_color = 'lightblue'
x_ticks = []
patient_x_ticks = []
position = 0
for pair in data:
if len(pair) == 2:
bp = ax2.boxplot(pair,
positions=[position + 1, position + 2],
widths=0.6,
patch_artist=True)
bp['boxes'][0].set_facecolor(ot_color)
bp['boxes'][1].set_facecolor(patient_color)
patient_x_ticks.append(position + 2)
x_ticks.append(position + 1.5)
else:
bp = ax2.boxplot(pair,
positions=[position + 1],
widths=0.6,
patch_artist=True)
bp['boxes'][0].set_facecolor(patient_color)
patient_x_ticks.append(position + 1)
x_ticks.append(position + 1)
position = position + 3
ax2.set_ylabel('Donning Time (minutes)')
ax2.set_title(title)
ax2.set_xticks(x_ticks)
ax2.set_xticklabels(('First', 'Last', '10', '14'))
ot_label = mpatches.Patch(color=ot_color, label="OT")
patient_label = mpatches.Patch(color=patient_color, label="Carers")
ax2.set_axisbelow(True)
# legend
wilcoxson_text = "p-value={}".format(p_p)
plt.legend(handles=[ot_label, patient_label])
ax2.annotate(wilcoxson_text,
xy=(0.3, 0.75),
xytext=(0.3, 0.90),
xycoords='axes fraction',
fontsize=10,
ha='center',
va='bottom',
bbox=dict(boxstyle='square', fc='lightblue'),
arrowprops=dict(arrowstyle='-[, widthB=2.5, lengthB=1',
lw=1.0))
# ------------------------------------------------------------------
plt.show()
def plot_patient_and_ot_donning_all_s_1_5_10_14_averaged_no_log():
title = 'Donning - OTs and Carers'
p_key = 'Donning'
p_indicator = 'total'
sessions = [10, 14]
p_firsts, p_lasts, _p_common, p_p = first_and_last_data(p_key, p_indicator)
ot_key = 'donning'
ot_indicator = 'minutes'
ot_firsts, ot_lasts, _ot_common, _ot_p = first_and_last_data(
ot_key, ot_indicator, OTS)
patient_donnings = [p.data[p_key] for p in PATIENTS]
session_dict = {}
for s in sessions:
session_dict[s] = []
for s in session_dict:
for donnings in patient_donnings:
session_dict[s].extend(
[d['total'] for d in donnings if d['session'] == s])
data = [[ot_firsts, p_firsts], [ot_lasts, p_lasts], [session_dict[10]],
[session_dict[14]]]
print(data)
fig, ax = plt.subplots()
ot_color = 'lightgreen'
patient_color = 'lightblue'
x_ticks = []
patient_x_ticks = []
position = 0
for pair in data:
if len(pair) == 2:
bp = ax.boxplot(pair,
positions=[position + 1, position + 2],
widths=0.6,
patch_artist=True)
bp['boxes'][0].set_facecolor(ot_color)
bp['boxes'][1].set_facecolor(patient_color)
patient_x_ticks.append(position + 2)
x_ticks.append(position + 1.5)
else:
bp = ax.boxplot(pair,
positions=[position + 1],
widths=0.6,
patch_artist=True)
bp['boxes'][0].set_facecolor(patient_color)
patient_x_ticks.append(position + 1)
x_ticks.append(position + 1)
position = position + 3
ax.set_ylabel('Donning Time (minutes)')
ax.set_title(title)
ax.set_xticks(x_ticks)
ax.set_xticklabels(('First Training', 'Last Training', '10', '14'))
ot_label = mpatches.Patch(color=ot_color, label="OT")
patient_label = mpatches.Patch(color=patient_color, label="Carers")
ax.set_axisbelow(True)
# legend
wilcoxson_text = "p-value={}".format(p_p)
plt.legend(handles=[ot_label, patient_label])
ax.annotate(wilcoxson_text,
xy=(0.3, 0.75),
xytext=(0.3, 0.90),
xycoords='axes fraction',
fontsize=10,
ha='center',
va='bottom',
bbox=dict(boxstyle='square', fc='lightblue'),
arrowprops=dict(arrowstyle='-[, widthB=5.5, lengthB=1',
lw=1.0))
plt.show()