def plot_cumul_hist(data, booklet=None):
"""
Plots cumulative histogram for PPMI data.
data: output of load()
booklet: optionally restrict to one of four booklets (1-4).
"""
if booklet:
smell = [value['upsit'][booklet - 1] for key, value in data.items()]
else:
smell = [sum(value['upsit']) for key, value in data.items()]
recruitment = [value['recruitment'] for key, value in data.items()]
smell_ctl = [
smell[i] for i in range(len(recruitment)) if recruitment[i] == 0
]
smell_ctl = sorted(smell_ctl)
smell_pd = [
smell[i] for i in range(len(recruitment)) if recruitment[i] == 1
]
smell_pd = sorted(smell_pd)
cumul_hist(smell_ctl, color='k')
cumul_hist(smell_pd, color='r')
plt.xlabel('UPSIT score')
plt.ylabel('Cumulative Probability')
def plot_cumul_hist(data,booklet=None):
"""
Plots cumulative histogram for PPMI data.
data: output of load()
booklet: optionally restrict to one of four booklets (1-4).
"""
if booklet:
smell = [value['upsit'][booklet-1] for key,value in data.items()]
else:
smell = [sum(value['upsit']) for key,value in data.items()]
recruitment = [value['recruitment'] for key,value in data.items()]
smell_ctl = [smell[i] for i in range(len(recruitment)) if recruitment[i]==0]
smell_ctl = sorted(smell_ctl)
smell_pd = [smell[i] for i in range(len(recruitment)) if recruitment[i]==1]
smell_pd = sorted(smell_pd)
cumul_hist(smell_ctl,color='k')
cumul_hist(smell_pd,color='r')
plt.xlabel('UPSIT score')
plt.ylabel('Cumulative Probability')
pd_p = np.random.binomial(23,pd_p0) / 23.0
diff[q-1] = ctrl_p - pd_p
shuffle_diff = np.zeros(40000)
for q in range(1,40001):
q_ctrl = ((q-1) % 40) + 1
q_pd = np.random.randint(1,41)
ctrl_p = proportion(q_ctrl,'ctrl')
ctrl_x = np.log(ctrl_p/(1-ctrl_p))
pd_x = ctrl_x - 1.4 + 0.7*np.random.randn()
pd_p0 = np.exp(pd_x)/(1+np.exp(pd_x))
pd_p = np.random.binomial(23,pd_p0) / 23.0
shuffle_diff[q-1] = ctrl_p - pd_p
upsit.plt.figure()
upsit.cumul_hist(diff,color='r')
upsit.cumul_hist(shuffle_diff,color='k')
upsit.plt.show()
if 0:
for test in tests:
if test.subject.label == 'ctrl':
ctrl.append(test.score)
if test.subject.label == 'pd':
pd.append(test.score)
#pprint(upsit_key)
#pprint(bbdp_data)
#upsit.cumul_hist(ctrl,color='k')
#upsit.cumul_hist(pd,color='r')
pd_p = np.random.binomial(23, pd_p0) / 23.0
diff[q - 1] = ctrl_p - pd_p
shuffle_diff = np.zeros(40000)
for q in range(1, 40001):
q_ctrl = ((q - 1) % 40) + 1
q_pd = np.random.randint(1, 41)
ctrl_p = proportion(q_ctrl, 'ctrl')
ctrl_x = np.log(ctrl_p / (1 - ctrl_p))
pd_x = ctrl_x - 1.4 + 0.7 * np.random.randn()
pd_p0 = np.exp(pd_x) / (1 + np.exp(pd_x))
pd_p = np.random.binomial(23, pd_p0) / 23.0
shuffle_diff[q - 1] = ctrl_p - pd_p
upsit.plt.figure()
upsit.cumul_hist(diff, color='r')
upsit.cumul_hist(shuffle_diff, color='k')
upsit.plt.show()
if 0:
for test in tests:
if test.subject.label == 'ctrl':
ctrl.append(test.score)
if test.subject.label == 'pd':
pd.append(test.score)
#pprint(upsit_key)
#pprint(bbdp_data)
#upsit.cumul_hist(ctrl,color='k')
#upsit.cumul_hist(pd,color='r')