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example_display_thickness_results.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def create_visualizations(dd,thickness,cartilage_type,timepoint,diff_timepoint=None, output_prefix=None):
td = dd.loc[(dd['cartilage_type']==cartilage_type) & (dd['timepoint']==timepoint)]
ids = td['cartilage_type_id'].to_numpy(dtype=int)
ids_diff = None
print('INFO: {} measurements for timepoint {} of {}'.format(len(ids), timepoint, cartilage_type))
if diff_timepoint is not None:
td_diff = dd.loc[(dd['cartilage_type']==cartilage_type) & (dd['timepoint']==diff_timepoint)]
# find the common patient-ids
is_common_td = (td['patient_id']).isin(td_diff['patient_id'])
is_common_diff = (td_diff['patient_id']).isin(td['patient_id'])
td = td[is_common_td].sort_values(by='patient_id')
td_diff = td_diff[is_common_diff].sort_values(by='patient_id')
ids = td['cartilage_type_id'].to_numpy(dtype=int)
ids_diff = td_diff['cartilage_type_id'].to_numpy(dtype=int)
print('INFO: {} measurements for timepoint {} of {} after selection via {}'.format(len(ids), timepoint, cartilage_type, diff_timepoint))
if diff_timepoint is not None:
thickness_selected = thickness[ids, ...]-thickness[ids_diff, ...]
else:
thickness_selected = thickness[ids, ...]
# now compute the statistical measures ignoring NaNs
mean_val = np.nanmean(thickness_selected, axis=0)
std_val = np.nanstd(thickness_selected, axis=0)
perc25 = np.nanpercentile(thickness_selected, 25, axis=0)
perc50 = np.nanpercentile(thickness_selected, 50, axis=0)
perc75 = np.nanpercentile(thickness_selected, 75, axis=0)
iqr = perc75-perc25
number_of_non_nan_measurements = np.sum(~np.isnan(thickness_selected) &
(np.greater(thickness_selected,0.0, where=~np.isnan(thickness_selected))),axis=0)
output_all_prefix = output_prefix + '_' + cartilage_type + '_' + timepoint
plt.clf()
plt.imshow(mean_val)
plt.colorbar()
plt.title('mean' + ' ' + timepoint )
plt.savefig( output_all_prefix + '_mean.pdf')
if diff_timepoint is not None:
plt.clf()
plt.imshow(mean_val<0)
plt.title('mean is negative' + ' ' + timepoint)
plt.savefig(output_all_prefix + '_mean_is_negative.pdf')
plt.clf()
plt.imshow(perc25 < 0)
plt.title('perc25 is negative' + ' ' + timepoint)
plt.savefig(output_all_prefix + '_perc25_is_negative.pdf')
plt.clf()
plt.imshow(perc50 < 0)
plt.title('median is negative' + ' ' + timepoint)
plt.savefig(output_all_prefix + '_median_is_negative.pdf')
plt.clf()
plt.imshow(perc75 < 0)
plt.title('perc75 is negative' + ' ' + timepoint)
plt.savefig(output_all_prefix + '_perc75_is_negative.pdf')
plt.clf()
plt.imshow(std_val)
plt.colorbar()
plt.title('std' + ' ' + timepoint )
plt.savefig( output_all_prefix + '_std.pdf')
plt.clf()
plt.imshow(number_of_non_nan_measurements)
plt.colorbar()
plt.title('number of non-zero measurements' + ' ' + timepoint)
plt.savefig( output_all_prefix + '_non_zero.pdf')
plt.clf()
plt.imshow(perc50)
plt.colorbar()
plt.title('median' + ' ' + timepoint)
plt.savefig(output_all_prefix + '_median.pdf')
plt.clf()
plt.imshow(iqr)
plt.colorbar()
plt.title('interquartile range' + ' ' + timepoint)
plt.savefig(output_all_prefix + '_iqr.pdf')
femoral_thickness = np.load('thickness_results_femoral_cartilage.npz', allow_pickle=True)['data']
tibial_thickness = np.load('thickness_results_tibial_cartilage.npz', allow_pickle=True)['data']
dd = pd.read_pickle('thickness_results.pkl')
# select all the femoral_cartilage for the different time-points
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='ENROLLMENT', output_prefix='result')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='12_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='24_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='36_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='48_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='72_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='12_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='24_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='36_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='48_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
create_visualizations(dd=dd,thickness=femoral_thickness,cartilage_type='femoral',
timepoint='72_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
# select all the tibial_cartilage for the different time-points
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='ENROLLMENT', output_prefix='result')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='12_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='24_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='36_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='48_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='72_MONTH', output_prefix='result')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='12_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='24_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='36_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='48_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')
create_visualizations(dd=dd,thickness=tibial_thickness,cartilage_type='tibial',
timepoint='72_MONTH', diff_timepoint='ENROLLMENT', output_prefix='result_m_enrollment')