def _read_burst_ranges_file(self, burst_ranges_file):
patient = utils.get_pt_from_edf_name(burst_ranges_file)
burst_ranges_idx = burst_ranges_file.find('burst_ranges')
edf_name_no_ext = burst_ranges_file[:burst_ranges_idx-1]
burst_ranges_filepath = os.path.join(self.output_dir, patient, edf_name_no_ext, burst_ranges_file)
burst_ranges_df = pd.read_csv(burst_ranges_filepath, delimiter='\t')
return burst_ranges_df.as_matrix()
def _read_similarity_matfile(self, similarity_matfile, convert_to_np):
# returns np array of the data contained inside one similarity matfile
# (which is similarity array for one episode in one edf)
patient = utils.get_pt_from_edf_name(similarity_matfile)
if 'dtw' in similarity_matfile:
sim_fn_idx = similarity_matfile.find('dtw')
elif 'xcorr' in similarity_matfile:
sim_fn_idx = similarity_matfile.find('xcorr')
else:
assert('dtw' in similarity_matfile or 'xcorr' in similarity_matfile)
edf_name_no_ext = similarity_matfile[:sim_fn_idx-1]
similarity_output_filepath = os.path.join(self.output_dir, patient, edf_name_no_ext, similarity_matfile)
## TODO TODO
f = h5py.File(similarity_output_filepath, 'r')
similarity_arr = np.array(f[f.keys()[0]]).transpose()
# if the similarity_struct is empty, matlab would load properly as [],
# but h5py loads it as np array([0, 0], dtype=uint64)
if np.array_equal(similarity_arr, np.array([0,0])):
# similarity_struct should be empty
similarity_arr = np.array([])
else:
assert(similarity_arr.shape[0]==1), "similarity_arr must have shape 1 x n"
assert(len(similarity_arr.shape)==2), "similarity_arr must have shape 1 x n"
similarity_arr = similarity_arr[0]
if len(similarity_arr)==0:
print('len sim is 0')
return similarity_arr
def get_edf_results(self, edf_name, filter_condition):
# returns paths to output files for an edf for which filter_condition is true
patient = utils.get_pt_from_edf_name(edf_name)
results_dir = os.path.join(self.output_dir, patient, edf_name)
edf_results = os.listdir(results_dir)
edf_results = [x for x in edf_results if filter_condition(x)]
edf_result_paths = [os.path.join(results_dir, x) for x in edf_results]
return edf_result_paths
def _get_burst_ranges_files_for_edf(self, edf_name):
# returns list of files that contain burst range info for an edf
patient = utils.get_pt_from_edf_name(edf_name)
edf_no_ext = edf_name.split('.')[0]
patient_files = os.listdir(os.path.join(self.output_dir, patient, edf_no_ext))
edf_files = [f for f in patient_files if edf_name in f]
burst_ranges_files = [f for f in edf_files if 'burst_ranges' in f and f[-3:]=='csv']
return burst_ranges_files
def _get_similarity_files_for_edf(self, edf_name):
# returns dictionary of similarity_fn to list of filenames of results
# list will contain > 1 element when the results are in parts
# eg, given 'CA_MGH_sid141_01_20140218', returns
# {'dtw':['CA_MGH_sid141_01_20140218_142742_dtw_part1_episode_idx_1_13.mat'], 'xcorr':['CA_MGH_sid141_01_20140218_142742_xcorr_part1_episode_idx_1_13.mat']}
patient = utils.get_pt_from_edf_name(edf_name)
edf_no_ext = edf_name.split('.')[0]
patient_files = os.listdir(os.path.join(self.output_dir, patient, edf_no_ext))
edf_files = [f for f in patient_files if edf_name in f and '.mat' in f]
similarity_files = {'dtw':[], 'xcorr':[]}
for f in edf_files:
if 'dtw' in f:
similarity_files['dtw'].append(f)
elif 'xcorr' in f:
similarity_files['xcorr'].append(f)
else:
print('File {} with unknown similarity function'.format(f))
return similarity_files
'/afs/csail.mit.edu/u/t/tzhan/eeg/coma_EEG_alice_zhan/patient_outcome_info/exclude_patient_sids.txt'
)
# # All_all.txt
# ### Create list of all our patients, and {patient:[edfs]}
# In[13]:
our_patients = set([])
all_edfs = read_list(
'/afs/csail.mit.edu/u/t/tzhan/eeg/coma_EEG_alice_zhan/patient_outcome_info/all_all.txt'
)
patient_to_edfs = defaultdict(list)
for edf in all_edfs:
patient = utils.get_pt_from_edf_name(edf)
our_patients.add(patient)
edf_fn = edf.split('/')[-1]
patient_to_edfs[patient].append(edf_fn)
our_patients = list(our_patients)
our_patients.sort(key=utils.parse_patient_name)
patient_to_edfs = dict(patient_to_edfs)
# In[14]:
#write_list(our_patients, '/afs/csail.mit.edu/u/t/tzhan/eeg/coma_EEG_alice_zhan/patient_outcome_info/all_patient_names.txt')
# # Master Lookup
# ### List of all sids