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Former-commit-id: ab1e87b
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@skim2257
skim2257 committed Jun 16, 2022
1 parent b62875b commit b25df48
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Showing 2 changed files with 52 additions and 32 deletions.
74 changes: 49 additions & 25 deletions imgtools/modules/datagraph.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -277,7 +277,6 @@ def parser(self, query_string: str) -> pd.DataFrame:
final_df["index_chng"] = final_df.index.astype(str) + "_" + final_df["patient_ID"]
final_df.set_index("index_chng", inplace=True)
final_df.rename_axis(None, inplace=True)
print(final_df.columns)
#change relative paths to absolute paths
for col in final_df.columns:
if col.startswith("folder"):
Expand Down Expand Up @@ -322,24 +321,22 @@ def graph_query(self,
# Fetch the required data. Checks whether each study has edge 4 and (1 or (2 and 0)). Can remove later
relevant_study_id = self.df_new.loc[(self.df_new.edge_type.str.contains(regex_term)), "study_x"].unique()

# Based on the correct study ids, fetches are the relevant edges
# Based on the correct study ids, fetches the relevant edges
df_processed = self.df_edges.loc[self.df_edges.study_x.isin(relevant_study_id) & (self.df_edges.edge_type.isin(edge_list))]
# print(df_processed.to_csv("/cluster/home/sejinkim/projects/process/tcga_impatient.csv"))

# The components are deleted if it has less number of nodes than the passed modalities, change this so as to alter that condition
final_df = self._get_df(df_processed, relevant_study_id, remove_less_comp)
print('after _get_df', final_df.columns)

# Removing columns
if len(change_df) > 0:
# Find columns with change_df string present
col_ids = [cols for cols in final_df.columns if change_df not in cols]
final_df = final_df[col_ids]

if return_components:
return self.final_dict
else:
return final_df
# if return_components:
# return self.final_dict
# else:
return final_df

def _form_agg(self):
'''
Expand Down Expand Up @@ -377,14 +374,46 @@ def _get_df(self,
remove_less_comp
True for removing components with less number of edges than the query
Changelog
---------
* June 14th, 2022: Changing from studyID-based to sample-based for loop
'''
#Storing all the components across all the studies
self.final_dict = []
final_df = []
#For checking later if all the required modalities are present in a component or not
mods_wanted = set(self.mods)

# per-sample
for i in range(len(df_edges_processed)):
row = df_edges_processed.iloc[i]
series = row.series_x
modality = row.modality_x
folder = row.folder_x

series_y = row.series_y
modality_y = row.modality_y
folder_y = row.folder_y

temp = {"study": row.study_x,
series: {"modality": modality,
"folder": folder},
row.series_y: {"modality": modality_y,
"folder": folder_y,
"conn_to": modality}}

folder_save = {'study': row.study_x,
'patient_ID': row.patient_ID_x,
f'series_{modality}': series,
f'folder_{modality}': folder,
f'series_{modality_y}': series_y,
f'series_{modality_y}': folder_y}



#Determine the number of components
for i in range(len(rel_studyids)):
for i in range(len(rel_studyids)): # per study_id
df_temp = df_edges_processed.loc[df_edges_processed.study_x == rel_studyids[i]]
CT_locs = df_temp.loc[df_temp.modality_x.isin(['CT', 'MR'])]
comp = CT_locs.series_x.unique()
Expand All @@ -408,45 +437,39 @@ def _get_df(self,
folder_save[f"series_{df_connections['modality_x'].iloc[0]}"] = comp[j]
folder_save[f"folder_{df_connections['modality_x'].iloc[0]}"] = df_connections["folder_x"].iloc[0]
temp_dfconn = df_connections[["series_y", "modality_y", "folder_y"]]

for k in range(len(temp_dfconn)):
#This loop stores connection of the CT
temp[temp_dfconn.iloc[k,0]] = {}
temp[temp_dfconn.iloc[k,0]]["modality"] = temp_dfconn.iloc[k,1]
temp[temp_dfconn.iloc[k,0]]["folder"] = temp_dfconn.iloc[k,2]
temp[temp_dfconn.iloc[k,0]]["conn_to"] = temp[comp[j]]["modality"] #CT/MR
temp[temp_dfconn.iloc[k,0]] = {"modality": temp_dfconn.iloc[k,1], "folder": temp_dfconn.iloc[k,2], "conn_to": temp[comp[j]]["modality"]}
#Checks if there is already existing connection
key,key_series = self._check_save(folder_save,temp_dfconn.iloc[k,1],temp[comp[j]]["modality"]) #CT/MR
key, key_series = self._check_save(folder_save,temp_dfconn.iloc[k,1],temp[comp[j]]["modality"]) #CT/MR
folder_save[key_series] = temp_dfconn.iloc[k,0]
folder_save[key] = temp_dfconn.iloc[k,2]
A.append(temp)
save_folder_comp.append(folder_save)

#For rest of the edges left out, the connections are formed by going through the dictionary. For cases such as RTstruct-RTDose and PET-RTstruct
rest_locs = df_temp.loc[~df_temp.modality_x.isin(['CT', 'MR']), ["series_x", "modality_x","folder_x", "series_y", "modality_y", "folder_y"]]

flag = 0
for j in range(len(rest_locs)):
for k in range(len(comp)):
if rest_locs.iloc[j,0] in A[k]:
A[k][rest_locs.iloc[j,3]] = {}
A[k][rest_locs.iloc[j,3]]["modality"] = rest_locs.iloc[j,4]
A[k][rest_locs.iloc[j,3]]["folder"] = rest_locs.iloc[j,5]
A[k][rest_locs.iloc[j,3]]["conn_to"] = rest_locs.iloc[j,1]
A[k][rest_locs.iloc[j,3]] = {"modality": rest_locs.iloc[j,4], "folder": rest_locs.iloc[j,5], "conn_to": rest_locs.iloc[j,1]}
if rest_locs.iloc[j,4]=="RTDOSE":
#RTDOSE is connected via either RTstruct or/and CT, but we usually don't care, so naming it commonly
key,key_series = self._check_save(save_folder_comp[k],rest_locs.iloc[j,4], "CT")
key, key_series = self._check_save(save_folder_comp[k],rest_locs.iloc[j,4], "CT")
save_folder_comp[k][key_series] = rest_locs.iloc[j,3]
save_folder_comp[k][key] = rest_locs.iloc[j,5]
else: #Cases such as RTSTRUCT-PT
#if there is already a connection and one more same category modality wants to connect
key,key_series = self._check_save(save_folder_comp[k],rest_locs.iloc[j,4],rest_locs.iloc[j,1])
key, key_series = self._check_save(save_folder_comp[k],rest_locs.iloc[j,4],rest_locs.iloc[j,1])
save_folder_comp[k][key_series] = rest_locs.iloc[j,3]
save_folder_comp[k][key] = rest_locs.iloc[j,5]
flag = 0
else:
flag = 1
if flag==1:
raise ValueError(f"In studyID: {rel_studyids[i]}, one of the edges had no match to any of the components which start from CT. Please check the data")
raise ValueError(f"In studyID: {rel_studyids[i]}, one of the edges had no match to any of the components which start from CT/MR. Please check the data")

remove_index = []
if remove_less_comp:
Expand All @@ -459,8 +482,9 @@ def _get_df(self,
save_folder_comp = [save_folder_comp[idx] for idx in remove_index]
A = [A[idx] for idx in remove_index]

self.final_dict = self.final_dict + A
final_df = final_df + save_folder_comp
self.final_dict.extend(A)
final_df.extend(save_folder_comp)

final_df = pd.DataFrame(final_df)
return final_df

Expand All @@ -472,7 +496,7 @@ def _check_save(save_dict,node,dest):
while key in save_dict.keys():
key = f"folder_{node}_{dest}_{i}"
key_series = f"series_{node}_{dest}_{i}"
i+=1
i +=1
return key,key_series

@staticmethod
Expand Down
10 changes: 3 additions & 7 deletions imgtools/ops/ops.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,6 @@ def __init__(self,
self.output_streams = [("_").join(cols.split("_")[1:]) for cols in self.df_combined.columns if cols.split("_")[0] == "folder"]
self.column_names = [cols for cols in self.df_combined.columns if cols.split("_")[0] == "folder"]
self.series_names = [cols for cols in self.df_combined.columns if cols.split("_")[0] == "series"]

print(f"There are {len(self.df_combined)} cases containing all {modalities} modalities.")

self.readers = [read_dicom_auto for _ in range(len(self.output_streams))]
Expand Down Expand Up @@ -324,17 +323,14 @@ def __init__(self,
output_streams: List[str],
nnunet_info: Dict = None):

# File types
# print(self.file_name)

self.output = {}
print(output_streams)
for colname in output_streams:
# Not considering colnames ending with alphanumeric
colname_process = ("_").join([item for item in colname.split("_") if item.isnumeric()==False])
colname_process = colname #temproary force #
if not nnunet_info:
self.output[colname_process] = ImageSubjectFileOutput(pathlib.Path(root_directory,"{subject_id}",colname_process).as_posix(),
filename_format=colname_process+".nii.gz")
self.output[colname_process] = ImageSubjectFileOutput(pathlib.Path(root_directory,"{subject_id}",colname_process.split(".")[0]).as_posix(),
filename_format="{}.nii.gz".format(colname_process))
else:
self.output[colname_process] = ImageSubjectFileOutput(pathlib.Path(root_directory,"{label_or_image}{train_or_test}").as_posix(),
filename_format="{subject_id}_{modality_index}.nii.gz")
Expand Down

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