def gif_lobes_from_excel_sheets():
"""
sort the gif parcellations as per excel gif sheet lobes.
e.g. GIF FL = GIF Frontal Lobe - has a list of gif parcellations
which we want to see in 3D slicer, using the GUI
"""
_, _, excel_path, _ = file_paths()
GIF_SHEET_NAMES = gif_sheet_names()
lobes_mapping = {}
for gif_lobe in GIF_SHEET_NAMES:
gif_parcellations = pd.read_excel(
excel_path,
header=None,
usecols="A:B",
sheet_name=gif_lobe,
engine="openpyxl",
)
gif_parcellations.dropna(axis=0, how='any', inplace=True)
gif_parcellations.dropna(axis=1, how='all', inplace=True)
gifs = gif_parcellations.astype({1: 'uint16'})
gifs = gifs.iloc[:, 1]
lobes_mapping[gif_lobe] = gifs.values
return lobes_mapping
from mega_analysis.crosstab.file_paths import file_paths
from mega_analysis.crosstab.gif_sheet_names import gif_sheet_names
from mega_analysis.crosstab.mega_analysis.exclusions import exclusions
from mega_analysis.crosstab.mega_analysis.MEGA_ANALYSIS import MEGA_ANALYSIS
from mega_analysis.semiology import ( # semiology_dict_path,
QUERY_SEMIOLOGY, Laterality, Semiology, gif_lat_file, map_df_dict)
from mega_analysis.crosstab.hierarchy_class import Hierarchy
# define paths: note dummy data has a tab called test_counts
# a hand crafted test fixture count
repo_dir, resources_dir, dummy_data_path, dummy_semiology_dict_path = \
file_paths(dummy_data=True)
# Define the gif sheet names
gif_sheet_names = gif_sheet_names()
# Read Excel file only three times at initialisation
test_df, _, _, _, _, _, _ = MEGA_ANALYSIS(
excel_data=dummy_data_path,
n_rows=100,
usecols="A:DH",
header=1,
exclude_data=False,
plot=True,
)
map_df_dict = pd.read_excel(
dummy_data_path,
header=1,
sheet_name=gif_sheet_names,