5505-public/dima
2
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Moved dataframe_to_np_structured_array(df: pd.DataFrame) to src/g5505_utils.py. This is a more generic function that can be used more broadly accross modules.

Browse Source
This commit is contained in:
florez_j
2024-06-16 18:25:08 +02:00
parent 6f5c49dc64
commit 2d4ecec806
1 changed files with 1 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
src/g5505_file_reader.py
View File

@ -90,28 +90,6 @@ def infer_units(column_name):
return match
def dataframe_to_np_structured_array(df: pd.DataFrame):
# Define the dtype for the structured array, ensuring compatibility with h5py
dtype = []
for col in df.columns:
col_dtype = df[col].dtype
if pd.api.types.is_string_dtype(col_dtype):
# Convert string dtype to fixed-length strings
max_len = df[col].str.len().max()
dtype.append((col, f'S{max_len}'))
elif pd.api.types.is_integer_dtype(col_dtype):
dtype.append((col, 'i4')) # Assuming 32-bit integer
elif pd.api.types.is_float_dtype(col_dtype):
dtype.append((col, 'f4')) # Assuming 32-bit float
else:
raise ValueError(f"Unsupported dtype: {col_dtype}")
# Convert the DataFrame to a structured array
structured_array = np.array(list(df.itertuples(index=False, name=None)), dtype=dtype)
return structured_array
from collections import Counter
def read_txt_files_as_dict(filename : str , work_with_copy : bool = True ):
@ -271,7 +249,7 @@ def read_txt_files_as_dict(filename : str , work_with_copy : bool = True ):
if numerical_variables:
dataset = {}
dataset['name'] = 'data_table'#_numerical_variables'
dataset['data'] = dataframe_to_np_structured_array(pd.concat((df_categorical_attrs,df_numerical_attrs),axis=1)) #df_numerical_attrs.to_numpy()
dataset['data'] = utils.dataframe_to_np_structured_array(pd.concat((df_categorical_attrs,df_numerical_attrs),axis=1)) #df_numerical_attrs.to_numpy()
dataset['shape'] = dataset['data'].shape
dataset['dtype'] = type(dataset['data'])
#dataset['data_units'] = file_obj['wave']['data_units']