subdir_list = []
for der in der_names:
temp_name = subdir_name + "/" + der
subdir_list.append(temp_name)
init_kwargs = {
"input_directory": input_dir,
"save_directory": save_dir,
"subdir_name": subdir_name,
"func": (prep, "read_file_to_df"),
"search_suffix": ".csv",
"readfile": True,
"index_col": [0, 1, 2],
"header": [0]
}
delta_create_object = prep.SaveObjectPipeline(**init_kwargs)
delta_process_kwargs = {
"function": (prep, "create_df_for_single_band"),
"savecsv": False,
"name_of_band": ["Delta"],
"range_to_sum": ("0.50Hz", "4.00Hz")
}
delta_create_object.process_file(**delta_process_kwargs)
for der_no, subdir in enumerate(subdir_list):
init_kwargs["subdir_name"] = subdir
cumsum_plot_object = prep.SaveObjectPipeline(**init_kwargs)
process_kwargs = {
"function": (prep, "create_stage_df"),
"savecsv": False,
input_dir = pathlib.Path("/Users/angusfisk/Documents/01_PhD_files/01_projects"
"/P3_LLEEG_Chapter3/01_data_files/07_clean_fft_files")
save_dir = input_dir.parents[1] / "03_analysis_outputs"
subdir_name = "01_delta_hypnograms"
init_kwargs = {
"input_directory": input_dir,
"save_directory": save_dir,
"subdir_name": subdir_name,
"func": (prep, "read_file_to_df"),
"search_suffix": ".csv",
"readfile": True,
"index_col": [0, 1, 2],
"header": [0]
}
hypnogram_object = prep.SaveObjectPipeline(**init_kwargs)
process_kwargs = {"function": (prep, "_sep_by_top_index"), "savecsv": False}
hypnogram_object.process_file(**process_kwargs)
plot_kwargs = {
"function": (plot, "plot_hypnogram_from_list"),
"remove_col": False,
"data_list": hypnogram_object.processed_list,
"showfig": False,
"savefig": True,
"figsize": (10, 10),
"name_of_band": ["Delta"],
"range_to_sum": ("0.50Hz", "4.00Hz"),
"level_of_index": 0,
"label_col": -1,
save_dir = input_dir.parents[1]
subdir_name = "01_data_files/08_stage_csv"
plot_subdir_name = "03_analysis_outputs/02_cumulative_plots/01_cumulative_sleep"
plot_subdir_path = prep.create_subdir(save_dir, plot_subdir_name)
init_kwargs = {
"input_directory": input_dir,
"save_directory": save_dir,
"subdir_name": subdir_name,
"func": (prep, "read_file_to_df"),
"search_suffix": ".csv",
"readfile": True,
"index_col": [0, 1, 2],
"header": [0]
}
cumulative_sleep_object = prep.SaveObjectPipeline(**init_kwargs)
process_kwargs = {
"function": (prep, "create_stage_df"),
"savecsv": True,
}
cumulative_sleep_object.process_file(**process_kwargs)
plot_kwargs = {
"function": (plot, "plot_cumulative_from_stage_df"),
"data_list": cumulative_sleep_object.processed_list,
"remove_col": False,
"subdir_path": plot_subdir_path,
"stages": ["NR", "R", "NR1", "R1"],
"base_freq": "4S",
"target_freq": "1H",
import pathlib
import sys
sys.path.insert(0, "/Users/angusfisk/Documents/01_PhD_files/07_python_package/"
"sleepPy")
import sleepPy.preprocessing as prep
input_dir = pathlib.Path("/Users/angusfisk/Documents/01_PhD_files/"
"01_projects/P3_LLEEG_Chapter3/01_data_files/"
"06_fft_files")
save_dir = input_dir.parent
subdir_name = "07_clean_fft_files"
clean_object = prep.SaveObjectPipeline(input_directory=input_dir,
save_directory=save_dir,
search_suffix=".txt",
readfile=False,
subdir_name=subdir_name)
animal_file_list = prep.create_dict_of_animal_lists(clean_object.file_list,
input_dir,
anim_range=(0,3))
kwargs = {
"save_suffix_file": "_clean.csv",
"savecsv": True,
"function": (prep, "single_df_for_animal"),
"object_list": animal_file_list.values(),
"file_list": animal_file_list.keys(),
"header": 17,
"derivation_list": ["fro", "occ", "foc"],
for stage in stage_list:
for der in der_names:
temp_name = subdir_name + "/" + stage + "/" + der
subdir_list.append(temp_name)
init_kwargs = {
"input_directory": input_dir,
"save_directory": save_dir,
"subdir_name": subdir_name,
"func": (prep, "read_file_to_df"),
"search_suffix": ".csv",
"readfile": True,
"index_col": [0, 1, 2],
"header": [0]
}
spectrum_object = prep.SaveObjectPipeline(**init_kwargs)
process_kwargs = {
"function": (prep, "_get_spectrum_between_times"),
"savecsv": False,
"stage": ["W"],
"stage_col": "Stage",
"time_start": "12:00:00",
"time_end": "00:00:00",
}
spectrum_object.process_file(**process_kwargs)
spectrum_list = prep._get_spectrum_between_times(df, **kwargs)
spectrum_df = spectrum_list[0]