def test_read_file_to_df_funct(self):
"""
Create df with index and 10 rows
Save that to a csv file
Read that csv file
Check the index 0 is correct
Check index 0 dtype is a timestamp
Check length of df is correct.
:return:
"""
# check reads file correctly
# use the function to read the file.
df_testread = preprocessing.read_file_to_df(self.save_str)
# check index [0] is correct
first_index = (df_testread.index[0])
self.assertEqual(str(first_index), self.start)
# check index is a timestamp.
self.assertEqual(type(first_index), pd.Timestamp)
# check length is correct
self.assertEqual(len(df_testread), 100)
"xlabel": "Time",
"ylabel": "Days",
"interval": 6,
"title": "Double Plotted Actogram",
}
return fig, ax[-1], params_dict
# read in the data file
file_dir = pathlib.Path("/Users/angusfisk/Documents/01_PhD_files/01_projects"
"/01_thesisdata/04_ageing"
"/01_datafiles/01_activity")
file = sorted(file_dir.glob("*.csv"))[0]
df = prep.read_file_to_df(file, index_col=0)
# resample to hourly so can actually work with this data
df_h = df.resample("H").mean()
# try and do a loop with one for each
actogram_kwargs = {
"drop_level": False,
"set_file_title": False,
"linewidth": 0.1,
}
fig, ax = plt.subplots(nrows=3, ncols=2)
for anim, subplot in enumerate(ax.flatten()):
file_dir = pathlib.Path(
"/Users/angusfisk/Documents/01_PhD_files/01_projects"
"/01_thesisdata/04_ageing"
"/01_datafiles/01_activity/03_single_housed/01_concat"
)
save_dir = pathlib.Path(
"/Users/angusfisk/Documents/"
"01_PhD_files/01_projects/01_thesisdata/04_ageing/"
"03_analysisoutputs/01_figures"
)
save_fig = save_dir / "04_fig4.png"
files = sorted(file_dir.glob("*.csv"))
file_names = [x.stem.upper() for x in files]
df_list = [prep.read_file_to_df(x, index_col=[0, 1]) for x in files]
df_dict = dict(zip(file_names, df_list))
all_df = pd.concat(df_dict, sort=False)
# PIRS 6-11 look like they had LL for a few weeks from the actograms
# No notes to support what actually happened but unsure so excluding
all_df.loc[idx["LD"], "7":"12"] = np.nan
all_df.loc[idx["SJ"], "10"] = np.nan
# JUST use LD for now? - redo as new figure for DD
light_df = all_df.loc[idx[:, "ld", :], :].copy()
light_df.index = light_df.index.droplevel(1)
all_df = light_df.copy()
# constants
conditions = all_df.index.get_level_values(0).unique()
0, "/Users/angusfisk/Documents/01_PhD_files/"
"07_python_package/actiPy")
import actiPy.preprocessing as prep
import actiPy.actogram_plot as aplot
# read in the data files
file_dir = pathlib.Path("/Users/angusfisk/Documents/01_PhD_files/01_projects"
"/01_thesisdata/04_ageing"
"/01_datafiles/01_activity")
save_dir = pathlib.Path("/Users/angusfisk/Documents/"
"01_PhD_files/01_projects/01_thesisdata/04_ageing/"
"03_analysisoutputs/01_figures")
files = sorted(file_dir.glob("*.csv"))
file_names = [x.stem for x in files]
df_list = [prep.read_file_to_df(x, index_col=0) for x in files]
df_dict = dict(zip(file_names, df_list))
all_df = pd.concat(df_dict, sort=False)
# read in the post_disrupted files
post_file_dir = pathlib.Path(
"/Users/angusfisk/Documents/01_PhD_files/01_projects"
"/01_thesisdata/04_ageing"
"/01_datafiles/01_activity/01_post_disrupt")
post_files = sorted(post_file_dir.glob("*.csv"))
post_file_names = [x.stem for x in post_files]
post_list = [prep.read_file_to_df(x, index_col=0) for x in post_files]
post_dict = dict(zip(post_file_names, post_list))
post_df = pd.concat(post_dict, sort=False)
# shift to lights on