c_map = fmts.ColorMap()
c_map.assign_new_c_map(plt.get_cmap("tab10").colors)
a_paths = []
for cur_data in data:
if cur_data.line_calculation_settings_calculate == False: continue
else: pass
cur_data.root_dir = fio.get_part_of_path(
cur_data.root_dir_settings_root_dir, -1)
cur_data.all_files_paths = fio.get_files(
cur_data.root_dir_settings_root_dir, ["csv"], [], [])
a_paths = fio.get_files(cur_data.root_dir_settings_root_dir)
cur_data.file_names = fio.get_parts_of_paths_list(cur_data.all_files_paths,
-1)
cur_data.last_dirs_names = fio.get_parts_of_paths_list(
cur_data.all_files_paths, -2)
# root_dir = fio.get_part_of_given_os_path(cur_data.root_dir_settings_root_dir, -1)
# all_files_paths = fio.get_files_from_dirs(cur_data.root_dir_settings_root_dir,[],[], ["csv"])
# a_paths = fio.get_files_from_dirs(cur_data.root_dir_settings_root_dir)
# file_names = fio.get_parts_of_given_os_path_list(cur_data.all_files_paths, -1)
# last_dirs_names = fio.get_parts_of_given_os_path_list(cur_data.all_files_paths, -2)
if cur_data.line_label_plot_settings_text == "file_name":
cur_data.labels_line = [
"{}{}{}".format(cur_data.line_label_plot_settings_text_pref, i,
cur_data.line_label_plot_settings_text_post)
for i in cur_data.file_names
]
apaths = fio.get_files(cwd, extension = ['xlsx'], contains = [res_name], not_contains=['~'], print_path=False)
for name in apaths:
calc = input("results file has been found, calculate again ? ")
"""
# =============================================================================
# CALCULATION STARTING POINT
# =============================================================================
"""
if calc == '1' or calc == 'y':
# =============================================================================
# getting all data file names
# =============================================================================
abs_paths = fio.get_files(cwd, extension = ['xlsm'], contains = included_dirs_keywords, not_contains = excluded_dirs_keywords, print_path=False)
root_dirs = fio.get_parts_of_paths_list(abs_paths, -3)
sample_dirs = fio.get_parts_of_paths_list(abs_paths, -2)
filenames = fio.get_parts_of_paths_list(abs_paths, -1)
# =============================================================================
# filtering all data-files based on conditions, calculating means of selected values
# =============================================================================
filtered_frames_list = []
for num, (abs_path, root_dir, sample_dir, filename) in enumerate(zip(abs_paths, root_dirs, sample_dirs, filenames)):
print('Processing file ({}/{}) ........ {} / {} / {}'.format( '{x:02d}'.format(x=num+1), '{x:02d}'.format(x=len(abs_paths)), sample_dir, filename, root_dir))
input_frame = pd.read_excel(abs_path, usecols=column_indexes, skiprows=skip_rows).apply(pd.to_numeric, errors='coerce', downcast='float')
input_frame = input_frame.dropna(axis=0, how='any').reset_index(drop=True)
input_frame.columns = column_names
input_frame = input_frame[sorted_columns]
script_path = os.path.normpath(os.path.abspath(__file__))
cwd = os.path.split(script_path)[0]
os.makedirs('{}/{}'.format(cwd, 'results'), exist_ok=True)
def find_nearest(array,value):
idx = np.searchsorted(array, value, side="left")
if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])):
return idx-1
else:
return idx
columns = ['sample','assembly force 1', 'assembly force 2', 'assembly force 3', 'assembly force 4', 'assembly force 5', 'assembly force 6', 'assembly force 7', 'assembly force 8', 'assembly force 9', 'assembly force 10']
llist = []
files = fio.get_files(cwd, extension=['csv'])
dirs = fio.get_parts_of_paths_list(files, -2)
for file, dirr in zip(files, dirs):
data = fcsv.read_data_from_csv_file(file, [0,2], 7, None, 0)
data = fdp.nested_list_to_numpy_arr(data)
fig = plt.figure(figsize=(13,7))
plt.plot(data[1], data[0])
plt.title(dirr)
points = np.arange(0,11)*34
for index, i in enumerate(points):
if index == 0:
points[index] = 0
elif index == 1: