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| import numpy as np | |
| import pandas as pd | |
| import gradio as gr | |
| from funcs.convertors import slice_csv_to_json, slice_csv_to_json_v2 | |
| from funcs.plot_func import plot_sensor_data_from_json | |
| def process_data(input_file, slice_size=64, sample_rate=20, window_size=40, min_slice_size=10, threshold=1000, span_limit=10000000): | |
| # Read the data from the file, including the CRC column | |
| try: | |
| if input_file.name is None: | |
| return None, None, None, None, None, None, None, None, None | |
| data = pd.read_csv(input_file.name, delimiter=";", index_col="NR", usecols=["NR", "TS", "LEG", "GX", "GY", "GZ", "AX", "AY", "AZ", "CRC"]) | |
| except: | |
| if input_file is None: | |
| return None, None, None, None, None, None, None, None, None | |
| data = pd.read_csv(input_file, delimiter=";", index_col="NR", usecols=["NR", "TS", "LEG", "GX", "GY", "GZ", "AX", "AY", "AZ", "CRC"]) | |
| # Replace the values with NaN when the CRC value is not zero | |
| data.loc[data["CRC"] != 0, ["GX", "GY", "GZ", "AX", "AY", "AZ"]] = np.nan | |
| # Drop the CRC column as it is not needed anymore | |
| data = data.drop(columns="CRC") | |
| # Pivot the table to have only one line per timestamp but more columns | |
| data = data.pivot_table(values=["GX", "GY", "GZ", "AX", "AY", "AZ"], index="TS", columns="LEG") | |
| # Flatten the multi-level columns | |
| data.columns = [f"{col[0]}{col[1]}" for col in data.columns] | |
| # Sort the index (timestamps) | |
| data = data.sort_index() | |
| # Check if the span between min and max is too large, and limit it if necessary | |
| min_ts = data.index.min() | |
| max_ts = data.index.max() | |
| if (max_ts - min_ts) > span_limit: | |
| max_ts = min_ts + span_limit | |
| data = data[data.index <= max_ts] | |
| # Check if the timestamp distance is 20 ms and add timestamps necessary | |
| new_index = pd.RangeIndex(start=min_ts, stop=max_ts + 20, step=20) | |
| data = data.reindex(new_index) | |
| # Fill missing values with NaN | |
| data = data.replace(0, np.nan) | |
| # Check if the gap between two timestamps is bigger than 80 ms and show a warning | |
| gaps = data.isna().all(axis=1).astype(int).groupby(data.notna().all(axis=1).astype(int).cumsum()).sum() | |
| big_gaps = gaps[gaps > 3] | |
| if not big_gaps.empty: | |
| gap_start_index = big_gaps.index[0] * 20 | |
| gap_size = big_gaps.iloc[0] * 20 | |
| # print(f"Warning: gap of {gap_size} ms found at line {gap_start_index}") | |
| # Save the data up to the point where there is a gap of more than 80 ms | |
| data = data.iloc[:gap_start_index] | |
| # Calculate the absolute differences between consecutive rows for all channels | |
| differences = data.diff().abs() | |
| # Find the index where all differences are below the threshold | |
| no_significant_change_index = differences[differences.lt(threshold).all(axis=1)].index | |
| # if not no_significant_change_index.empty: | |
| # # Save the data up to the point where no significant change appears in all channels | |
| # data = data.loc[:no_significant_change_index[0]] | |
| # return None, None, f'Warning: Significantly shortened > check the recordings', None, None, None, None, None, None | |
| # Save the resulting DataFrame to a new file | |
| data.to_csv('output.csv', sep=";", na_rep="NaN", float_format="%.0f") | |
| file, len_, time_list = slice_csv_to_json('output.csv', slice_size, min_slice_size, sample_rate, window_size=window_size) | |
| # file, len_ = slice_csv_to_json_v2('output.csv', slice_size, min_slice_size, sample_rate) | |
| # get the plot automatically | |
| sensor_fig, slice_fig, get_all_slice, slice_json, overlay_fig = plot_sensor_data_from_json(file, "GZ1") # with the csv file | |
| # overlay_fig = plot_overlay_data_from_json(file, ["GZ1", "GZ2", "GZ3", "GZ4"]) | |
| return 'output.csv', file, f'num of slices found: {len_}', sensor_fig, overlay_fig, gr.Slider.update(interactive=True, maximum=len_, minimum=1, value=1), slice_fig, get_all_slice, slice_json, time_list |