| import re |
| import ast |
| import pandas as pd |
| import numpy as np |
|
|
|
|
| def parse_list_of_lists(cell): |
| |
| cell = ( |
| re.sub(r"\s+", ",", cell.replace("\n", "")) |
| .replace("[,", "[") |
| .replace(",,", ",") |
| ) |
| |
| return ast.literal_eval(cell) |
|
|
|
|
| def load_event_df(path): |
| """load CSV containing localized events, parsing columns that contain lists""" |
| df = pd.read_csv( |
| path, |
| index_col=0, |
| converters={ |
| "receiver_files": parse_list_of_lists, |
| "receiver_locations": parse_list_of_lists, |
| "tdoas": parse_list_of_lists, |
| "cc_maxs": parse_list_of_lists, |
| "receiver_start_time_offsets": parse_list_of_lists, |
| "distance_residuals": parse_list_of_lists, |
| "location_estimate": parse_list_of_lists, |
| }, |
| parse_dates=["start_timestamp"], |
| ) |
| df["residual_rms"] = np.sqrt( |
| df["distance_residuals"].apply(lambda x: np.mean(np.square(x))) |
| ) |
| df["x"] = np.array(list(df.location_estimate.values))[:, 0] |
| df["y"] = np.array(list(df.location_estimate.values))[:, 1] |
|
|
| return df |
|
|
