diff --git a/src/data/mat_to_csv.py b/src/data/mat_to_csv.py deleted file mode 100644 index 0033ea9..0000000 --- a/src/data/mat_to_csv.py +++ /dev/null @@ -1,263 +0,0 @@ -""" -Converts raw .mat files into a flattened .csv structure which can be imported into python pandas. -""" - -import logging.config -from datetime import datetime, timedelta - -import pandas as pd -from mat4py import loadmat -import numpy as np - -logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False) -logger = logging.getLogger(__name__) - - -def parse_orientations(orientations_mat): - """ - Parses the raw orientations.mat file and returns a pandas dataframe. Note that orientations are the direction - towards land measured in degrees anti-clockwise from east. - :param orientations_mat: - :return: - """ - logger.info('Parsing %s', orientations_mat) - mat_data = loadmat(orientations_mat)['output'] - rows = [] - for i in range(0, len(mat_data['beach'])): - rows.append({ - 'beach': mat_data['beach'][i], - 'orientation': mat_data['orientation'][i], - 'lat_center': mat_data['lat_center'][i], - 'lon_center': mat_data['lon_center'][i], - 'lat_land': mat_data['lat_land'][i], - 'lon_land': mat_data['lon_land'][i], - 'lat_sea': mat_data['lat_sea'][i], - 'lon_sea': mat_data['lon_sea'][i], - }) - - df = pd.DataFrame(rows) - return df - -def combine_sites_and_orientaions(df_sites, df_orientations): - """ - Replaces beach/lat/lon columns with the unique site_id. - :param dfs: - :param df_sites: - :return: - """ - df_merged_sites = df_sites.merge(df_orientations[['beach', 'lat_center', 'lon_center', 'orientation']], - left_on=['beach', 'lat', 'lon'], - right_on=['beach', 'lat_center', 'lon_center']) - - # Check that all our records have a unique site identifier - n_unmatched = len(df_sites) - len(df_merged_sites) - if n_unmatched > 0: - logger.warning('Not all records (%d of %d) matched with an orientation', n_unmatched, len(df_sites)) - - # Drop extra columns - df_merged_sites = df_merged_sites.drop(columns = ['lat_center', 'lon_center']) - - return df_merged_sites - -def specify_lat_lon_profile_center(df_sites, x_val=200): - """ - Specify which x-coordinate in the beach profile cross section the lat/lon corresponds to - :param df_sites: - :return: - """ - df_sites['profile_x_lat_lon'] = x_val - return df_sites - -def parse_waves(waves_mat): - """ - Parses the raw waves.mat file and returns a pandas dataframe - :param waves_mat: - :return: - """ - logger.info('Parsing %s', waves_mat) - mat_data = loadmat(waves_mat)['data'] - rows = [] - for i in range(0, len(mat_data['site'])): - for j in range(0, len(mat_data['dates'][i])): - rows.append({ - 'beach': mat_data['site'][i], - 'lon': mat_data['lon'][i], - 'lat': mat_data['lat'][i], - 'datetime': matlab_datenum_to_datetime(mat_data['dates'][i][j][0]), - 'Hs': mat_data['H'][i][j][0], - 'Hs0': mat_data['Ho'][i][j][0], - 'Tp': mat_data['T'][i][j][0], - 'dir': mat_data['D'][i][j][0], - 'E': mat_data['E'][i][j][0], - 'P': mat_data['P'][i][j][0], - 'Exs': mat_data['Exs'][i][j][0], - 'Pxs': mat_data['Pxs'][i][j][0], - }) - - df = pd.DataFrame(rows) - df['datetime'] = df['datetime'].dt.round('1s') - return df - - -def parse_tides(tides_mat): - """ - Parses the raw tides.mat file and returns a pandas dataframe - :param tides_mat: - :return: - """ - logger.info('Parsing %s', tides_mat) - mat_data = loadmat(tides_mat)['data'] - rows = [] - for i in range(0, len(mat_data['site'])): - for j in range(0, len(mat_data['time'])): - rows.append({ - 'beach': mat_data['site'][i][0], - 'lon': mat_data['lons'][i][0], - 'lat': mat_data['lats'][i][0], - 'datetime': matlab_datenum_to_datetime(mat_data['time'][j][0]), - 'tide': mat_data['tide'][i][j] - }) - - df = pd.DataFrame(rows) - df['datetime'] = df['datetime'].dt.round('1s') - return df - - -def parse_profiles(profiles_mat): - """ - Parses the raw profiles.mat file and returns a pandas dataframe - :param tides_mat: - :return: - """ - logger.info('Parsing %s', profiles_mat) - mat_data = loadmat(profiles_mat)['data'] - rows = [] - for i in range(0, len(mat_data['site'])): - for j in range(0, len(mat_data['pfx'][i])): - for profile_type in ['prestorm', 'poststorm']: - - if profile_type == 'prestorm': - z = mat_data['pf1'][i][j][0] - if profile_type == 'poststorm': - z = mat_data['pf2'][i][j][0] - - rows.append({ - 'beach': mat_data['site'][i], - 'lon': mat_data['lon'][i], - 'lat': mat_data['lat'][i], - 'profile_type': profile_type, - 'x': mat_data['pfx'][i][j][0], - 'z': z, - }) - - df = pd.DataFrame(rows) - return df - -def remove_zeros(df_profiles): - """ - When parsing the pre/post storm profiles, the end of some profiles have constant values of zero. Let's change - these to NaNs for consistancy. Didn't use pandas fillnan because 0 may still be a valid value. - :param df: - :return: - """ - - df_profiles = df_profiles.sort_index() - groups = df_profiles.groupby(level=['site_id','profile_type']) - for key, _ in groups: - logger.debug('Removing zeros from {} profile at {}'.format(key[1], key[0])) - idx_site = (df_profiles.index.get_level_values('site_id') == key[0]) & \ - (df_profiles.index.get_level_values('profile_type') == key[1]) - df_profile = df_profiles[idx_site] - x_last_ele = df_profile[df_profile.z!=0].index.get_level_values('x')[-1] - df_profiles.loc[idx_site & (df_profiles.index.get_level_values('x')>x_last_ele), 'z'] = np.nan - - return df_profiles - -def matlab_datenum_to_datetime(matlab_datenum): - # https://stackoverflow.com/a/13965852 - return datetime.fromordinal(int(matlab_datenum)) + timedelta(days=matlab_datenum % 1) - timedelta( - days=366) - - -def get_unique_sites(dfs, cols=['beach', 'lat', 'lon']): - """ - Generates a dataframe of unique sites based on beach names, lats and lons. Creates a unique site ID for each. - :param dfs: - :param cols: - :return: - """ - - rows = [] - df_all = pd.concat([df[cols] for df in dfs]) - beach_groups = df_all.groupby(['beach']) - for beach_name, beach_group in beach_groups: - site_groups = beach_group.groupby(['lat', 'lon']) - siteNo = 1 - for site_name, site_group in site_groups: - site = '{}{:04d}'.format(beach_name, siteNo) - rows.append({'site_id': site, - 'lat': site_name[0], - 'lon': site_name[1], - 'beach': beach_name}) - siteNo += 1 - - df = pd.DataFrame(rows) - - return df - - -def replace_unique_sites(df, df_sites, cols=['beach', 'lat', 'lon']): - """ - Replaces beach/lat/lon columns with the unique site_id - :param dfs: - :param df_sites: - :return: - """ - - df_merged = df.merge(df_sites, on=cols) - - # Check that all our records have a unique site identifier - n_unmatched = len(df) - len(df_merged) - if n_unmatched > 0: - logger.warning('Not all records (%d of %d) matched with a unique site', n_unmatched, len(df)) - - df_merged = df_merged.drop(columns=cols) - - return df_merged - - -def main(): - df_waves = parse_waves(waves_mat='./data/raw/processed_shorelines/waves.mat') - df_tides = parse_tides(tides_mat='./data/raw/processed_shorelines/tides.mat') - df_profiles = parse_profiles(profiles_mat='./data/raw/processed_shorelines/profiles.mat') - df_sites = get_unique_sites(dfs=[df_waves, df_tides, df_profiles]) - df_orientations = parse_orientations(orientations_mat='./data/raw/processed_shorelines/orientations.mat') - - logger.info('Identifying unique sites') - df_waves = replace_unique_sites(df_waves, df_sites) - df_tides = replace_unique_sites(df_tides, df_sites) - df_profiles = replace_unique_sites(df_profiles, df_sites) - - logger.info('Combine orientations into sites') - df_sites = combine_sites_and_orientaions(df_sites, df_orientations) - df_sites = specify_lat_lon_profile_center(df_sites) - - logger.info('Setting pandas index') - df_profiles.set_index(['site_id', 'profile_type', 'x'], inplace=True) - df_waves.set_index(['site_id', 'datetime'], inplace=True) - df_tides.set_index(['site_id', 'datetime'], inplace=True) - df_sites.set_index(['site_id'], inplace=True) - - logger.info('Nanning profile zero elevations') - df_profiles = remove_zeros(df_profiles) - - logger.info('Outputting .csv files') - df_profiles.to_csv('./data/interim/profiles.csv') - df_tides.to_csv('./data/interim/tides.csv') - df_waves.to_csv('./data/interim/waves.csv') - df_sites.to_csv('./data/interim/sites.csv') - logger.info('Done!') - - -if __name__ == '__main__': - main() diff --git a/src/data/parse_mat.py b/src/data/parse_mat.py new file mode 100644 index 0000000..2e0abda --- /dev/null +++ b/src/data/parse_mat.py @@ -0,0 +1,345 @@ +""" +Converts raw .mat files into a flattened .csv structure which can be imported into python pandas. +""" + +import logging.config +from datetime import datetime, timedelta +import click +import pandas as pd +from mat4py import loadmat +import numpy as np + +logging.config.fileConfig("./src/logging.conf", disable_existing_loggers=False) +logger = logging.getLogger(__name__) + + +def parse_orientations(orientations_mat): + """ + Parses the raw orientations.mat file and returns a pandas dataframe. Note that orientations are the direction + towards land measured in degrees anti-clockwise from east. + :param orientations_mat: + :return: + """ + logger.info("Parsing %s", orientations_mat) + mat_data = loadmat(orientations_mat)["output"] + rows = [] + for i in range(0, len(mat_data["beach"])): + rows.append( + { + "beach": mat_data["beach"][i], + "orientation": mat_data["orientation"][i], + "lat_center": mat_data["lat_center"][i], + "lon_center": mat_data["lon_center"][i], + "lat_land": mat_data["lat_land"][i], + "lon_land": mat_data["lon_land"][i], + "lat_sea": mat_data["lat_sea"][i], + "lon_sea": mat_data["lon_sea"][i], + } + ) + + df = pd.DataFrame(rows) + return df + + +def combine_sites_and_orientaions(df_sites, df_orientations): + """ + Replaces beach/lat/lon columns with the unique site_id. + :param dfs: + :param df_sites: + :return: + """ + df_merged_sites = df_sites.merge( + df_orientations[["beach", "lat_center", "lon_center", "orientation"]], + left_on=["beach", "lat", "lon"], + right_on=["beach", "lat_center", "lon_center"], + ) + + # Check that all our records have a unique site identifier + n_unmatched = len(df_sites) - len(df_merged_sites) + if n_unmatched > 0: + logger.warning("Not all records (%d of %d) matched with an orientation", n_unmatched, len(df_sites)) + + # Drop extra columns + df_merged_sites = df_merged_sites.drop(columns=["lat_center", "lon_center"]) + + return df_merged_sites + + +def specify_lat_lon_profile_center(df_sites, x_val=200): + """ + Specify which x-coordinate in the beach profile cross section the lat/lon corresponds to + :param df_sites: + :return: + """ + df_sites["profile_x_lat_lon"] = x_val + return df_sites + + +def parse_waves(waves_mat): + """ + Parses the raw waves.mat file and returns a pandas dataframe + :param waves_mat: + :return: + """ + logger.info("Parsing %s", waves_mat) + mat_data = loadmat(waves_mat)["data"] + rows = [] + for i in range(0, len(mat_data["site"])): + for j in range(0, len(mat_data["dates"][i])): + rows.append( + { + "beach": mat_data["site"][i], + "lon": mat_data["lon"][i], + "lat": mat_data["lat"][i], + "datetime": matlab_datenum_to_datetime(mat_data["dates"][i][j][0]), + "Hs": mat_data["H"][i][j][0], + "Hs0": mat_data["Ho"][i][j][0], + "Tp": mat_data["T"][i][j][0], + "dir": mat_data["D"][i][j][0], + "E": mat_data["E"][i][j][0], + "P": mat_data["P"][i][j][0], + "Exs": mat_data["Exs"][i][j][0], + "Pxs": mat_data["Pxs"][i][j][0], + } + ) + + df = pd.DataFrame(rows) + df["datetime"] = df["datetime"].dt.round("1s") + return df + + +def parse_tides(tides_mat): + """ + Parses the raw tides.mat file and returns a pandas dataframe + :param tides_mat: + :return: + """ + logger.info("Parsing %s", tides_mat) + mat_data = loadmat(tides_mat)["data"] + rows = [] + for i in range(0, len(mat_data["site"])): + for j in range(0, len(mat_data["time"])): + rows.append( + { + "beach": mat_data["site"][i][0], + "lon": mat_data["lons"][i][0], + "lat": mat_data["lats"][i][0], + "datetime": matlab_datenum_to_datetime(mat_data["time"][j][0]), + "tide": mat_data["tide"][i][j], + } + ) + + df = pd.DataFrame(rows) + df["datetime"] = df["datetime"].dt.round("1s") + return df + + +def parse_profiles(profiles_mat): + """ + Parses the raw profiles.mat file and returns a pandas dataframe + :param tides_mat: + :return: + """ + logger.info("Parsing %s", profiles_mat) + mat_data = loadmat(profiles_mat)["data"] + rows = [] + for i in range(0, len(mat_data["site"])): + for j in range(0, len(mat_data["pfx"][i])): + for profile_type in ["prestorm", "poststorm"]: + + if profile_type == "prestorm": + z = mat_data["pf1"][i][j][0] + if profile_type == "poststorm": + z = mat_data["pf2"][i][j][0] + + rows.append( + { + "beach": mat_data["site"][i], + "lon": mat_data["lon"][i], + "lat": mat_data["lat"][i], + "profile_type": profile_type, + "x": mat_data["pfx"][i][j][0], + "z": z, + } + ) + + df = pd.DataFrame(rows) + return df + + +def remove_zeros(df_profiles): + """ + When parsing the pre/post storm profiles, the end of some profiles have constant values of zero. Let's change + these to NaNs for consistancy. Didn't use pandas fillnan because 0 may still be a valid value. + :param df: + :return: + """ + + df_profiles = df_profiles.sort_index() + groups = df_profiles.groupby(level=["site_id", "profile_type"]) + for key, _ in groups: + logger.debug("Removing zeros from {} profile at {}".format(key[1], key[0])) + idx_site = (df_profiles.index.get_level_values("site_id") == key[0]) & ( + df_profiles.index.get_level_values("profile_type") == key[1] + ) + df_profile = df_profiles[idx_site] + x_last_ele = df_profile[df_profile.z != 0].index.get_level_values("x")[-1] + df_profiles.loc[idx_site & (df_profiles.index.get_level_values("x") > x_last_ele), "z"] = np.nan + + return df_profiles + + +def matlab_datenum_to_datetime(matlab_datenum): + """ + Adapted from https://stackoverflow.com/a/13965852 + :param matlab_datenum: + :return: + """ + return datetime.fromordinal(int(matlab_datenum)) + timedelta(days=matlab_datenum % 1) - timedelta(days=366) + + +def get_unique_sites(dfs, cols=["beach", "lat", "lon"]): + """ + Generates a dataframe of unique sites based on beach names, lats and lons. Creates a unique site ID for each. + :param dfs: + :param cols: + :return: + """ + + rows = [] + df_all = pd.concat([df[cols] for df in dfs]) + beach_groups = df_all.groupby(["beach"]) + for beach_name, beach_group in beach_groups: + site_groups = beach_group.groupby(["lat", "lon"]) + siteNo = 1 + for site_name, site_group in site_groups: + site = "{}{:04d}".format(beach_name, siteNo) + rows.append({"site_id": site, "lat": site_name[0], "lon": site_name[1], "beach": beach_name}) + siteNo += 1 + + df = pd.DataFrame(rows) + + return df + + +def replace_unique_sites(df, df_sites, cols=["lat", "lon"]): + """ + Replaces beach/lat/lon columns with the unique site_id + :param dfs: + :param df_sites: + :return: + """ + # Make the sites index a column, so it can be merged into df + df_sites["site_id"] = df_sites.index.get_level_values("site_id") + + # Merging on a float can lead to subtle bugs. Lets convert lat/lons to integers and merge on that instead + precision = 8 + df_sites["lat_int"] = np.round(df_sites["lat"] * 10 ** precision).astype(np.int64) + df_sites["lon_int"] = np.round(df_sites["lon"] * 10 ** precision).astype(np.int64) + df["lat_int"] = np.round(df["lat"] * 10 ** precision).astype(np.int64) + df["lon_int"] = np.round(df["lon"] * 10 ** precision).astype(np.int64) + + df_merged = df.merge(df_sites, on=["lat_int", "lon_int"]) + + # Check that all our records have a unique site identifier + n_unmatched = len(df) - len(df_merged) + if n_unmatched > 0: + logger.warning("Not all records (%d of %d) matched with a unique site", n_unmatched, len(df)) + + df_merged = df_merged.drop( + columns=[ + "lat_x", + "lon_x", + "lat_int", + "lon_int", + "beach_y", + "beach_x", + "lat_y", + "lon_y", + "orientation", + "profile_x_lat_lon", + ] + ) + + return df_merged + + +@click.command(short_help="create sites.csv") +@click.option("--waves-mat", required=True, help=".mat file containing wave records") +@click.option("--tides-mat", required=True, help=".mat file containing tide records") +@click.option("--profiles-mat", required=True, help=".mat file containing beach profiles") +@click.option("--orientations-mat", required=True, help=".mat file containing orientation of beach profiles") +@click.option("--output-file", required=True, help="where to save sites.csv") +def create_sites_csv(waves_mat, tides_mat, profiles_mat, orientations_mat, output_file): + logger.info("Creating %s", output_file) + df_waves = parse_waves(waves_mat=waves_mat) + df_tides = parse_tides(tides_mat=tides_mat) + df_profiles = parse_profiles(profiles_mat=profiles_mat) + df_orientations = parse_orientations(orientations_mat=orientations_mat) + df_sites = get_unique_sites(dfs=[df_waves, df_tides, df_profiles]) + df_sites = combine_sites_and_orientaions(df_sites, df_orientations) + df_sites = specify_lat_lon_profile_center(df_sites) + df_sites.set_index(["site_id"], inplace=True) + df_sites.to_csv(output_file) + logger.info("Created %s", output_file) + + +@click.command(short_help="create waves.csv") +@click.option("--waves-mat", required=True, help=".mat file containing wave records") +@click.option("--sites-csv", required=True, help=".csv file description of cross section sites") +@click.option("--output-file", required=True, help="where to save waves.csv") +def create_waves_csv(waves_mat, sites_csv, output_file): + logger.info("Creating %s", output_file) + df_waves = parse_waves(waves_mat=waves_mat) + df_sites = pd.read_csv(sites_csv, index_col=[0]) + df_waves = replace_unique_sites(df_waves, df_sites) + df_waves.set_index(["site_id", "datetime"], inplace=True) + df_waves.sort_index(inplace=True) + df_waves.to_csv(output_file) + logger.info("Created %s", output_file) + + +@click.command(short_help="create profiles.csv") +@click.option("--profiles-mat", required=True, help=".mat file containing beach profiles") +@click.option("--sites-csv", required=True, help=".csv file description of cross section sites") +@click.option("--output-file", required=True, help="where to save profiles.csv") +def create_profiles_csv(profiles_mat, sites_csv, output_file): + logger.info("Creating %s", output_file) + df_profiles = parse_profiles(profiles_mat=profiles_mat) + df_sites = pd.read_csv(sites_csv, index_col=[0]) + df_profiles = replace_unique_sites(df_profiles, df_sites) + df_profiles.set_index(["site_id", "profile_type", "x"], inplace=True) + df_profiles.sort_index(inplace=True) + df_profiles.to_csv(output_file) + logger.info("Created %s", output_file) + + +@click.command(short_help="create profiles.csv") +@click.option("--tides-mat", required=True, help=".mat file containing tides") +@click.option("--sites-csv", required=True, help=".csv file description of cross section sites") +@click.option("--output-file", required=True, help="where to save tides.csv") +def create_tides_csv(tides_mat, sites_csv, output_file): + logger.info("Creating %s", output_file) + df_tides = parse_tides(tides_mat=tides_mat) + df_sites = pd.read_csv(sites_csv, index_col=[0]) + df_tides = replace_unique_sites(df_tides, df_sites) + df_tides.set_index(["site_id", "datetime"], inplace=True) + df_tides.sort_index(inplace=True) + df_tides.to_csv(output_file) + logger.info("Created %s", output_file) + + +@click.group() +def cli(): + pass + + +if __name__ == "__main__": + cli.add_command(create_sites_csv) + cli.add_command(create_waves_csv) + cli.add_command(create_profiles_csv) + cli.add_command(create_tides_csv) + cli() + +pd.set_option("display.precision", 8) +pd.set_option("display.max_columns", None)