From 1b521a052424886bf87a236a279b47639275ac09 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Thu, 15 Nov 2018 12:40:58 +1100 Subject: [PATCH] Add module for parsing observed storm impacts --- src/analysis/observed_storm_impacts.py | 138 +++++++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 src/analysis/observed_storm_impacts.py diff --git a/src/analysis/observed_storm_impacts.py b/src/analysis/observed_storm_impacts.py new file mode 100644 index 0000000..1b501ed --- /dev/null +++ b/src/analysis/observed_storm_impacts.py @@ -0,0 +1,138 @@ +import logging.config +import os + +import numpy as np +import pandas as pd +from scipy.integrate import simps + +logging.config.fileConfig('./src/logging.conf', disable_existing_loggers=False) +logger = logging.getLogger(__name__) + + +def return_first_or_nan(l): + """ + Returns the first value of a list if empty or returns nan. Used for getting dune/toe and crest values. + :param l: + :return: + """ + if len(l) == 0: + return np.nan + else: + return l[0] + + +def volume_change(df_profiles, df_profile_features, zone): + logger.info('Calculating change in beach volume in {} zone'.format(zone)) + + df_vol_changes = pd.DataFrame(index=df_profile_features.index) + df_profiles = df_profiles.sort_index() + sites = df_profiles.groupby(level=['site_id']) + + for site_id, df_site in sites: + logger.debug('Calculating change in beach volume at {} in {} zone'.format(site_id, zone)) + prestorm_dune_toe_x = df_profile_features.loc[df_profile_features.index == site_id].dune_toe_x.tolist() + prestorm_dune_crest_x = df_profile_features.loc[df_profile_features.index == site_id].dune_crest_x.tolist() + + # We may not have a dune toe or crest defined, or there may be multiple defined. + prestorm_dune_crest_x = return_first_or_nan(prestorm_dune_crest_x) + prestorm_dune_toe_x = return_first_or_nan(prestorm_dune_toe_x) + + # If no dune to has been defined, Dlow = Dhigh. Refer to Sallenger (2000). + if np.isnan(prestorm_dune_toe_x): + prestorm_dune_toe_x = prestorm_dune_crest_x + + # Find last x coordinate where we have both prestorm and poststorm measurements. If we don't do this, + # the prestorm and poststorm values are going to be calculated over different lengths. + df_zone = df_site.dropna(subset=['z']) + x_last_obs = min([max(df_zone.query("profile_type == '{}'".format(profile_type)).index.get_level_values('x')) + for profile_type in ['prestorm', 'poststorm']]) + + # Where we want to measure pre and post storm volume is dependant on the zone selected + if zone == 'swash': + x_min = prestorm_dune_toe_x + x_max = x_last_obs + elif zone == 'dune_face': + x_min = prestorm_dune_crest_x + x_max = prestorm_dune_toe_x + else: + logger.warning('Zone argument not properly specified. Please check') + x_min = None + x_max = None + + # Now, compute the volume of sand between the x-coordinates prestorm_dune_toe_x and x_swash_last for both prestorm + # and post storm profiles. + prestorm_vol = beach_volume(x=df_zone.query("profile_type=='prestorm'").index.get_level_values('x'), + z=df_zone.query("profile_type=='prestorm'").z, + x_min=x_min, + x_max=x_max) + poststorm_vol = beach_volume(x=df_zone.query("profile_type=='poststorm'").index.get_level_values('x'), + z=df_zone.query("profile_type=='poststorm'").z, + x_min=x_min, + x_max=x_max) + + df_vol_changes.loc[site_id, 'prestorm_{}_vol'.format(zone)] = prestorm_vol + df_vol_changes.loc[site_id, 'poststorm_{}_vol'.format(zone)] = poststorm_vol + df_vol_changes.loc[site_id, '{}_vol_change'.format(zone)] = prestorm_vol - poststorm_vol + + return df_vol_changes + + +def beach_volume(x, z, x_min=np.NINF, x_max=np.inf): + """ + Returns the beach volume of a profile, calculated with Simpsons rule + :param x: x-coordinates of beach profile + :param z: z-coordinates of beach profile + :param x_min: Minimum x-coordinate to consider when calculating volume + :param x_max: Maximum x-coordinate to consider when calculating volume + :return: + """ + profile_mask = [True if x_min < x_coord < x_max else False for x_coord in x] + x_masked = np.array(x)[profile_mask] + z_masked = np.array(z)[profile_mask] + + if len(x_masked) == 0 or len(z_masked) == 0: + return np.nan + else: + return simps(z_masked, x_masked) + + +def impacts_from_profiles(df_profiles, df_profile_features): + # Impacts should be per site, so use the profile_features as the base index. + df_observed_impacts = pd.DataFrame(index=df_profile_features.index) + + # Swash zone volume change + prestorm_swash_vol, poststorm_swash_vol = volume_change(df_profiles, df_profile_features, zone='swash') + + # Dune volume change + + # If no dune volume change, then swash zone + + # + pass + + +def storm_regime(df_observed_impacts): + logger.info('Getting observed storm regimes') + df_observed_impacts.loc[df_observed_impacts.swash_vol_change < 3,'storm_regime'] = 'swash' + df_observed_impacts.loc[df_observed_impacts.dune_face_vol_change > 3, 'storm_regime'] = 'collision' + return df_observed_impacts + +if __name__ == '__main__': + logger.info('Importing existing data') + data_folder = './data/interim' + df_profiles = pd.read_csv(os.path.join(data_folder, 'profiles.csv'), index_col=[0, 1, 2]) + df_profile_features = pd.read_csv(os.path.join(data_folder, 'profile_features.csv'), index_col=[0]) + + logger.info('Creating new dataframe for observed impacts') + df_observed_impacts = pd.DataFrame(index=df_profile_features.index) + + logger.info('Getting pre/post storm volumes') + df_swash_vol_changes = volume_change(df_profiles, df_profile_features, zone='swash') + df_dune_face_vol_changes = volume_change(df_profiles, df_profile_features, zone='dune_face') + df_observed_impacts = df_observed_impacts.join([df_swash_vol_changes, df_dune_face_vol_changes ]) + + # TODO Classify regime based on volume changes + df_observed_impacts = storm_regime(df_observed_impacts) + + # TODO Save dataframe to csv + df_observed_impacts.to_csv(os.path.join(data_folder, 'impacts_observed.csv'))