From 965dcf2ec07e071380f824dff6d900fbc46fd463 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Mon, 29 Apr 2019 14:06:25 +1000 Subject: [PATCH] Optimize calculation of change in volume We now calculate the where the pre-storm and post-storm profiles change a bit more robustly. --- src/analysis/observed_storm_impacts.py | 194 ++++++++++++++++++++----- 1 file changed, 157 insertions(+), 37 deletions(-) diff --git a/src/analysis/observed_storm_impacts.py b/src/analysis/observed_storm_impacts.py index 320610d..66f681f 100644 --- a/src/analysis/observed_storm_impacts.py +++ b/src/analysis/observed_storm_impacts.py @@ -5,6 +5,7 @@ from scipy.integrate import simps from scipy.signal import savgol_filter from scipy.interpolate import interp1d from numpy import ma as ma +from itertools import groupby from tqdm import tqdm from logs import setup_logging @@ -56,59 +57,154 @@ def volume_change(df_profiles, df_profile_features): params = {} + # Calculate pre and post storm volume seawards of our profile change point + # and store in dictionary and dataframe. + df_prestorm = df_profiles.loc[(site_id, "prestorm")] + df_poststorm = df_profiles.loc[(site_id, "poststorm")] + + # Calculate total subaerial volume change + # Calculate difference between pre and post storm profiles + z_diff = ( + df_profiles.loc[(site_id, "poststorm")].z + - df_profiles.loc[(site_id, "prestorm")].z + ) + + # There are no common points between pre and poststorm values, so we have to + # skip this + if z_diff.dropna().size == 0: + continue + + # # # Debug + # import matplotlib.pyplot as plt + # plt.plot(z_diff) + # plt.plot(df_profiles.loc[(site_id, "prestorm")].z) + # plt.plot(df_profiles.loc[(site_id, "poststorm")].z) + # plt.show() + + # First, find locations where we have a good match between pre and post storm + # profiles. + lidar_accuracy = 0.2 # m + good_match = start_stop( + (abs(z_diff) < lidar_accuracy).values, trigger_val=True, len_thresh=20 + ) + + # If entire profile has changed (usually at lagoon entrance), take change + # point as the first x-coord where we have both pre and poststorm profiles + if good_match.size == 0: + x_change_point = min( + set(df_prestorm.z.dropna().index.get_level_values("x")) + & set(df_poststorm.z.dropna().index.get_level_values("x")) + ) + z_change_point = df_prestorm.loc[x_change_point].z + + else: + + # Minimum idx_change_points should be the first place where we have a good match + idx_change_point_min = good_match[0][0] + + # Identify locations where z_diff is negative, i.e. profile has been + # eroded by the storm. Then group them by the number of consecutive + # values. + grouped = start_stop((z_diff < 0).values, trigger_val=True, len_thresh=1) + + # Sort by streaklength, then get the start index of the longest streak + # of true values. x_change_point is then the x-coordinate where our pre and + # post storm profiles start to change. + idx_change_points = sorted( + [x for x in grouped if x[0] > idx_change_point_min], + key=lambda x: x[1] - x[0], + reverse=True, + ) + + if len(idx_change_points) == 0: + continue + else: + idx_change_point = idx_change_points[0][0] + x_change_point = z_diff.index[idx_change_point] + z_change_point = df_prestorm.loc[x_change_point].z + + # Landward of the change point, set difference in pre/post storm profiles + # equal to zero + z_diff[z_diff.index < x_change_point] = 0 + + params["prestorm_total_subaerial_vol"] = beach_volume( + x=df_prestorm.index.get_level_values("x"), + z=df_prestorm.z.values, + x_min=x_change_point, + ) + params["poststorm_total_subaerial_vol"] = beach_volume( + x=df_poststorm.index.get_level_values("x"), + z=df_poststorm.z.values, + x_min=x_change_point, + ) + params["total_vol_change"] = ( + params["poststorm_total_subaerial_vol"] + - params["prestorm_total_subaerial_vol"] + ) + params["x_change_point"] = x_change_point + params["z_change_point"] = z_change_point + + df_vol_changes.loc[site_id, "total_vol_change"] = params["total_vol_change"] + df_vol_changes.loc[site_id, "x_change_point"] = params["x_change_point"] + df_vol_changes.loc[site_id, "z_change_point"] = params["z_change_point"] + for zone in ["dune", "swash"]: params[zone] = {} for profile_type in ["prestorm", "poststorm"]: - params[zone][profile_type] = {} + + # Store zone/profile_type results in a dictionary, then append at the + # end to the params dictionary. + d = {} + + # Get variables, this helps simplify the below code + df_profile_feature = df_profile_features.loc[(site_id, profile_type)] + df_profile = df_profiles.loc[(site_id, profile_type)] # Define the edges of the swash and dunes where we want to calculate subaeraial volume. if zone == "swash": - params[zone][profile_type]["x_min"] = df_profile_features.loc[ - (site_id, profile_type) - ].dune_toe_x - params[zone][profile_type]["x_max"] = max( - df_profiles.loc[(site_id, profile_type)].index.get_level_values( - "x" - ) - ) + d["x_min"] = df_profile_feature.dune_toe_x + d["x_max"] = max(df_profile.index.get_level_values("x")) # For profiles with no Dlow value, we take Dhigh as the minimum value to calculate swash - if np.isnan(params[zone][profile_type]["x_min"]): - params[zone][profile_type]["x_min"] = df_profile_features.loc[ - (site_id, profile_type) - ].dune_crest_x + if np.isnan(d["x_min"]): + d["x_min"] = df_profile_feature.dune_crest_x elif zone == "dune": - params[zone][profile_type]["x_min"] = df_profile_features.loc[ - (site_id, profile_type) - ].dune_crest_x - params[zone][profile_type]["x_max"] = df_profile_features.loc[ - (site_id, profile_type) - ].dune_toe_x + d["x_min"] = df_profile_feature.dune_crest_x + d["x_max"] = df_profile_feature.dune_toe_x # For profiles with no Dlow value, the dune is undefined and we cannot calculate a dune volume. # Calculate subaerial volume based on our x min and maxes - params[zone][profile_type]["subaerial_vol"] = beach_volume( - x=df_profiles.loc[(site_id, profile_type)].index.get_level_values( - "x" - ), - z=df_profiles.loc[(site_id, profile_type)].z.values, - x_min=params[zone][profile_type]["x_min"], - x_max=params[zone][profile_type]["x_max"], + d["subaerial_vol"] = beach_volume( + x=df_profile.index.get_level_values("x"), + z=df_profile.z.values, + x_min=d["x_min"], + x_max=d["x_max"], ) - # TODO Landward limit still needs to be incorporated + params[zone][profile_type] = d - params[zone]["vol_change"] = ( - params[zone]["poststorm"]["subaerial_vol"] - - params[zone]["prestorm"]["subaerial_vol"] + # Calculate change in volumes. Use the z_diff array which has been + # zero'ed out landward of the x_change_point + params[zone]["vol_change"] = beach_volume( + x=z_diff.index.values, + z=z_diff.values, + x_min=params[zone]["prestorm"]["x_min"], + x_max=params[zone]["prestorm"]["x_max"], ) + params[zone]["pct_change"] = ( params[zone]["vol_change"] / params[zone]["prestorm"]["subaerial_vol"] ) + # params[zone]["vol_loss"] = (params[zone]["prestorm"]["subaerial_vol"] - + # params[zone]["poststorm"]["subaerial_vol"]) + # params[zone]["pct_loss"] = \ + # params[zone]["vol_loss"] / params[zone]["prestorm"]["subaerial_vol"] + + # Save results in our data frame df_vol_changes.loc[site_id, "prestorm_{}_vol".format(zone)] = params[zone][ "prestorm" ]["subaerial_vol"] @@ -121,7 +217,6 @@ def volume_change(df_profiles, df_profile_features): df_vol_changes.loc[site_id, "{}_pct_change".format(zone)] = params[zone][ "pct_change" ] - return df_vol_changes @@ -153,12 +248,8 @@ def storm_regime(df_observed_impacts): """ logger.info("Getting observed storm regimes") - swash = (df_observed_impacts.dune_pct_change <= 2) & ( - df_observed_impacts.dune_vol_change <= 3 - ) - collision = (df_observed_impacts.dune_pct_change >= 2) | ( - df_observed_impacts.dune_vol_change > 3 - ) + swash = df_observed_impacts.dune_vol_change > -3 + collision = df_observed_impacts.dune_vol_change < -3 df_observed_impacts.loc[swash, "storm_regime"] = "swash" df_observed_impacts.loc[collision, "storm_regime"] = "collision" @@ -344,3 +435,32 @@ def get_beach_width(df_profile_features, df_profiles, profile_type, ele, col_nam ).dune_toe_x df_width = (df_x_position - df_x_prestorm_dune_toe).rename(col_name) return df_width + + +def start_stop(a, trigger_val, len_thresh=2): + """ + https://stackoverflow.com/a/51259253 + + In [47]: myArray + Out[47]: array([1, 1, 0, 2, 0, 1, 1, 1, 1, 0, 0, 1, 2, 1, 1, 1]) + + In [48]: start_stop(myArray, trigger_val=1, len_thresh=2) + Out[48]: + array([[ 5, 8], + [13, 15]]) + + :param a: + :param trigger_val: + :param len_thresh: + :return: + """ + # "Enclose" mask with sentients to catch shifts later on + mask = np.r_[False, np.equal(a, trigger_val), False] + + # Get the shifting indices + idx = np.flatnonzero(mask[1:] != mask[:-1]) + + # Get lengths + lens = idx[1::2] - idx[::2] + + return idx.reshape(-1, 2)[lens > len_thresh] - [0, 1]