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nsw-2016-storm-impact
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Fix bugs for forecasting impacts

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develop
Chris Leaman 6 years ago
parent 44310e3be4
commit 5b9b4141b3
2 changed files with 39 additions and 39 deletions
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6
src/analysis/forecasted_storm_impacts.py
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@ -20,7 +20,7 @@ def forecasted_impacts(df_profile_features, df_forecasted_twl):
"""
logger.info("Getting forecasted storm impacts")
df_forecasted_impacts = pd.DataFrame(index=df_profile_features.index)
df_forecasted_impacts = pd.DataFrame(index=df_profile_features.index.get_level_values('site_id').unique())
# For each site, find the maximum R_high value and the corresponding R_low value.
idx = df_forecasted_twl.groupby(level=["site_id"])["R_high"].idxmax().dropna()
@ -29,7 +29,7 @@ def forecasted_impacts(df_profile_features, df_forecasted_twl):
# Join with df_profile features to find dune toe and crest elevations
df_forecasted_impacts = df_forecasted_impacts.merge(
df_profile_features[["dune_toe_z", "dune_crest_z"]], how="left", left_index=True, right_index=True
df_profile_features.query("profile_type=='prestorm'")[["dune_toe_z", "dune_crest_z"]], how="left", left_index=True, right_index=True
)
# Compare R_high and R_low wirth dune crest and toe elevations
@ -73,7 +73,7 @@ def storm_regime(df_forecasted_impacts):
def create_forecasted_impacts(profile_features_csv, forecasted_twl_csv, output_file):
logger.info("Creating observed wave impacts")
logger.info("Importing existing data")
df_profile_features = pd.read_csv(profile_features_csv, index_col=[0])
df_profile_features = pd.read_csv(profile_features_csv, index_col=[0,1])
df_forecasted_twl = pd.read_csv(forecasted_twl_csv, index_col=[0, 1])
df_forecasted_impacts = forecasted_impacts(df_profile_features, df_forecasted_twl)

72
src/analysis/observed_storm_impacts.py
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@ -30,14 +30,15 @@ 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_vol_changes = pd.DataFrame(index=df_profile_features.index.get_level_values('site_id').unique())
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()
query ="site_id=='{}'&profile_type=='prestorm'".format(site_id)
prestorm_dune_toe_x = df_profile_features.query(query).dune_toe_x.tolist()
prestorm_dune_crest_x = df_profile_features.query(query).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)
@ -61,14 +62,20 @@ def volume_change(df_profiles, df_profile_features, zone):
for profile_type in ["prestorm", "poststorm"]
]
)
x_first_obs = max(
[
min(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_min = max(prestorm_dune_toe_x,x_first_obs)
x_max = x_last_obs
elif zone == "dune_face":
x_min = prestorm_dune_crest_x
x_max = prestorm_dune_toe_x
x_min = max(prestorm_dune_crest_x, x_first_obs)
x_max = min(prestorm_dune_toe_x,x_last_obs)
else:
logger.warning("Zone argument not properly specified. Please check")
x_min = None
@ -89,13 +96,23 @@ def volume_change(df_profiles, df_profile_features, zone):
x_max=x_max,
)
# No point keeping so many decimal places, let's round them
prestorm_vol = np.round(prestorm_vol, 2)
poststorm_vol = np.round(poststorm_vol, 2)
# Volume change needs to be calculated including a tolerance for LIDAR accuracy. If difference between
# profiles is less than 20 cm, consider them as zero difference.
prestorm_z = df_zone.query("profile_type=='prestorm'").reset_index('profile_type').z
poststorm_z = df_zone.query("profile_type=='poststorm'").reset_index('profile_type').z
diff_z = prestorm_z - poststorm_z
diff_z[abs(diff_z) < 0.2] = 0
diff_vol = beach_volume(
x=diff_z.index.get_level_values("x"),
z=diff_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
df_vol_changes.loc[site_id, "{}_vol_change".format(zone)] = diff_vol
df_vol_changes.loc[site_id, "{}_pct_change".format(zone)] = diff_vol / prestorm_vol * 100
return df_vol_changes
@ -127,31 +144,14 @@ def storm_regime(df_observed_impacts):
:return:
"""
logger.info("Getting observed storm regimes")
df_observed_impacts.loc[df_observed_impacts.dune_face_vol_change <= 5, "storm_regime"] = "swash"
df_observed_impacts.loc[df_observed_impacts.dune_face_vol_change > 5, "storm_regime"] = "collision"
return df_observed_impacts
swash = (df_observed_impacts.dune_face_pct_change <= 2) & (df_observed_impacts.dune_face_vol_change <= 3)
collision = (df_observed_impacts.dune_face_pct_change >= 2) |(df_observed_impacts.dune_face_vol_change > 3)
df_observed_impacts.loc[swash, "storm_regime"] = "swash"
df_observed_impacts.loc[collision, "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])
#
# # Classify regime based on volume changes
# df_observed_impacts = storm_regime(df_observed_impacts)
#
# # Save dataframe to csv
# df_observed_impacts.to_csv(os.path.join(data_folder, "impacts_observed.csv"))
@click.command()
@ -163,10 +163,10 @@ def create_observed_impacts(profiles_csv, profile_features_csv, output_file):
logger.info("Creating observed wave impacts")
logger.info("Importing data")
df_profiles = pd.read_csv(profiles_csv, index_col=[0, 1, 2])
df_profile_features = pd.read_csv(profile_features_csv, index_col=[0])
df_profile_features = pd.read_csv(profile_features_csv, index_col=[0,1])
logger.info("Creating new dataframe for observed impacts")
df_observed_impacts = pd.DataFrame(index=df_profile_features.index)
df_observed_impacts = pd.DataFrame(index=df_profile_features.index.get_level_values('site_id').unique())
logger.info("Getting pre/post storm volumes")
df_swash_vol_changes = volume_change(df_profiles, df_profile_features, zone="swash")
@ -177,7 +177,7 @@ def create_observed_impacts(profiles_csv, profile_features_csv, output_file):
df_observed_impacts = storm_regime(df_observed_impacts)
# Save dataframe to csv
df_observed_impacts.to_csv(output_file)
df_observed_impacts.to_csv(output_file, float_format='%.4f')
logger.info("Saved to %s", output_file)
logger.info("Done!")

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