Refactor overwriting dune crest/toes and impacts
Uses one, central .csv file contained in ./data/raw/profile_features_chris_leamandevelop
Chris Leaman
6 years ago
parent
e1d95a1752
commit
3af90601ef
@ -1,103 +0,0 @@
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"""
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After generating interim data files based on raw data, we may need to overwrite some rows with manual data.
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"""
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import pandas as pd
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import numpy as np
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import click
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from logs import setup_logging
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logger = setup_logging()
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def overwrite_profile_features(df_interim, df_overwrite, df_profiles, overwrite=True):
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"""
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Overwrite the interim profile features file with an excel file.
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:param interim_file: Should be './data/interim/profile_features.csv'
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:param overwrite_file: Should be './data/raw/profile_features_chris_leaman/profile_features_chris_leaman.csv'
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:param overwrite: Whether or not to overwrite the original interim_file. If false, file will not be written
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:return:
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"""
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# Merge
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df_merged = df_interim.merge(df_overwrite, left_index=True, right_index=True, suffixes=["", "_overwrite"])
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# Remove x vals if overwrite file as remove
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df_merged.loc[df_merged.dune_crest_x_overwrite == "remove", "dune_crest_x"] = np.nan
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df_merged.loc[df_merged.dune_toe_x_overwrite == "remove", "dune_toe_x"] = np.nan
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# Put in new x vals. Note that a NaN value in the overwrite column, means keep the original value.
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idx = (df_merged.dune_crest_x_overwrite.notnull()) & (df_merged.dune_crest_x_overwrite != "remove")
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df_merged.loc[idx, "dune_crest_x"] = df_merged.loc[idx, "dune_crest_x_overwrite"]
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idx = (df_merged.dune_toe_x_overwrite.notnull()) & (df_merged.dune_toe_x_overwrite != "remove")
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df_merged.loc[idx, "dune_toe_x"] = df_merged.loc[idx, "dune_toe_x_overwrite"]
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# Recalculate z values from x coordinates
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for site_id in df_merged.index.get_level_values("site_id").unique():
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logger.info("Overwriting dune crest/toes with manual values: {}".format(site_id))
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# Get profiles
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df_profile = df_profiles.query('site_id=="{}"'.format(site_id))
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for param in ["prestorm", "poststorm"]:
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for loc in ["crest", "toe"]:
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# Get x value to find corresponding z value
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x_val = df_merged.loc[(site_id, param), "dune_{}_x".format(loc)]
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if np.isnan(x_val):
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df_merged.loc[(site_id, param), "dune_{}_z".format(loc)] = np.nan
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continue
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# Get the corresponding z value for our x value
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query = 'site_id=="{}" & profile_type=="{}" & x=="{}"'.format(site_id, param, x_val)
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# Try get the value from the other profile if we return nan or empty dataframe
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if df_profile.query(query).empty:
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if param == "prestorm":
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query = 'site_id=="{}" & profile_type=="{}" & x=="{}"'.format(site_id, "poststorm", x_val)
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elif param == "poststorm":
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query = 'site_id=="{}" & profile_type=="{}" & x=="{}"'.format(site_id, "prestorm", x_val)
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z_val = df_profile.query(query).iloc[0].z
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else:
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z_val = df_profile.query(query).iloc[0].z
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# Put results back into merged dataframe
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df_merged.loc[(site_id, param), "dune_{}_z".format(loc)] = z_val
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# Drop columns
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df_merged = df_merged.drop(columns=["dune_crest_x_overwrite", "dune_toe_x_overwrite", "comment"], errors="ignore")
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# Merge back into interim data frame. Use concat/duplicates since .update will not update nan values
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df_final = pd.concat([df_merged, df_interim])
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df_final = df_final[~df_final.index.duplicated(keep="first")]
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df_final = df_final.sort_index()
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# Write to file
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return df_final
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@click.command(short_help="overwrite profile_features with manual excel sheet")
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@click.option("--interim_file", required=True, help="path of profile_features.csv")
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@click.option("--overwrite_file", required=True, help="path of excel file with overwrite data")
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@click.option("--profile_file", required=True, help="path of profiles.csv")
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@click.option("--overwrite/--no-overwrite", default=True)
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def apply_profile_features_overwrite(interim_file, overwrite_file, profile_file, overwrite):
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logger.info("Overwriting profile features with manual excel file")
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# Load files
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df_interim = pd.read_csv(interim_file, index_col=[0, 1])
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df_overwrite = pd.read_excel(overwrite_file)
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df_profiles = pd.read_csv(profile_file, index_col=[0, 1, 2])
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if "site_id" in df_overwrite.columns and "profile_type" in df_overwrite.columns:
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df_overwrite = df_overwrite.set_index(["site_id", "profile_type"])
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# Replace interim values with overwrite values
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df_interim = overwrite_profile_features(df_interim, df_overwrite, df_profiles, overwrite)
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# Write to csv
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df_interim.to_csv(interim_file, float_format="%.3f")
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logger.info("Done!")
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