From 947d2093bd812d7085370ab1b61cad6e270ff212 Mon Sep 17 00:00:00 2001 From: Chris Leaman Date: Fri, 14 Dec 2018 12:02:16 +1100 Subject: [PATCH] Take post-storm profile based on isgood parameter in .mat file --- src/data/parse_mat.py | 110 ++++++++++++++++++++++++++---------------- 1 file changed, 69 insertions(+), 41 deletions(-) diff --git a/src/data/parse_mat.py b/src/data/parse_mat.py index b2e73c6..4020479 100644 --- a/src/data/parse_mat.py +++ b/src/data/parse_mat.py @@ -198,6 +198,17 @@ def parse_profiles_and_sites(profiles_mat): site_rows = [] site_counter = 0 + # Our z values can come from these columns, depending on the isgood flag. + # Let's reoganise them into a list of list + z_names = ["Zpre", 'Zpost', 'Zrec1', 'Zrec2', 'Zrec3', 'Zrec4'] + z_cols = [mat_data[col] for col in z_names] + z_sites = [] + for cols in zip(*z_cols): + z_vals = [] + for z_vector in zip(*cols): + z_vals.append([z[0] for z in z_vector]) + z_sites.append(z_vals) + for i, site in enumerate(mat_data["site"]): logger.debug("Processing site {} of {}".format(i + 1, len(mat_data["site"]))) @@ -215,54 +226,70 @@ def parse_profiles_and_sites(profiles_mat): # Want to calculation the orientation orientation = {} - for x, lat, lon, z_prestorm, z_poststorm, easting, northing in zip( + + + for x, lat, lon, z_site, easting, northing in zip( mat_data["x"][i], mat_data["lats"][i], mat_data["lons"][i], - mat_data["Zpre"][i], - mat_data["Zpost"][i], + z_sites[i], mat_data["eastings"][i], mat_data["northings"][i], ): - # Only extract pre and post storm profile - for j, profile_type in enumerate(["prestorm", "poststorm"]): - if mat_data["isgood"][i][j] == 1: + profile_type = None + for j, is_good in enumerate([1] + mat_data["isgood"][i]): + + # Assumes the first profile is always good and is the prestorm profike + if j == 0: + profile_type = 'prestorm' + z = z_site[j] + land_lim = np.nan + + # Skips bad profiles + elif is_good == 0: + continue + + # Takes the first isgood profile as the post storm profile + else: + profile_type = 'poststorm' + z = z_site[j] land_lim = mat_data["landlims"][i][j] - survey_datetime = matlab_datenum_to_datetime(mat_data["surveydates"][i][j]) - - if profile_type == "prestorm": - z = z_prestorm - else: - z = z_poststorm - - # Keep a record of the where the center of the profile is located, and the locations of the land - # and sea - - # TODO: This code isn't very transferrable. What if we don't have lat/lons at 200 m? Relook at this - if x[0] == 200: - x_200_lat = lat[0] - x_200_lon = lon[0] - elif x[0] == 0: - orientation["land_easting"] = easting[0] - orientation["land_northing"] = northing[0] - elif x[0] == 400: - orientation["sea_easting"] = easting[0] - orientation["sea_northing"] = northing[0] - - profile_rows.append( - { - "site_id": site_id, - "lon": lon[0], - "lat": lat[0], - "profile_type": profile_type, - "x": x[0], - "z": z[0], - "land_lim": land_lim, - "survey_datetime": survey_datetime, - } - ) + + survey_datetime = matlab_datenum_to_datetime(mat_data["surveydates"][i][j]) + + # Keep a record of the where the center of the profile is located, and the locations of the land + # and sea + + # TODO: This code isn't very transferrable. What if we don't have lat/lons at 200 m? Relook at this + if x[0] == 200: + x_200_lat = lat[0] + x_200_lon = lon[0] + elif x[0] == 0: + orientation["land_easting"] = easting[0] + orientation["land_northing"] = northing[0] + elif x[0] == 400: + orientation["sea_easting"] = easting[0] + orientation["sea_northing"] = northing[0] + + profile_rows.append( + { + "site_id": site_id, + "lon": lon[0], + "lat": lat[0], + "profile_type": profile_type, + "x": x[0], + "z": z, + "land_lim": land_lim, + "survey_datetime": survey_datetime, + } + ) + + # Stop looking at profiles if we've got our post-storm profile + if profile_type == 'poststorm': + break + orientation = math.degrees( math.atan2( @@ -306,8 +333,9 @@ def remove_zeros(df_profiles): 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 + x_last_ele = df_profile[df_profile.z == 0].index.get_level_values("x")[0] + df_profiles.loc[idx_site & (df_profiles.index.get_level_values("x") > x_last_ele), + "z"] = np.nan logger.info("Removed zeros from end of profiles") return df_profiles