Rename csv_to_shp to csv_to_geojson
Chris Leaman
6 years ago
parent
3f2cfa50aa
commit
e50fadb35c
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"""
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Converts .csv files to .shape files
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"""
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import os
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import numpy.ma as ma
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import click
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import fiona
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import numpy as np
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import pandas as pd
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from fiona.crs import from_epsg
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from shapely.geometry import Point, mapping, LineString
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from collections import OrderedDict
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from utils import crossings, convert_coord_systems
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from logs import setup_logging
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logger = setup_logging()
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def lat_lon_from_profile_x_coord(center_lat_lon, orientation, center_profile_x, x_coord):
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"""
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Returns the lat/lon of a point on a profile with the given x_coord
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:param center_lat_lon: Shapely point of lat/lon of profile center
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:param orientation: Orientation of the profile (positive east, counterclockwise)
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:param center_profile_x: x value of the center of the profile
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:param x_coord: X coordinate of the point to get a lat lon from
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:return:
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"""
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center_xy = convert_coord_systems(center_lat_lon)
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center_x, center_y = center_xy.xy
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point_x = center_x + (center_profile_x - x_coord) * np.cos(np.deg2rad(orientation))
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point_y = center_y + (center_profile_x - x_coord) * np.sin(np.deg2rad(orientation))
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point_xy = Point(point_x, point_y)
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point_lat_lon = convert_coord_systems(point_xy, in_coord_system="EPSG:28356", out_coord_system="EPSG:4326")
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return point_lat_lon
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@click.command()
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@click.option("--sites-csv", required=True, help=".csv file to convert")
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@click.option("--profile-csv", required=True, help=".csv file to convert")
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@click.option("--impacts-csv", required=True, help=".csv file to convert")
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@click.option("--output-geojson", required=True, help="where to store .geojson file")
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def R_high_to_geojson(sites_csv, profiles_csv, impacts_csv, output_geojson):
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"""
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Converts impact R_high into a lat/lon geojson that we can plot in QGIS
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:param sites_csv:
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:param profiles_csv:
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:param impacts_csv:
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:param output_geojson:
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:return:
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"""
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sites_csv = "./data/interim/sites.csv"
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profiles_csv = "./data/interim/profiles.csv"
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impacts_csv = "./data/interim/impacts_forecasted_mean_slope_sto06.csv"
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output_geojson = "./data/interim/R_high_forecasted_mean_slope_sto06.geojson"
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df_sites = pd.read_csv(sites_csv, index_col=[0])
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df_profiles = pd.read_csv(profiles_csv, index_col=[0, 1, 2])
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df_impacts = pd.read_csv(impacts_csv, index_col=[0])
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# Create geojson file
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schema = {
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"geometry": "Point",
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"properties": OrderedDict([("beach", "str"), ("site_id", "str"), ("elevation", "float")]),
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}
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with fiona.open(output_geojson, "w", driver="GeoJSON", crs=from_epsg(4326), schema=schema) as output:
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for index, row in df_impacts.iterrows():
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site_id = index
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beach = index[:-4]
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# Find lat/lon of R_high position
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R_high_z = row["R_high"]
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# Get poststorm profile (or should this be prestorm?)
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df_profile = df_profiles.query('site_id=="{}" & profile_type=="prestorm"'.format(index))
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int_x = crossings(df_profile.index.get_level_values("x").tolist(), df_profile.z.tolist(), R_high_z)
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# Take most landward interesection. Continue to next site if there is no intersection
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try:
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int_x = max(int_x)
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except:
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continue
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# Get lat/lon on intercept position
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site = df_sites.loc[site_id]
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point_lat_lon = lat_lon_from_profile_x_coord(
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center_lat_lon=Point(site["lon"], site["lat"]),
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orientation=site["orientation"],
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center_profile_x=site["profile_x_lat_lon"],
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x_coord=int_x,
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)
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prop = OrderedDict([("beach", beach), ("site_id", site_id), ("elevation", R_high_z)])
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output.write({"geometry": mapping(point_lat_lon), "properties": prop})
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@click.command()
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@click.option("--sites-csv", required=True, help=".csv file to convert")
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@click.option("--profile-features-csv", required=True, help=".csv file to convert")
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@click.option("--output-geojson", required=True, help="where to store .geojson file")
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def profile_features_to_geojson(sites_csv, profile_features_csv, output_geojson):
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"""
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Converts profile_features containing dune toes and crest locations to a geojson we can load into QGIS
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:param sites_csv:
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:param profiles_csv:
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:param profile_features_csv:
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:param output_geojson:
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:return:
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"""
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logger.info("Creating profile features geojson")
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# Read files from interim folder
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df_sites = pd.read_csv(sites_csv, index_col=[0])
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df_profile_features = pd.read_csv(profile_features_csv, index_col=[0])
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# Create geojson file
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schema = {
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"geometry": "Point",
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"properties": OrderedDict(
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[
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("beach", "str"),
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("site_id", "str"),
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(
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"point_type",
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"str",
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), # prestorm_dune_toe, prestorm_dune_crest, poststorm_dune_toe, poststorm_dune_crest
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("profile_type", "str"),
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("elevation", "float"),
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]
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),
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}
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with fiona.open(output_geojson, "w", driver="GeoJSON", crs=from_epsg(4326), schema=schema) as output:
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for index, row in df_profile_features.iterrows():
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beach = index[:-4]
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site_id = index
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profile_type = row["profile_type"]
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for point_type in ["crest", "toe"]:
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# point_type='crest'
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elevation = row["dune_{}_z".format(point_type)]
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x = row["dune_{}_x".format(point_type)]
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if np.isnan(x):
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continue
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# Geojsons need to use 'null' instead of 'nan'
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if np.isnan(elevation):
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elevation = None
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# Convert x position to lat/lon
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site = df_sites.loc[site_id]
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point_lat_lon = lat_lon_from_profile_x_coord(
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center_lat_lon=Point(site["lon"], site["lat"]),
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orientation=site["orientation"],
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center_profile_x=site["profile_x_lat_lon"],
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x_coord=x,
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)
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prop = OrderedDict(
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[
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("beach", beach),
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("site_id", site_id),
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("point_type", point_type),
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("profile_type", profile_type),
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("elevation", elevation),
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]
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)
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output.write({"geometry": mapping(point_lat_lon), "properties": prop})
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@click.command()
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@click.option("--input-csv", required=True, help=".csv file to convert")
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@click.option("--output-geojson", required=True, help="where to store .geojson file")
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def sites_csv_to_geojson(input_csv, output_geojson):
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"""
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Converts our dataframe of sites to .geojson to load in QGis. Sites are loaded as linestrings of the profile
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cross-sections
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:param input_csv:
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:param output_geojson:
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:return:
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"""
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logger.info("Converting %s to %s", input_csv, output_geojson)
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df_sites = pd.read_csv(input_csv, index_col=[0])
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logger.info(os.environ.get("GDAL_DATA", None))
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schema = {"geometry": "LineString", "properties": OrderedDict([("beach", "str"), ("site_id", "str")])}
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with fiona.open(output_geojson, "w", driver="GeoJSON", crs=from_epsg(4326), schema=schema) as output:
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for index, row in df_sites.iterrows():
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center_lat_lon = Point(row["lon"], row["lat"])
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# Work out where landward profile limit is
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land_lat_lon = lat_lon_from_profile_x_coord(
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center_lat_lon=center_lat_lon,
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orientation=row["orientation"],
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center_profile_x=row["profile_x_lat_lon"],
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x_coord=0,
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)
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# Work out where seaward profile limit is
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sea_lat_lon = lat_lon_from_profile_x_coord(
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center_lat_lon=center_lat_lon,
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orientation=row["orientation"],
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center_profile_x=row["profile_x_lat_lon"],
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x_coord=2 * row["profile_x_lat_lon"],
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)
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line_string = LineString([land_lat_lon, center_lat_lon, sea_lat_lon])
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prop = OrderedDict([("beach", row["beach"]), ("site_id", index)])
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output.write({"geometry": mapping(line_string), "properties": prop})
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logger.info("Done!")
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@click.command()
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@click.option("--sites-csv", required=True, help="sites.csv file to convert")
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@click.option("--observed-impacts-csv", required=True, help="impacts-observed.csv file to convert")
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@click.option("--forecast-impacts-csv", required=True, help="impacts-forecast.csv file to convert")
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@click.option("--output-geojson", required=True, help="where to store .geojson file")
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def impacts_to_geojson(sites_csv, observed_impacts_csv, forecast_impacts_csv, output_geojson):
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"""
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Converts impacts observed and forecasted to a geojson for visualization in QGIS
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:param sites_csv:
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:param observed_impacts_csv:
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:param forecast_impacts_csv:
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:param output_geojson:
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:return:
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"""
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# Get information from .csv and read into pandas dataframe
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df_sites = pd.read_csv(sites_csv, index_col=[0])
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df_observed = pd.read_csv(observed_impacts_csv, index_col=[0])
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df_forecast = pd.read_csv(forecast_impacts_csv, index_col=[0]).rename({"storm_regime": "forecast_storm_regime"})
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# Rename columns, so we can distinguish between forecast and observed
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df_observed = df_observed.rename(columns={"storm_regime": "observed_storm_regime"})
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df_forecast = df_forecast.rename(columns={"storm_regime": "forecast_storm_regime"})
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# Concat into one big dataframe
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df = pd.concat([df_sites, df_observed, df_forecast], sort=True, axis=1)
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# Make new column for accuracy of forecast. Use underpredict/correct/overpredict classes
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df.loc[df.observed_storm_regime == df.forecast_storm_regime, "forecast_accuray"] = "correct"
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# Observed/Forecasted/Class for each combination
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classes = [
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("swash", "collision", "overpredict"),
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("swash", "swash", "correct"),
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("swash", "overwash", "overpredict"),
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("collision", "swash", "underpredict"),
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("collision", "collision", "correct"),
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("collision", "overwash", "overpredict"),
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("overwash", "swash", "underpredict"),
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("overwash", "collision", "underpredict"),
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("overwash", "overwash", "correct"),
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]
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for c in classes:
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df.loc[(df.observed_storm_regime == c[0]) & (df.forecast_storm_regime == c[1]), "forecast_accuracy"] = c[2]
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schema = {
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"geometry": "Point",
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"properties": OrderedDict(
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[
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("beach", "str"),
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("site_id", "str"),
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("forecast_storm_regime", "str"),
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("observed_storm_regime", "str"),
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("forecast_accuracy", "str"),
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]
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),
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}
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with fiona.open(output_geojson, "w", driver="GeoJSON", crs=from_epsg(4326), schema=schema) as output:
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for index, row in df.iterrows():
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# Locate the marker at the seaward end of the profile to avoid cluttering the coastline.
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# Work out where seaward profile limit is
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sea_lat_lon = lat_lon_from_profile_x_coord(
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center_lat_lon=Point(row["lon"], row["lat"]),
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orientation=row["orientation"],
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center_profile_x=row["profile_x_lat_lon"],
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x_coord=2 * row["profile_x_lat_lon"],
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)
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prop = OrderedDict(
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[
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("beach", row["beach"]),
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("site_id", index),
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("forecast_storm_regime", row["forecast_storm_regime"]),
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("observed_storm_regime", row["observed_storm_regime"]),
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("forecast_accuracy", row["forecast_accuracy"]),
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]
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)
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output.write({"geometry": mapping(sea_lat_lon), "properties": prop})
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logger.info("Done!")
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@ -1,333 +0,0 @@
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"""
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Converts .csv files to .shape files
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"""
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import os
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import numpy.ma as ma
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import click
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import fiona
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import numpy as np
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import pandas as pd
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from fiona.crs import from_epsg
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from shapely.geometry import Point, mapping, LineString
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from collections import OrderedDict
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from data.parse_shp import convert_coord_systems
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from utils import setup_logging
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logger = setup_logging()
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def R_high_to_geojson(sites_csv, profiles_csv, impacts_csv, output_geojson):
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sites_csv = './data/interim/sites.csv'
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profiles_csv = './data/interim/profiles.csv'
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impacts_csv = './data/interim/impacts_forecasted_mean_slope_sto06.csv'
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output_geojson = './data/interim/R_high_forecasted_mean_slope_sto06.geojson'
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df_sites = pd.read_csv(sites_csv, index_col=[0])
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df_profiles = pd.read_csv(profiles_csv, index_col=[0,1,2])
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df_impacts = pd.read_csv(impacts_csv, index_col=[0])
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# Create geojson file
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schema = {
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'geometry': 'Point',
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'properties': OrderedDict([
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('beach', 'str'),
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('site_id', 'str'),
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('elevation', 'float'),
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])
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}
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with fiona.open(output_geojson, 'w', driver='GeoJSON', crs=from_epsg(4326), schema=schema) as output:
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for index, row in df_impacts.iterrows():
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site_id = index
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beach = index[:-4]
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# Find lat/lon of R_high position
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R_high_z = row['R_high']
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# Get poststorm profile (or should this be prestorm?)
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df_profile = df_profiles.query('site_id=="{}" & profile_type=="prestorm"'.format(index))
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int_x = crossings(df_profile.index.get_level_values('x').tolist(),
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df_profile.z.tolist(),
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|
||||||
R_high_z)
|
|
||||||
|
|
||||||
# Take most landward interesection. Continue to next site if there is no intersection
|
|
||||||
try:
|
|
||||||
int_x = max(int_x)
|
|
||||||
except:
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Get lat/lon on intercept position
|
|
||||||
site = df_sites.loc[site_id]
|
|
||||||
center_profile_x = site["profile_x_lat_lon"]
|
|
||||||
orientation = site["orientation"]
|
|
||||||
center_lat_lon = Point(site['lon'], site['lat']) # Get lat/lon of center of profile
|
|
||||||
center_xy = convert_coord_systems(center_lat_lon)
|
|
||||||
center_x, center_y = center_xy.xy
|
|
||||||
|
|
||||||
# Calculate xy position of point and convert to lat/lon
|
|
||||||
point_x = center_x + (center_profile_x - int_x) * np.cos(np.deg2rad(orientation))
|
|
||||||
point_y = center_y + (center_profile_x - int_x) * np.sin(np.deg2rad(orientation))
|
|
||||||
point_xy = Point(point_x, point_y)
|
|
||||||
point_lat_lon = convert_coord_systems(point_xy,
|
|
||||||
in_coord_system="EPSG:28356",
|
|
||||||
out_coord_system="EPSG:4326")
|
|
||||||
|
|
||||||
prop = OrderedDict([
|
|
||||||
('beach', beach),
|
|
||||||
('site_id', site_id),
|
|
||||||
('elevation', R_high_z),
|
|
||||||
])
|
|
||||||
output.write({"geometry": mapping(point_lat_lon), "properties": prop})
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def crossings(profile_x, profile_z, constant_z):
|
|
||||||
"""
|
|
||||||
Finds the x coordinate of a z elevation for a beach profile. Much faster than using shapely to calculate
|
|
||||||
intersections since we are only interested in intersections of a constant value. Will return multiple
|
|
||||||
intersections if found. Used in calculating beach slope.
|
|
||||||
Adapted from https://stackoverflow.com/a/34745789
|
|
||||||
:param profile_x: List of x coordinates for the beach profile section
|
|
||||||
:param profile_z: List of z coordinates for the beach profile section
|
|
||||||
:param constant_z: Float of the elevation to find corresponding x coordinates
|
|
||||||
:return: List of x coordinates which correspond to the constant_z
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Remove nans to suppress warning messages
|
|
||||||
valid = ~ma.masked_invalid(profile_z).mask
|
|
||||||
profile_z = np.array(profile_z)[valid]
|
|
||||||
profile_x = np.array(profile_x)[valid]
|
|
||||||
|
|
||||||
# Normalize the 'signal' to zero.
|
|
||||||
# Use np.subtract rather than a list comprehension for performance reasons
|
|
||||||
z = np.subtract(profile_z, constant_z)
|
|
||||||
|
|
||||||
# Find all indices right before any crossing.
|
|
||||||
# TODO Sometimes this can give a runtime warning https://stackoverflow.com/a/36489085
|
|
||||||
indicies = np.where(z[:-1] * z[1:] < 0)[0]
|
|
||||||
|
|
||||||
# Use linear interpolation to find intersample crossings.
|
|
||||||
return [profile_x[i] - (profile_x[i] - profile_x[i + 1]) / (z[i] - z[i + 1]) * (z[i]) for i in indicies]
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@click.command()
|
|
||||||
@click.option("--sites-csv", required=True, help=".csv file to convert")
|
|
||||||
@click.option("--profile-features-csv", required=True, help=".csv file to convert")
|
|
||||||
@click.option("--output-geojson", required=True, help="where to store .geojson file")
|
|
||||||
def profile_features_to_geojson(sites_csv, profile_features_csv, output_geojson):
|
|
||||||
"""
|
|
||||||
Converts profile_features containing dune toes and crest locations to a geojson we can load into QGIS
|
|
||||||
:param sites_csv:
|
|
||||||
:param profiles_csv:
|
|
||||||
:param profile_features_csv:
|
|
||||||
:param output_geojson:
|
|
||||||
:return:
|
|
||||||
"""
|
|
||||||
logger.info("Creating profile features geojson")
|
|
||||||
|
|
||||||
# Read files from interim folder
|
|
||||||
df_sites = pd.read_csv(sites_csv, index_col=[0])
|
|
||||||
df_profile_features = pd.read_csv(profile_features_csv, index_col=[0])
|
|
||||||
|
|
||||||
# Create geojson file
|
|
||||||
schema = {
|
|
||||||
'geometry': 'Point',
|
|
||||||
'properties': OrderedDict([
|
|
||||||
('beach', 'str'),
|
|
||||||
('site_id', 'str'),
|
|
||||||
('point_type', 'str'), # prestorm_dune_toe, prestorm_dune_crest, poststorm_dune_toe, poststorm_dune_crest
|
|
||||||
('profile_type', 'str'),
|
|
||||||
('elevation', 'float'),
|
|
||||||
])
|
|
||||||
}
|
|
||||||
|
|
||||||
with fiona.open(output_geojson, 'w', driver='GeoJSON', crs=from_epsg(4326), schema=schema) as output:
|
|
||||||
for index, row in df_profile_features.iterrows():
|
|
||||||
beach = index[:-4]
|
|
||||||
site_id = index
|
|
||||||
profile_type = row['profile_type']
|
|
||||||
|
|
||||||
for point_type in ['crest', 'toe']:
|
|
||||||
# point_type='crest'
|
|
||||||
elevation = row['dune_{}_z'.format(point_type)]
|
|
||||||
x = row['dune_{}_x'.format(point_type)]
|
|
||||||
|
|
||||||
if np.isnan(x):
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Geojsons need to use 'null' instead of 'nan'
|
|
||||||
if np.isnan(elevation):
|
|
||||||
elevation = None
|
|
||||||
|
|
||||||
# Convert x position to lat/lon
|
|
||||||
site = df_sites.loc[site_id]
|
|
||||||
center_profile_x = site["profile_x_lat_lon"]
|
|
||||||
orientation = site["orientation"]
|
|
||||||
center_lat_lon = Point(site['lon'], site['lat']) # Get lat/lon of center of profile
|
|
||||||
center_xy = convert_coord_systems(center_lat_lon)
|
|
||||||
center_x, center_y = center_xy.xy
|
|
||||||
|
|
||||||
# Calculate xy position of point and convert to lat/lon
|
|
||||||
point_x = center_x + (center_profile_x - x) * np.cos(np.deg2rad(orientation))
|
|
||||||
point_y = center_y + (center_profile_x - x) * np.sin(np.deg2rad(orientation))
|
|
||||||
point_xy = Point(point_x, point_y)
|
|
||||||
point_lat_lon = convert_coord_systems(point_xy,
|
|
||||||
in_coord_system="EPSG:28356",
|
|
||||||
out_coord_system="EPSG:4326")
|
|
||||||
|
|
||||||
prop = OrderedDict([
|
|
||||||
('beach', beach),
|
|
||||||
('site_id',site_id),
|
|
||||||
('point_type', point_type),
|
|
||||||
('profile_type', profile_type),
|
|
||||||
('elevation', elevation),
|
|
||||||
])
|
|
||||||
output.write({"geometry": mapping(point_lat_lon), "properties": prop})
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@click.command()
|
|
||||||
@click.option("--input-csv", required=True, help=".csv file to convert")
|
|
||||||
@click.option("--output-geojson", required=True, help="where to store .geojson file")
|
|
||||||
def sites_csv_to_geojson(input_csv, output_geojson):
|
|
||||||
"""
|
|
||||||
Converts our dataframe of sites to .geojson to load in QGis. Sites are loaded as linestrings of the profile
|
|
||||||
cross-sections
|
|
||||||
:param input_csv:
|
|
||||||
:param output_geojson:
|
|
||||||
:return:
|
|
||||||
"""
|
|
||||||
logger.info("Converting %s to %s", input_csv, output_geojson)
|
|
||||||
df_sites = pd.read_csv(input_csv, index_col=[0])
|
|
||||||
logger.info(os.environ.get("GDAL_DATA", None))
|
|
||||||
|
|
||||||
schema = {
|
|
||||||
'geometry': 'LineString',
|
|
||||||
'properties': OrderedDict([
|
|
||||||
('beach','str'),
|
|
||||||
('site_id', 'str'),
|
|
||||||
])
|
|
||||||
}
|
|
||||||
|
|
||||||
with fiona.open(output_geojson, 'w', driver='GeoJSON', crs=from_epsg(4326), schema=schema) as output:
|
|
||||||
for index, row in df_sites.iterrows():
|
|
||||||
# Work out where center of profile is
|
|
||||||
orientation = row["orientation"]
|
|
||||||
center_profile_x = row["profile_x_lat_lon"]
|
|
||||||
center_lon = row["lon"]
|
|
||||||
center_lat = row["lat"]
|
|
||||||
center_lat_lon = Point(center_lon, center_lat)
|
|
||||||
center_xy = convert_coord_systems(center_lat_lon)
|
|
||||||
center_x, center_y = center_xy.xy
|
|
||||||
|
|
||||||
# Work out where landward profile limit is
|
|
||||||
land_x = center_x + center_profile_x * np.cos(np.deg2rad(orientation))
|
|
||||||
land_y = center_y + center_profile_x * np.sin(np.deg2rad(orientation))
|
|
||||||
land_xy = Point(land_x, land_y)
|
|
||||||
land_lat_lon = convert_coord_systems(land_xy, in_coord_system="EPSG:28356",
|
|
||||||
out_coord_system="EPSG:4326")
|
|
||||||
|
|
||||||
# Work out where seaward profile limit is
|
|
||||||
sea_x = center_x - center_profile_x * np.cos(np.deg2rad(orientation))
|
|
||||||
sea_y = center_y - center_profile_x * np.sin(np.deg2rad(orientation))
|
|
||||||
sea_xy = Point(sea_x, sea_y)
|
|
||||||
sea_lat_lon = convert_coord_systems(sea_xy, in_coord_system="EPSG:28356", out_coord_system="EPSG:4326")
|
|
||||||
|
|
||||||
line_string = LineString([land_lat_lon, center_lat_lon, sea_lat_lon])
|
|
||||||
prop = OrderedDict([("beach", row["beach"]),
|
|
||||||
("site_id", index)])
|
|
||||||
output.write({"geometry": mapping(line_string), "properties": prop})
|
|
||||||
|
|
||||||
logger.info("Done!")
|
|
||||||
|
|
||||||
|
|
||||||
@click.command()
|
|
||||||
@click.option("--sites-csv", required=True, help="sites.csv file to convert")
|
|
||||||
@click.option("--observed-impacts-csv", required=True, help="impacts-observed.csv file to convert")
|
|
||||||
@click.option("--forecast-impacts-csv", required=True, help="impacts-forecast.csv file to convert")
|
|
||||||
@click.option("--output-geojson", required=True, help="where to store .geojson file")
|
|
||||||
def impacts_to_geojson(sites_csv, observed_impacts_csv, forecast_impacts_csv, output_geojson):
|
|
||||||
"""
|
|
||||||
Converts impacts observed and forecasted to a geojson for visualization in QGIS
|
|
||||||
:param sites_csv:
|
|
||||||
:param observed_impacts_csv:
|
|
||||||
:param forecast_impacts_csv:
|
|
||||||
:param output_geojson:
|
|
||||||
:return:
|
|
||||||
"""
|
|
||||||
|
|
||||||
# Get information from .csv and read into pandas dataframe
|
|
||||||
df_sites = pd.read_csv(sites_csv, index_col=[0])
|
|
||||||
df_observed = pd.read_csv(observed_impacts_csv, index_col=[0])
|
|
||||||
df_forecast = pd.read_csv(forecast_impacts_csv, index_col=[0]).rename({'storm_regime': 'forecast_storm_regime'})
|
|
||||||
|
|
||||||
# Rename columns, so we can distinguish between forecast and observed
|
|
||||||
df_observed = df_observed.rename(columns={'storm_regime': 'observed_storm_regime'})
|
|
||||||
df_forecast = df_forecast.rename(columns={'storm_regime': 'forecast_storm_regime'})
|
|
||||||
|
|
||||||
# Concat into one big dataframe
|
|
||||||
df = pd.concat([df_sites, df_observed, df_forecast], sort=True,axis=1)
|
|
||||||
|
|
||||||
# Make new column for accuracy of forecast. Use underpredict/correct/overpredict classes
|
|
||||||
df.loc[df.observed_storm_regime == df.forecast_storm_regime, 'forecast_accuray'] = 'correct'
|
|
||||||
|
|
||||||
# Observed/Forecasted/Class for each combination
|
|
||||||
classes = [('swash', 'collision', 'overpredict'),
|
|
||||||
('swash', 'swash', 'correct'),
|
|
||||||
('swash', 'overwash', 'overpredict'),
|
|
||||||
('collision', 'swash', 'underpredict'),
|
|
||||||
('collision', 'collision', 'correct'),
|
|
||||||
('collision', 'overwash', 'overpredict'),
|
|
||||||
('overwash', 'swash', 'underpredict'),
|
|
||||||
('overwash', 'collision', 'underpredict'),
|
|
||||||
('overwash', 'overwash', 'correct')]
|
|
||||||
for c in classes:
|
|
||||||
df.loc[(df.observed_storm_regime == c[0])&(df.forecast_storm_regime == c[1]), 'forecast_accuracy'] = c[2]
|
|
||||||
|
|
||||||
schema = {
|
|
||||||
'geometry': 'Point',
|
|
||||||
'properties': OrderedDict([
|
|
||||||
('beach','str'),
|
|
||||||
('site_id', 'str'),
|
|
||||||
('forecast_storm_regime', 'str'),
|
|
||||||
('observed_storm_regime', 'str',),
|
|
||||||
('forecast_accuracy', 'str')
|
|
||||||
])
|
|
||||||
}
|
|
||||||
|
|
||||||
# TODO Impact marker location should be at the seaward end of the profile
|
|
||||||
with fiona.open(output_geojson, 'w', driver='GeoJSON', crs=from_epsg(4326), schema=schema) as output:
|
|
||||||
for index, row in df.iterrows():
|
|
||||||
|
|
||||||
# Locate the marker at the seaward end of the profile to avoid cluttering the coastline.
|
|
||||||
# Work out where seaward profile limit is
|
|
||||||
orientation = row["orientation"]
|
|
||||||
center_profile_x = row["profile_x_lat_lon"]
|
|
||||||
center_lon = row["lon"]
|
|
||||||
center_lat = row["lat"]
|
|
||||||
center_lat_lon = Point(center_lon, center_lat)
|
|
||||||
center_xy = convert_coord_systems(center_lat_lon)
|
|
||||||
center_x, center_y = center_xy.xy
|
|
||||||
|
|
||||||
sea_x = center_x - center_profile_x * np.cos(np.deg2rad(orientation))
|
|
||||||
sea_y = center_y - center_profile_x * np.sin(np.deg2rad(orientation))
|
|
||||||
sea_xy = Point(sea_x, sea_y)
|
|
||||||
sea_lat_lon = convert_coord_systems(sea_xy, in_coord_system="EPSG:28356", out_coord_system="EPSG:4326")
|
|
||||||
|
|
||||||
prop = OrderedDict([
|
|
||||||
('beach',row["beach"]),
|
|
||||||
('site_id', index),
|
|
||||||
('forecast_storm_regime', row["forecast_storm_regime"]),
|
|
||||||
('observed_storm_regime', row["observed_storm_regime"],),
|
|
||||||
('forecast_accuracy', row["forecast_accuracy"])
|
|
||||||
])
|
|
||||||
|
|
||||||
output.write({"geometry": mapping(sea_lat_lon), "properties": prop})
|
|
||||||
|
|
||||||
|
|
||||||
logger.info("Done!")
|
|
||||||
Loading…
Reference in New Issue