Skip to content

Commit

Permalink
Merge branch 'alpha2-development' into documentation-start
Browse files Browse the repository at this point in the history
  • Loading branch information
@erikvansebille
erikvansebille committed May 3, 2024
2 parents 8ef43c1 + a46318b commit 6692fcc
Showing 1 changed file with 35 additions and 35 deletions.
70 changes: 35 additions & 35 deletions plasticparcels/scripts/create_release_maps.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -405,15 +405,14 @@ def create_global_concentrations_kaandorp_release_map(mask_land_filepath, mask_c
We match this data to the model coastal and ocean cells for a better coverage release.
We use only the 2020 data for all plastic class sizes, and for the ocean cells we only use the surface 0-5m depth.
"""

# Load in the data and select year 2020, all plastic sizes, and for ocean concentrations take the surface layer
ds = xr.open_dataset(kaandorp_filepath)
beach = ds['concentration_beach_mass_log10'].sel({'size_nominal':'all', 'time':2020})
ocean = ds['concentration_mass_log10'].sel({'size_nominal':'all', 'time':2020, 'depth':'0 - <5'})
beach = ds['concentration_beach_mass_log10'].sel({'size_nominal': 'all', 'time': 2020})
ocean = ds['concentration_mass_log10'].sel({'size_nominal': 'all', 'time': 2020, 'depth': '0 - <5'})

# Load in coast mask and model coordinates
data_mask_coast = xr.open_dataset(mask_coast_filepath)
coords = xr.open_dataset(coords_filepath, decode_cf=False)

lats_coast = data_mask_coast['lat'].data[np.where(data_mask_coast['mask_coast'])]
lons_coast = data_mask_coast['lon'].data[np.where(data_mask_coast['mask_coast'])]
Expand All @@ -422,12 +421,12 @@ def create_global_concentrations_kaandorp_release_map(mask_land_filepath, mask_c
# Create a list of lons, lats, concentration values
lon_beach = beach.lon_beach.values
lat_beach = beach.lat_beach.values
conc_beach = np.power(10,beach.values) # beach.values are in log10 form
conc_beach = np.power(10, beach.values) # beach.values are in log10 form

# Load Natural Earth dataset for attaching country information to beach source
shpfilename = shpreader.natural_earth(resolution='50m',
category='cultural',
name='admin_0_countries')
category='cultural',
name='admin_0_countries')
reader = shpreader.Reader(shpfilename)
countries = reader.records()

Expand All @@ -442,52 +441,52 @@ def create_global_concentrations_kaandorp_release_map(mask_land_filepath, mask_c
country_lons, country_lats = country_coords[:, 0], country_coords[:, 1]

country_df = pd.DataFrame({'Continent': np.repeat(continent, len(country_lons)),
'Region': np.repeat(region_un, len(country_lons)),
'Subregion': np.repeat(subregion, len(country_lons)),
'Country': np.repeat(country_name, len(country_lons)),
'Longitude': country_lons,
'Latitude': country_lats})
'Region': np.repeat(region_un, len(country_lons)),
'Subregion': np.repeat(subregion, len(country_lons)),
'Country': np.repeat(country_name, len(country_lons)),
'Longitude': country_lons,
'Latitude': country_lats})
countries_list.append(country_df)
coastal_df = pd.concat(countries_list)

# Create coastal concentrations dataset
coast_concentration_list = []
distance_threshhold=50.
distance_threshhold = 50.

for i, (lon, lat) in enumerate(zip(lons_coast,lats_coast)):
for i, (lon, lat) in enumerate(zip(lons_coast, lats_coast)):
# Find the closest beach concentration
distances = distance(np.repeat(lon, len(lon_beach)), np.repeat(lat, len(lat_beach)), lon_beach, lat_beach)
closest_beach_id = np.argmin(distances)
if distances[closest_beach_id] > distance_threshhold: # skip coastal grid cells not within a threshhold from a littered beach
if distances[closest_beach_id] > distance_threshhold: # skip coastal grid cells not within a threshhold from a littered beach
continue
else:
# Find the closest country point to the coastal cell to assign country information
distances_country = distance(np.repeat(lon, len(coastal_df['Longitude'])),
np.repeat(lat, len(coastal_df['Latitude'])),
coastal_df['Longitude'],
coastal_df['Latitude'])
np.repeat(lat, len(coastal_df['Latitude'])),
coastal_df['Longitude'],
coastal_df['Latitude'])
closest_country_id = np.argmin(distances_country)

coast_concentration_list.append({'Continent': coastal_df['Continent'].iloc[closest_country_id],
'Region': coastal_df['Region'].iloc[closest_country_id],
'Subregion': coastal_df['Subregion'].iloc[closest_country_id],
'Country': coastal_df['Country'].iloc[closest_country_id],
'Longitude': lon,
'Latitude': lat,
'Concentration': conc_beach[closest_beach_id],
'ConcentrationType': 'Beach'})
'Region': coastal_df['Region'].iloc[closest_country_id],
'Subregion': coastal_df['Subregion'].iloc[closest_country_id],
'Country': coastal_df['Country'].iloc[closest_country_id],
'Longitude': lon,
'Latitude': lat,
'Concentration': conc_beach[closest_beach_id],
'ConcentrationType': 'Beach'})

coast_concentration_df = pd.DataFrame.from_records(coast_concentration_list)

# Now tackle the surface ocean concentrations:
conc_ocean = np.power(10,ocean.values) # Values are in log10 space
conc_ocean = np.power(10, ocean.values) # Values are in log10 space

data_mask_land = xr.open_dataset(mask_land_filepath)
lats_ocean = data_mask_land['lat'].data[np.where(~data_mask_land['mask_land'])]
lons_ocean = data_mask_land['lon'].data[np.where(~data_mask_land['mask_land'])]

# Function to interpolate the ocean concentrations
f_interp_conc_ocean = RegularGridInterpolator((ocean.lon, ocean.lat), conc_ocean.T, method='nearest', bounds_error=False, fill_value=None)
f_interp_conc_ocean = RegularGridInterpolator((ocean.lon, ocean.lat), conc_ocean.T, method='nearest', bounds_error=False, fill_value=None)

# Interpolate the ocean concentrations to the model grid cells
interp_conc_ocean = f_interp_conc_ocean((lons_ocean, lats_ocean))
Expand All @@ -497,20 +496,21 @@ def create_global_concentrations_kaandorp_release_map(mask_land_filepath, mask_c
ocean_concentration_list = []
for i in np.where(non_nan_id)[0]:
ocean_concentration_list.append({'Continent': 'N/A',
'Region': 'N/A',
'Subregion': 'N/A',
'Country': 'N/A',
'Longitude': lons_ocean[i],
'Latitude': lats_ocean[i],
'Concentration': interp_conc_ocean[i],
'ConcentrationType': 'Ocean'})
'Region': 'N/A',
'Subregion': 'N/A',
'Country': 'N/A',
'Longitude': lons_ocean[i],
'Latitude': lats_ocean[i],
'Concentration': interp_conc_ocean[i],
'ConcentrationType': 'Ocean'})
ocean_concentration_df = pd.DataFrame.from_records(ocean_concentration_list)

# Combine the two beach and ocean datasets
concentration_df = pd.concat([ocean_concentration_df, coast_concentration_df])

return concentration_df


output_data = '/Users/denes001/Research/Projects/PlasticParcels/PlasticParcels/data/release/generated_files/'
mask_land_filepath = '/Users/denes001/Research/Projects/PlasticParcels/PlasticParcels/data/output_data/masks/mask_land_NEMO0083.nc'
mask_coast_filepath = '/Users/denes001/Research/Projects/PlasticParcels/PlasticParcels/data/output_data/masks/mask_coast_NEMO0083.nc'
Expand Down Expand Up @@ -565,4 +565,4 @@ def create_global_concentrations_kaandorp_release_map(mask_land_filepath, mask_c
concentration_dataset.to_csv(output_name)
print("Concentration map file created:", output_name)
else:
print("Concentration map file already exists:", output_name)
print("Concentration map file already exists:", output_name)

0 comments on commit 6692fcc

Please sign in to comment.