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interpolation.py
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interpolation.py
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from copy import deepcopy
import numpy as np
import yaml
from scipy.interpolate import Rbf, RegularGridInterpolator
import data_request as dr
def spherical_to_cartesian(lat, lon, r=1):
"""
return cartesian coordinates of given spherical coordinates
:param r:
:param lon: longitude of given point
:param lat: latitude of given point
:return: a tuple of three coordinates in cartesian system
"""
lat_rad = np.deg2rad(lat)
lon_rad = np.deg2rad(lon)
x = r * np.cos(lat_rad) * np.cos(lon_rad)
y = r * np.cos(lat_rad) * np.sin(lon_rad)
z = r * np.sin(lat_rad)
return x, y, z
def get_data(wave_wind_not_inter):
"""
extract longitude, latitude and time data from fetched wave and wind data
:param wave_wind_not_inter: wave_wind from watcher data
:return: a tuple of three dimensions
"""
lat = np.array(wave_wind_not_inter["latitude"])
lon = np.array(wave_wind_not_inter["longitude"])
time = np.array(wave_wind_not_inter["time"])
return lat, lon, time
def get_copernicus_data(data):
lat = np.array(data["coords"]["latitude"]["data"])
lon = np.array(data["coords"]["longitude"]["data"])
time = np.array(data["coords"]["time"]["data"])
time = time.astype('datetime64[s]').astype('int64')
return lat, lon, time
def check_keys(keys_to_check, wave_wind_not_inter, keys, weather, result, lon_inter, lat_inter, time_inter):
wave_and_wind_dict = {
"dirpw": "wave_direction",
"swh": "wave_height",
"perpw": "wave_period",
"u": "u",
"v": "v",
"ws": "wind_speed"
}
for key in keys_to_check:
if key in wave_wind_not_inter:
if key == "dirpw":
components = [wave_and_wind_dict[key] + "_x", wave_and_wind_dict[key] + "_y"]
og_key = key
for c in components:
key = c
keys.append(key)
key_inter = key + "_inter"
if key[-1] == "x":
weather[key] = np.cos(np.deg2rad(np.array(wave_wind_not_inter[og_key])))
else:
weather[key] = np.sin(np.deg2rad(np.array(wave_wind_not_inter[og_key])))
weather[key + "_mask"] = np.isnan(weather[key]).astype(float)
keys.append(key + "_mask")
result[key_inter] = [[[0] * len(lon_inter) for _ in range(len(lat_inter))] for _ in
range(len(time_inter))]
result[key + "_mask" + "_inter"] = [[[0] * len(lon_inter) for _ in range(len(lat_inter))] for _ in
range(len(time_inter))]
else:
keys.append(wave_and_wind_dict[key])
key_inter = wave_and_wind_dict[key] + "_inter"
weather[wave_and_wind_dict[key]] = np.array(wave_wind_not_inter[key])
weather[wave_and_wind_dict[key] + "_mask"] = np.isnan(weather[wave_and_wind_dict[key]]).astype(float)
keys.append(wave_and_wind_dict[key] + "_mask")
result[key_inter] = [[[0] * len(lon_inter) for _ in range(len(lat_inter))] for _ in
range(len(time_inter))]
result[wave_and_wind_dict[key] + "_mask" + "_inter"] = [[[0] * len(lon_inter) for _ in range(len(lat_inter))] for _ in
range(len(time_inter))]
def latlon_interpolation(time, weather, key, lat_grid, lon_grid, lat_inter_grid, lon_inter_grid, res):
for k in range(len(time)):
elem = weather[key]
nan_mask = np.isnan(elem[k])
indices = np.where(~nan_mask)
data_points_valid = elem[k][indices]
lat_valid = lat_grid[~nan_mask]
lon_valid = lon_grid[~nan_mask]
x, y, z = spherical_to_cartesian(lat_valid.ravel(), lon_valid.ravel())
interp_spatial = Rbf(x, y, z, data_points_valid, function='thin_plate', smooth=0)
x_inter, y_inter, z_inter = spherical_to_cartesian(lat_inter_grid, lon_inter_grid)
res[k] = interp_spatial(x_inter, y_inter, z_inter)
def time_interpolation(time, lat_inter, lon_inter, res, key, time_inter, weather):
interpolator = RegularGridInterpolator((time, lat_inter, lon_inter), res)
key_inter = key + "_inter"
result = [[[0] * len(lon_inter) for _ in range(len(lat_inter))] for _ in range(len(time_inter))]
for k in range(len(time_inter)):
for i in range(len(lat_inter)):
for j in range(len(lon_inter)):
try:
result[k][i][j] = interpolator([time_inter[k], lat_inter[i], lon_inter[j]])
except:
result[k][i][j] = None
weather[key] = [[[float(value[0]) if value is not None else None for value in row] for row in slice_] for slice_ in result]
def apply_nan_masc(keys_to_iter, weather, land_treshhold):
for k in keys_to_iter:
if "mask" in k:
key_to_nan = k.replace("_mask", "")
for t in range(len(weather[key_to_nan])):
for l in range(len(weather[key_to_nan][t])):
wl = weather[k][t][l]
for lt in range(len(weather[key_to_nan][t][l])):
wlt = wl[lt]
if wlt is not None:
if wlt >= land_treshhold:
weather[key_to_nan][t][l][lt] = np.NaN
else:
weather[key_to_nan][t][l][lt] = np.NaN
# weather.pop(k)
def interpolate_for_copernicus(weather, result, request, requested_time):
if isinstance(request, dr.DataRequest):
interval = request.get_time_interval()
with open("config.yaml", "r") as f:
config = yaml.safe_load(f)
resolution = config["resolution"]
land_treshhold = config["land_treshhold"]
cop_weather = {}
try:
data = result["copernicus"]
except:
return weather
land_treshhold = 0.5
for el in data:
element = data[el]
lat, lon, time = get_copernicus_data(element)
if weather != {}:
lat_inter = weather["lat_inter"]
lon_inter = weather["lon_inter"]
time_inter = weather["time_inter"]
else:
if requested_time[0] != requested_time[-1]:
time_inter = np.arange(requested_time[0], requested_time[-1], int(interval * 60))
else:
time_inter = requested_time[0]
lat_inter = np.arange(lat[0], lat[-1], resolution)
lon_inter = np.arange(lon[0], lon[-1], resolution)
weather["time_inter"] = time_inter.tolist()
weather["lat_inter"] = lat_inter.tolist()
weather["lon_inter"] = lon_inter.tolist()
lon_grid, lat_grid = np.meshgrid(lon, lat)
lon_inter_grid, lat_inter_grid = np.meshgrid(lon_inter, lat_inter)
for key in element["data_vars"]:
res = [[[0] * len(lon_inter) for _ in range(len(lat_inter))] for _ in range(len(time))]
key_inter = key + "_inter"
result[key_inter] = [[[0] * len(lon_inter) for _ in range(len(lat_inter))] for _ in
range(len(time_inter))]
reduced_array = [sublist[0] for sublist in element["data_vars"][key]["data"]]
cop_weather[key] = np.array(reduced_array)
cop_weather[key + "_mask"] = np.isnan(cop_weather[key]).astype(float)
latlon_interpolation(time, cop_weather, key, lat_grid, lon_grid, lat_inter_grid, lon_inter_grid, res)
latlon_interpolation(time, cop_weather, key + "_mask", lat_grid, lon_grid, lat_inter_grid, lon_inter_grid, res)
time_interpolation(time, lat_inter, lon_inter, res, key, time_inter, cop_weather)
time_interpolation(time, lat_inter, lon_inter, res, key + "_mask", time_inter, cop_weather)
keys_to_iter = deepcopy(list(cop_weather.keys()))
apply_nan_masc(keys_to_iter, cop_weather, land_treshhold)
weather_copy = cop_weather.copy()
for key in cop_weather:
if key[-5:] != "_mask":
if key == "zos":
weather["tide_height"] = cop_weather[key]
else:
weather[key] = cop_weather[key]
try:
curr_request = request.parse_for_copernicus_currents()["request"]
except:
return weather
for e in curr_request:
if e == "sea_current_direction":
key_weather = np.arctan2(weather["uo"], weather["vo"]) * (180 / np.pi) + 180
key_weather = np.mod(key_weather, 360)
weather[e] = [[[float(value) for value in row] for row in slice_] for slice_ in key_weather]
elif e == "sea_current_speed":
wind_speed = np.sqrt(np.power(weather["uo"], 2) + np.power(weather["vo"], 2))
weather[e] = [[[float(value) for value in row] for row in slice_] for slice_ in wind_speed]
if "uo" in weather:
del weather["uo"]
del weather["vo"]
return weather
def interpolate(result, request, requested_time):
print("Interpolating data...")
if isinstance(request, dr.DataRequest):
interval = request.get_time_interval()
with open("config.yaml", "r") as f:
config = yaml.safe_load(f)
resolution = config["resolution"]
land_treshhold = config["land_treshhold"]
weather = {}
wave_wind_not_inter = result["waves_and_wind"]
if wave_wind_not_inter is not None:
lat, lon, time = get_data(wave_wind_not_inter)
lat_inter = np.arange(lat[0], lat[-1], resolution)
lon_inter = np.arange(lon[0], lon[-1], resolution)
if requested_time[0] != requested_time[-1]:
time_inter = np.arange(requested_time[0], requested_time[-1], int(interval * 60))
else:
time_inter = requested_time[0]
keys_to_check = ["dirpw", "swh", "perpw", "u", "v", "ws"]
keys = []
check_keys(keys_to_check, wave_wind_not_inter, keys, weather, result, lon_inter, lat_inter, time_inter)
lon_grid, lat_grid = np.meshgrid(lon, lat)
lon_inter_grid, lat_inter_grid = np.meshgrid(lon_inter, lat_inter)
res = [[[0] * len(lon_inter) for _ in range(len(lat_inter))] for _ in range(len(time))]
for key in keys:
latlon_interpolation(time, weather, key, lat_grid, lon_grid, lat_inter_grid, lon_inter_grid, res)
time_interpolation(time, lat_inter, lon_inter, res, key, time_inter, weather)
keys_to_iter = deepcopy(list(weather.keys()))
apply_nan_masc(keys_to_iter, weather, land_treshhold)
weather_copy = weather.copy()
for el in weather_copy:
if el[-2:] == "_x":
key = el[:-2]
key_weather = np.rad2deg(np.arctan2(weather[key + "_y"], weather[key + "_x"]))
key_weather = np.mod(key_weather, 360)
weather[key] = [[[float(value) for value in row] for row in slice_] for slice_ in key_weather]
del weather[key + "_x"]
del weather[key + "_y"]
elif el == "v":
key = "wind_direction"
key_weather = np.arctan2(weather["u"], weather["v"]) * (180 / np.pi) + 180
key_weather = np.mod(key_weather, 360)
weather[key] = [[[float(value) for value in row] for row in slice_] for slice_ in key_weather]
del weather["u"]
del weather["v"]
elif el[-4:] == "mask":
del weather[el]
print("Interpolation complete")
weather["time_inter"] = time_inter.tolist()
weather["lat_inter"] = lat_inter.tolist()
weather["lon_inter"] = lon_inter.tolist()
return interpolate_for_copernicus(weather, result, request, requested_time)