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Commit e6ff1511 authored by Eva Börgens's avatar Eva Börgens
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linting the code

parent 52034e1b
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.gitignore
View file @ e6ff1511
...@@ -3,3 +3,6 @@ ...@@ -3,3 +3,6 @@
# Ignore IDE # Ignore IDE
.idea .idea
# Ignore Test folder
Test
\ No newline at end of file
Pipfile
View file @ e6ff1511
...@@ -9,6 +9,7 @@ numpy = "==1.20.3" ...@@ -9,6 +9,7 @@ numpy = "==1.20.3"
scipy = "==1.6.3" scipy = "==1.6.3"
[dev-packages] [dev-packages]
pylint = "*"
[requires] [requires]
python_version = "3.8" python_version = "3.8"
Pipfile.lock
View file @ e6ff1511
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"requires": { "requires": {
...@@ -150,5 +150,79 @@ ...@@ -150,5 +150,79 @@
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src/__main__.py
View file @ e6ff1511
from .covariance import * """"
from .input_arguments import * This is the main file to invoke the regional tws uncertainty routines.
"""
from io import read_netcdf, read_ascii, save_results
from covariance import get_timeseries, compute_covariance
from input_arguments import arg_parser
parsed_command_line_input = arg_parser() parsed_command_line_input = arg_parser()
...@@ -15,17 +20,16 @@ region_coords = read_ascii(region_filename) ...@@ -15,17 +20,16 @@ region_coords = read_ascii(region_filename)
grid, grid_std, lon, lat, time = read_netcdf(filename) grid, grid_std, lon, lat, time = read_netcdf(filename)
timeseries = None
if flag_timeseries: if flag_timeseries:
timeseries, flag_timeseries = get_timeseries(grid, lon, lat, region_coords) timeseries, flag_timeseries = get_timeseries(grid, lon, lat, region_coords)
else:
timeseries = None
results = None
if flag_uncertainty or flag_matrix: if flag_uncertainty or flag_matrix:
results = compute_covariance(region_coords, grid_std, flag_uncertainty, flag_matrix) results = compute_covariance(region_coords, grid_std, flag_uncertainty, flag_matrix)
else:
results = None
save_results(out_filename, filename, results, region_coords, timeseries, flag_uncertainty, flag_matrix, flag_timeseries ) save_results(out_filename, filename, results, region_coords, timeseries,
flag_uncertainty, flag_matrix, flag_timeseries )
src/covariance.py
View file @ e6ff1511
from .io import * """"
Module handling the computation of the covariances and uncertainties
"""
import math
from typing import Dict
import scipy.special as sp
import numpy as np
import numpy.typing as npt
rho = np.pi/180 rho = np.pi/180
R = 6367.4447 R = 6367.4447
def get_grid_area(lon, lat): def get_grid_area(lon: npt.ArrayLike, lat: npt.ArrayLike) -> np.ndarray:
# Function getting the area weights of a coordinate list """"
Function getting the area weights of a coordinate list
w = np.zeros(len(lat)) Arg:
lon, array of longitudes: np.ndarray
lat, array of latitudes: np.ndarray
Returns:
area per grid point
"""
area = np.zeros(len(lat))
delta_lon = np.abs(np.unique(lon)[1]-np.unique(lon)[0]) delta_lon = np.abs(np.unique(lon)[1]-np.unique(lon)[0])
delta_lat = np.abs(np.unique(lat)[1] - np.unique(lat)[0]) delta_lat = np.abs(np.unique(lat)[1] - np.unique(lat)[0])
for i in range(len(lat)): for i, lat_i in enumerate(lat):
w[i] = get_area(0, lat[i]-delta_lat/2, delta_lon, lat[i]+delta_lat/2) area[i] = get_area(0, lat_i-delta_lat/2, delta_lon, lat_i+delta_lat/2)
return w return area
def get_area(lon_ll, lat_ll, lon_ur,lat_ur): def get_area(lon_ll: float, lat_ll: float, lon_ur: float,lat_ur: float) -> float:
# returns area of rectangular on sphere, defined by corner points lower left ll and upper right ur """"
a = np.abs((lon_ll-lon_ur)*rho)*np.abs((np.sin(lat_ll*rho)-np.sin(lat_ur*rho)))*R**2 returns area of rectangular on sphere, defined by corner points lower left ll and upper right ur
return a Arg:
lon_ll, lon of lower left corner: float
lat_ll, lat of lower left corner: float
def Cov_function_lat(lat1, lat2, d, theta, ny,a0, ka0_2, ka0_4, ka0_6, ka0_8, a1,ka1_2, ka1_4, ka1_6, ka1_8, c0, k2,k4, k6, k8): lon_ur, lon of upper right corner: float
# Function to compute covariance function between two points according to publication Boergens et al. 2020 lat_ur, lat of upper right corner: float
# Input: Returns:
# lat1, lat2: Latitude of the two points area per grid point
# d: distance between points """
# theta: azimuth angle between points area = np.abs((lon_ll-lon_ur)*rho)*np.abs((np.sin(lat_ll*rho)-np.sin(lat_ur*rho)))*R**2
# ny: order of Bessle function return area
# a0: anisotropy parameter
# ka0_2, ka0_4, ka0_6, ka0_8: Legende polynome parameters for a0
# a1: isotropy shape parameter def cov_function_lat(lat1: float, lat2: float,
# ka1_2, ka1_4, ka1_6, ka1_8: Legende polynome parameters for a1 dist: float,
# c0: amplitude parameter theta: float,
# k2,k4, k6, k8: Legende polynome parameters for c0 ny: float,
# Author: Eva Boergens, GFZ a_0: float, k_a_0_2: float, k_a_0_4: float, k_a_0_6: float, k_a_0_8: float,
# date: June 2019 a_1: float, k_a_1_2: float, k_a_1_4: float, k_a_1_6: float, k_a_1_8: float,
k = [1,0,k2,0,k4, 0, k6, 0, k8] c_0: float, k_2: float, k_4: float, k_6: float, k_8: float) -> float:
P1 = sum_Legendre(len(k)-1, k, lat1*rho) """"
P2 = sum_Legendre(len(k)-1, k, lat2*rho) Function to compute covariance function between two points according to
publication Boergens et al. 2020
ka0 = [1, 0, ka0_2, 0,ka0_4, 0, ka0_6, 0, ka0_8] Arg:
Pa0_1 = sum_Legendre(len(ka0)-1, ka0, np.mean([lat1, lat2])*rho) lat1, lat2, Latitude of the two points: float
d, distance between points: float
theta, azimuth angle between points: float
A0 = Pa0_1 * a0 ny, order of Bessle function: float
a0, anisotropy parameter: float
ka1 = [1, 0, ka1_2, 0,ka1_4, 0, ka1_6, 0, ka1_8] ka0_2, ka0_4, ka0_6, ka0_8, Legende polynome parameters for a0: float
Pa1_1 = sum_Legendre(len(ka1) - 1, ka1, np.mean([lat1, lat2]) * rho) a1, isotropy shape parameter: float
ka1_2, ka1_4, ka1_6, ka1_8, Legende polynome parameters for a1: float
A1 = Pa1_1 * a1 c0, amplitude parameter: float
if d==0: k2,k4, k6, k8, Legende polynome parameters for c0: float
d = 0.001 Returns:
C0 = yaglom(d, theta, A0, A1, c0, ny) Covariance: float
"""
Cov = P1*P2*C0 k = [1, 0, k_2, 0, k_4, 0, k_6, 0, k_8]
P_1 = sum_legendre(len(k) - 1, k, lat1 * rho)
P_2 = sum_legendre(len(k) - 1, k, lat2 * rho)
k_a_0 = [1, 0, k_a_0_2, 0, k_a_0_4, 0, k_a_0_6, 0, k_a_0_8]
P_a_0_1 = sum_legendre(len(k_a_0) - 1, k_a_0, np.mean([lat1, lat2]) * rho)
A_0 = P_a_0_1 * a_0
k_a_1 = [1, 0, k_a_1_2, 0, k_a_1_4, 0, k_a_1_6, 0, k_a_1_8]
P_a_1_1 = sum_legendre(len(k_a_1) - 1, k_a_1, np.mean([lat1, lat2]) * rho)
A_1 = P_a_1_1 * a_1
if dist==0:
dist = 0.001
C_0 = yaglom(dist, theta, A_0, A_1, c_0, ny)
Cov = P_1*P_2*C_0
return Cov return Cov
def Legendre_polynome(n,lat): def legendre_polynome(n: int, lat: float) -> float:
'''
Computes Legendre Polynome of degree n at given latitude lat
:param n: int
:param lat: float
:return: float
'''
sin_lat = np.sin(lat) sin_lat = np.sin(lat)
P_n = sp.legendre(n) P_n = sp.legendre(n)
P_n_x = P_n(sin_lat) P_n_x = P_n(sin_lat)
return P_n_x return P_n_x
def sum_Legendre(n_max, k, lat): def sum_legendre(n_max: int, leg_weights: npt.ArrayLike, lat:float) -> float:
if n_max!=len(k)-1: '''
Computes weighted sum of Legendre Polynomes
:param n_max: maximum degree of Legendre Polynomes: int
:param leg_weights: arrays of the weights for the sum: np.ndarray
:param lat: latitude where the Legendre Polynomes are summed: float
:return:
'''
if n_max!=len(leg_weights)-1:
raise('N_max coefficients are needed ') raise('N_max coefficients are needed ')
P = 0 p_sum = 0
for i in range(n_max+1): for i in range(n_max+1):
P += k[i]*Legendre_polynome(i, lat) p_sum += leg_weights[i] * legendre_polynome(i, lat)
return P return p_sum
def yaglom(d, theta, a0, a1, c0, ny): def yaglom(dist: float,
alpha = a0 * np.sin(theta) + a1 theta: float,
a_0: float,
C = c0 / (alpha * d) ** ny * sp.jv(ny, alpha * d) a_1: float,
return C c_0: float,
ny: float) -> float:
def distance(glon0, glat0, glon1, glat1): '''
# convert geograpic coordinates to spherical distances Function to compute the adapted Yaglom function
# author: Eva Boergens :param dist: spherical distance: float
:param theta: azimut angel: float
lon = glon1*rho :param a_0: anisotropic width parameter: float
lat = glat1*rho :param a_1: isotropic width parameter: float
:param c_0: global scaling factor: float
lon0 = glon0*rho :param ny: Order of Bessel function: int
lat0 = glat0*rho :return: float
sin_lat0 = math.sin(lat0)
cos_lat0 = math.cos(lat0) '''
cos_lon = math.cos(lon-lon0) alpha = a_0 * np.sin(theta) + a_1
sin_lat = math.sin(lat)
cos_lat = math.cos(lat) cov = c_0 / (alpha * dist) ** ny * sp.jv(ny, alpha * dist)
return cov
def distance(lon_0: float, lat_0: float, lon_1: float, lat_1: float):
'''
convert geograpic coordinates to spherical distances
:param lon_0[degree]: float
:param lat_0[degree]: float
:param lon_1[degree]: float
:param lat_1[degree]: float
:return: float
'''
lon_1_rad = lon_1 * rho
lat_1_rad = lat_1 * rho
lon_0_rad = lon_0 * rho
lat_0_rad = lat_0 * rho
sin_lat0 = math.sin(lat_0_rad)
cos_lat0 = math.cos(lat_0_rad)
cos_lon = math.cos(lon_1_rad-lon_0_rad)
sin_lat = math.sin(lat_1_rad)
cos_lat = math.cos(lat_1_rad)
hilf = sin_lat*sin_lat0 + cos_lat*cos_lat0*cos_lon hilf = sin_lat*sin_lat0 + cos_lat*cos_lat0*cos_lon
di = R * math.acos(hilf) d_i = R * math.acos(hilf)
if glon0==glon1 and glat0==glat1: if lon_0==lon_1 and lat_0==lat_1:
di =0 d_i =0
return di return d_i
def azimut_angle(lon0, lat0, glon1, glat1): def azimut_angle(lon_0: float, lat_0: float, lon_1: float, lat_1: float):
# get azimut angle between geograpic coordinates '''
# author: Eva Boergens get azimut angle between geograpic coordinates
:param lon_0[degree]: float
:param lat_0[degree]: float
:param lon_1[degree]: float
:param lat_1[degree]: float
:return: float
'''
lat1 = glat1 * rho lat_1_rad = lat_1 * rho
lat0 = lat0 * rho lat_0_rad = lat_0 * rho
lon0 = lon0 * rho lon_0_rad = lon_0 * rho
lon1 = glon1 * rho lon_1_rad = lon_1 * rho
sin_lat0 = math.sin(lat0) sin_lat0 = math.sin(lat_0_rad)
cos_lat0 = math.cos(lat0) cos_lat0 = math.cos(lat_0_rad)
tan_lat = math.tan(lat1) tan_lat = math.tan(lat_1_rad)
cos_lon = math.cos(lon1-lon0) cos_lon = math.cos(lon_1_rad-lon_0_rad)
sin_lon = math.sin(lon1-lon0) sin_lon = math.sin(lon_1_rad-lon_0_rad)
alpha = math.atan2(sin_lon,(cos_lat0*tan_lat-sin_lat0*cos_lon)) alpha = math.atan2(sin_lon,(cos_lat0*tan_lat-sin_lat0*cos_lon))
if glat1 == 90: if lat_1 == 90:
alpha=0 alpha=0
elif glat1 ==-90: elif lat_1 ==-90:
alpha = np.pi alpha = np.pi
return alpha return alpha
def compute_covariance(region_coords, gridstd, flag_uncertainty, flag_matrix): def compute_covariance(region_coords: npt.ArrayLike,
gridstd: npt.ArrayLike,
flag_uncertainty: bool, flag_matrix: bool) -> Dict[str, np.ndarray]:
'''
Function to compute the covariances for a region
:param region_coords: coordinates of region: np.ndarray[n,2]
:param gridstd: standard deviation for each grid point: np.ndarray[n]
:param flag_uncertainty: return uncertainty of mean tws of region: bool
:param flag_matrix: return covariance matrix of region: bool
:return: Dict[str, np.ndarray]
'''
lon = np.array([r[0] for r in region_coords]) lon = np.array([r[0] for r in region_coords])
lat = np.array([r[1] for r in region_coords]) lat = np.array([r[1] for r in region_coords])
...@@ -134,51 +218,55 @@ def compute_covariance(region_coords, gridstd, flag_uncertainty, flag_matrix): ...@@ -134,51 +218,55 @@ def compute_covariance(region_coords, gridstd, flag_uncertainty, flag_matrix):
m = len(gridstd) m = len(gridstd)
# Grid weights # Grid weights
w = get_grid_area(lon, lat) area = get_grid_area(lon, lat)
region_size = np.sum(w) region_size = np.sum(area)
# Parameter of covariance function # Parameter of covariance function
x = np.array([1.09008520e-03, -2.91816028e+00, -1.90076454e+00, -8.56445896e-01, -4.60125098e+00, x = np.array([1.09e-03, -2.92, -1.90, -0.86, -4.60,
0.00265558, 0.40380741, -0.29669686, 0.06876961, 0.32137515, 2.66e-03, 0.40, -0.30, 0.07, 0.32,
5.82423256 * 2.30 * 0.08 * 2.41 * 1.45, -0.2, -0.26511069, -0.17415433, -0.21936711]) 3.74, -0.2, -0.27, -0.17, -0.22])
if flag_uncertainty: if flag_uncertainty:
var_model = np.zeros(m) var_model = np.zeros(m)
else:
var_model = None
if flag_matrix: if flag_matrix:
cov_model = np.zeros((m,n,n)) cov_model = np.zeros((m,n,n))
else:
cov_model = None
for ii in range(n): for ii in range(n):
d = 0 dist = 0
theta = np.pi * 2 theta = np.pi * 2
C = Cov_function_lat(lat[ii], lat[ii], d, theta, 2, *x) corr = cov_function_lat(lat[ii], lat[ii], dist, theta, 2, *x)
w1 = w[ii] / region_size w_1 = area[ii] / region_size
w2 = w[ii] / region_size w_2 = area[ii] / region_size
for j in range(m): for j in range(m):
sigma = gridstd[j] sigma = gridstd[j]
if np.isnan(sigma): if np.isnan(sigma):
continue continue
if flag_uncertainty: if flag_uncertainty:
var_model[j] += w1 * w2 * sigma **2 * C var_model[j] += w_1 * w_2 * sigma **2 * corr
if flag_matrix: if flag_matrix:
cov_model[j,ii,ii] = sigma **2 * C cov_model[j,ii,ii] = sigma **2 * corr
for jj in range(ii+1, n): for jj in range(ii+1, n):
d = distance(lon[ii], lat[ii], lon[jj], lat[jj]) dist = distance(lon[ii], lat[ii], lon[jj], lat[jj])
theta = azimut_angle(lon[ii], lat[ii], lon[jj], lat[jj]) theta = azimut_angle(lon[ii], lat[ii], lon[jj], lat[jj])
C = Cov_function_lat(lat[ii], lat[jj], d, theta, 2, *x) corr = cov_function_lat(lat[ii], lat[jj], dist, theta, 2, *x)
w1 = w[ii] / region_size w_1 = area[ii] / region_size
w2 = w[jj] / region_size w_2 = area[jj] / region_size
for j in range(m): for j in range(m):
sigma = gridstd[j] sigma = gridstd[j]
if np.isnan(sigma): if np.isnan(sigma):
continue continue
if flag_uncertainty: if flag_uncertainty:
var_model[j] += w1 * w2 * sigma**2 * C var_model[j] += w_1 * w_2 * sigma**2 * corr
if flag_matrix: if flag_matrix:
cov_model[j, ii, jj] = sigma ** 2 * C cov_model[j, ii, jj] = sigma ** 2 * corr
cov_model[j, jj, ii] = sigma ** 2 * C cov_model[j, jj, ii] = sigma ** 2 * corr
result = {} result = {}
if flag_uncertainty: if flag_uncertainty:
std_model = np.sqrt(var_model) std_model = np.sqrt(var_model)
...@@ -189,17 +277,25 @@ def compute_covariance(region_coords, gridstd, flag_uncertainty, flag_matrix): ...@@ -189,17 +277,25 @@ def compute_covariance(region_coords, gridstd, flag_uncertainty, flag_matrix):
def get_timeseries(grid, lon_grid, lat_grid, region_coords): def get_timeseries(grid, lon_grid, lat_grid, region_coords):
'''
Returns mean tws time series of region
:param grid: tws grid: np.ndarray[t,n,m]
:param lon_grid: longitude of grid: np.ndarray[m]
:param lat_grid: latitude of grid: np.ndarray[n]
:param region_coords: coordinates of region of interest: np.ndarray[l,2]
:return: np.ndarray[t]
'''
lon_region = np.array([r[0] for r in region_coords]) lon_region = np.array([r[0] for r in region_coords])
lat_region = np.array([r[1] for r in region_coords]) lat_region = np.array([r[1] for r in region_coords])
if len(np.isin(lon_grid, lon_region).nonzero()[0])< len(np.unique(lon_region)) or \ if len(np.isin(lon_grid, lon_region).nonzero()[0])< len(np.unique(lon_region)) or \
len(np.isin(lat_grid, lat_region).nonzero()[0])< len(np.unique(lat_region)):