Finding Ocean Eddies using Satellite Altimetry: Part 2#
In Part 1 of “Finding Ocean Eddies using Satellite Altimetry”, we explored the AVISO+ Mesoscale Eddy Trajectory Atlas (META3.2 DT) to identify a significant eddy within a region and timeframe of interest. After selecting a target eddy, we extracted its key parameters and retrieved Sentinel-3 altimetry data within a defined buffer zone around it.
In Part 2 of this study, we will use GPSat to optimally interpolate the sea surface height anomaly (SSHA) derived from Sentinel-3 data in attempt to reconstruct our selected eddy.
Install and load GPSat and the required packages#
try:
import google.colab
IN_COLAB = True
except:
IN_COLAB = True
if IN_COLAB:
import sys
import os
import re
# change to working directory
work_dir = os.getcwd()
assert os.path.exists(work_dir), f"workspace directory: {work_dir} does not exist"
os.chdir(work_dir)
# clone repository
! git clone https://github.com/CPOMUCL/GPSat.git
repo_dir = os.path.join(work_dir, "GPSat")
print(f"changing directory to: {repo_dir}")
os.chdir(repo_dir)
! pip install -r requirements.txt
! pip install -e .
! pip install --upgrade pandas
print(f"changing directory back to: {work_dir}")
os.chdir(work_dir)
sys.path.append(os.path.join(repo_dir, "GPSat"))
Cloning into 'GPSat'...
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changing directory to: /content/GPSat
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INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
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?25hInstalling collected packages: lit, global-land-mask, wrapt, wadler-lindig, tensorflow-estimator, qtpy, nvidia-nvtx-cu11, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu12, nvidia-curand-cu11, nvidia-cufft-cu12, nvidia-cufft-cu11, nvidia-cuda-runtime-cu12, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu12, nvidia-cuda-nvrtc-cu11, nvidia-cuda-nvcc-cu12, nvidia-cuda-cupti-cu12, nvidia-cuda-cupti-cu11, nvidia-cublas-cu12, nvidia-cublas-cu11, mypy-extensions, ml-dtypes, marshmallow, lark, keras, jedi, dropstackframe, cftime, typing-inspect, scikit-learn, pandas, nvidia-cusparse-cu12, nvidia-cusolver-cu11, nvidia-cudnn-cu12, nvidia-cudnn-cu11, netCDF4, marshmallow-enum, jaxtyping, check-shapes, xarray, tensorflow-probability, sphinxemoji, sphinxcontrib-jquery, nvidia-cusolver-cu12, numpydoc, fastparquet, dataclasses-json, cartopy, tensorboard, sphinx-rtd-theme, qtconsole, tensorflow, gpflow, nbsphinx, jupyter, triton, torch, linear-operator, gpytorch
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 1.5.3 which is incompatible.
mlxtend 0.23.4 requires scikit-learn>=1.3.1, but you have scikit-learn 1.2.2 which is incompatible.
torchaudio 2.5.1+cu124 requires torch==2.5.1, but you have torch 2.0.1 which is incompatible.
tf-keras 2.18.0 requires tensorflow<2.19,>=2.18, but you have tensorflow 2.15.1 which is incompatible.
plotnine 0.14.5 requires pandas>=2.2.0, but you have pandas 1.5.3 which is incompatible.
imbalanced-learn 0.13.0 requires scikit-learn<2,>=1.3.2, but you have scikit-learn 1.2.2 which is incompatible.
dask-expr 1.1.21 requires pandas>=2, but you have pandas 1.5.3 which is incompatible.
torchvision 0.20.1+cu124 requires torch==2.5.1, but you have torch 2.0.1 which is incompatible.
tensorflow-text 2.18.1 requires tensorflow<2.19,>=2.18.0, but you have tensorflow 2.15.1 which is incompatible.
dask-cudf-cu12 25.2.2 requires pandas<2.2.4dev0,>=2.0, but you have pandas 1.5.3 which is incompatible.
mizani 0.13.1 requires pandas>=2.2.0, but you have pandas 1.5.3 which is incompatible.
jax 0.5.2 requires ml_dtypes>=0.4.0, but you have ml-dtypes 0.3.2 which is incompatible.
cudf-cu12 25.2.1 requires pandas<2.2.4dev0,>=2.0, but you have pandas 1.5.3 which is incompatible.
Successfully installed cartopy-0.22.0 cftime-1.6.4.post1 check-shapes-1.1.1 dataclasses-json-0.5.7 dropstackframe-0.1.1 fastparquet-2024.11.0 global-land-mask-1.0.0 gpflow-2.9.2 gpytorch-1.10 jaxtyping-0.2.38 jedi-0.19.2 jupyter-1.0.0 keras-2.15.0 lark-1.2.2 linear-operator-0.6 lit-18.1.8 marshmallow-3.26.1 marshmallow-enum-1.5.1 ml-dtypes-0.3.2 mypy-extensions-1.0.0 nbsphinx-0.9.7 netCDF4-1.6.2 numpydoc-1.8.0 nvidia-cublas-cu11-11.10.3.66 nvidia-cublas-cu12-12.2.5.6 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-cupti-cu12-12.2.142 nvidia-cuda-nvcc-cu12-12.2.140 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-nvrtc-cu12-12.2.140 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cuda-runtime-cu12-12.2.140 nvidia-cudnn-cu11-8.5.0.96 nvidia-cudnn-cu12-8.9.4.25 nvidia-cufft-cu11-10.9.0.58 nvidia-cufft-cu12-11.0.8.103 nvidia-curand-cu11-10.2.10.91 nvidia-curand-cu12-10.3.3.141 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusolver-cu12-11.5.2.141 nvidia-cusparse-cu11-11.7.4.91 nvidia-cusparse-cu12-12.1.2.141 nvidia-nccl-cu11-2.14.3 nvidia-nccl-cu12-2.16.5 nvidia-nvjitlink-cu12-12.2.140 nvidia-nvtx-cu11-11.7.91 pandas-1.5.3 qtconsole-5.6.1 qtpy-2.4.3 scikit-learn-1.2.2 sphinx-rtd-theme-3.0.2 sphinxcontrib-jquery-4.1 sphinxemoji-0.3.1 tensorboard-2.15.2 tensorflow-2.15.1 tensorflow-estimator-2.15.0 tensorflow-probability-0.23.0 torch-2.0.1 triton-2.0.0 typing-inspect-0.9.0 wadler-lindig-0.1.3 wrapt-1.14.1 xarray-2024.3.0
Obtaining file:///content/GPSat
Preparing metadata (setup.py) ... ?25l?25hdone
Installing collected packages: GPSat
Running setup.py develop for GPSat
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?25hInstalling collected packages: pandas
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.2.3 which is incompatible.
mlxtend 0.23.4 requires scikit-learn>=1.3.1, but you have scikit-learn 1.2.2 which is incompatible.
Successfully installed pandas-2.2.3
changing directory back to: /content
gpsat_repo_dir = os.path.join(os.getcwd(), "GPSat/")
sys.path.append(gpsat_repo_dir)
import sys
import xarray as xr
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from pyproj import Proj, transform
from pathlib import Path
from scipy.spatial import KDTree
from GPSat import get_data_path, get_parent_path
from GPSat.dataprepper import DataPrep
from GPSat.utils import WGS84toEASE2_New, EASE2toWGS84_New, cprint, grid_2d_flatten, get_weighted_values
from GPSat.local_experts import LocalExpertOI, get_results_from_h5file
from GPSat.plot_utils import plot_pcolormesh, get_projection, plot_pcolormesh_from_results_data
from GPSat.config_dataclasses import DataConfig, ModelConfig, PredictionLocsConfig, ExpertLocsConfig
from GPSat.postprocessing import glue_local_predictions_2d
from global_land_mask import globe
import matplotlib.lines as mlines
Set paths#
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
week8_path = Path('/content/drive/MyDrive/GEOL0069/2425/Week 8')
Load our selected eddy#
#Load the selected eddy data
eddy_number = 718441
date = pd.to_datetime("2019-01-18").date()
#load the selected eddy
selected_eddy = gpd.read_file(week8_path / f"selected_eddy_{date}_{eddy_number}.gpkg", layer='row')
selected_eddy
#The eddy coords use the 0-360 longitude convention, let's change them to -180 to 180 for consistency with the EPSG:4326 projection
from shapely.geometry import Polygon
# Function to shift longitudes from 0-360 to -180 to 180
def shift_longitude(geometry):
if geometry.geom_type == "Polygon":
new_coords = [((lon - 360 if lon > 180 else lon), lat) for lon, lat in geometry.exterior.coords]
return Polygon(new_coords)
elif geometry.geom_type == "MultiPolygon":
new_polygons = []
for poly in geometry.geoms:
new_coords = [((lon - 360 if lon > 180 else lon), lat) for lon, lat in poly.exterior.coords]
new_polygons.append(Polygon(new_coords))
return gpd.GeoSeries(new_polygons).unary_union # Convert list back to MultiPolygon
return geometry
selected_eddy["geometry"] = selected_eddy["geometry"].apply(shift_longitude)
selected_eddy = selected_eddy.set_crs('EPSG:4326')
#plot the selected eddy location
fig, ax = plt.subplots(subplot_kw={'projection': ccrs.NorthPolarStereo()})
ax.set_extent([-25, -90, 60, 80], crs=ccrs.PlateCarree())
ax.add_feature(cfeature.COASTLINE)
ax.add_geometries([selected_eddy.geometry.iloc[0]], crs=ccrs.PlateCarree(), facecolor='red', edgecolor='red')
eddy_proxy = mlines.Line2D([], [], color='red', linewidth=4, alpha=0.8, label="Selected known eddy")
ax.legend(handles=[eddy_proxy], loc='lower left')
plt.show()
Load the Sentinel-3 altimetry data and compute SSHA#
Sea Surface Height Anomaly (SSHA) represents the deviation of the instantaneous sea surface height (SSH) from a long-term mean sea surface (MSS). It provides insights into ocean circulation, mesoscale eddies, and sea level variability, making it a key parameter in altimetry-based ocean studies.
SSHA is calculated as:
\[
\text{SSHA} = \text{SSH} - \text{MSS}
\]
where:
SSH (Sea Surface Height) is the instantaneous height of the sea surface measured by the satellite altimeter. MSS (Mean Sea Surface) represents the long-term average sea surface height, derived from multi-year satellite observations. SSHA is particularly useful for detecting dynamic ocean features, such as eddies, currents, and tides, as it removes the static component of the sea surface height, revealing short-term variability.
The Sentinel-3 product we will be using in this practical does not include SSH derived from the ideal opean ocean retracker (SAMOSA). We therefore need to compute it from the SAMOSA-retracked range to the water surface, including correcting for geophysical effects contaminating our signal.
SSHA is therefore calculated as:
SSH = H - R - C
\[
\text{SSH} = \text{H} - \text{R} - \text{C}
\]
where:
H = Satellite Altitude
R = Ku-band retracked range to water
C = Geophysical Corrections, which include: Ionospheric correction, dry tropospheric correction, wet tropospheric corrections, sea state bias correction, tidal corrections, inverted barometer correction high-frequency sea surface fluctuation correction
def add_sea_surface_height_and_anomaly(sral_ds):
"""
Calculate the sea surface height anomaly from a Sentinel-3 SRAL dataset.
Based on the L2 WAT definition:
SSHA = altitude of satellite (alt_20_ku) - Ku band corrected ocean altimeter range (range_ocean_20_ku)
- filtered altimeter ionospheric correction on Ku band (iono_cor_alt_filtered_01_ku)
- model dry tropospheric correction (mod_dry_tropo_cor_zero_altitude_01)
- radiometer wet tropospheric correction (rad_wet_tropo_cor_01_ku)
- sea state bias correction in Ku band (sea_state_bias_01_ku)
- solid earth tide height (solid_earth_tide_01)
- geocentric ocean tide height solution 2 = FES (ocean_tide_sol2_01)
- geocentric pole tide height (pole_tide_01)
- inverted barometer height correction (inv_bar_cor_01)
- high frequency fluctuations of the sea surface topography (hf_fluct_cor_01 for NTC/STC off line products only)
- mean sea surface (mean_sea_surf_sol2_20_ku)"
Parameters:
sral_ds (xarray.Dataset): The Sentinel-3 SRAL dataset.
Returns:
The Sentinel-3 SRAL dataset with the calculated sea surface height and sea surface height anomaly.
"""
#Before we can calculate the sea surface height anomaly (SSHA), we need to interpolate the 1Hz corrections to the 20Hz data
mod_dry_tropo_cor_zero_altitude_20_ku = sral_ds.mod_dry_tropo_cor_zero_altitude_01.interp(time_01=sral_ds.time_20_ku)
iono_cor_alt_filtered_20_ku = sral_ds.iono_cor_alt_filtered_01_ku.interp(time_01=sral_ds.time_20_ku)
rad_wet_tropo_cor_20_ku = sral_ds.rad_wet_tropo_cor_01_ku.interp(time_01=sral_ds.time_20_ku)
sea_state_bias_20_ku = sral_ds.sea_state_bias_01_ku.interp(time_01=sral_ds.time_20_ku)
solid_earth_tide_20_ku = sral_ds.solid_earth_tide_01.interp(time_01=sral_ds.time_20_ku)
ocean_tide_sol2_20_ku = sral_ds.ocean_tide_sol2_01.interp(time_01=sral_ds.time_20_ku)
pole_tide_20_ku = sral_ds.pole_tide_01.interp(time_01=sral_ds.time_20_ku)
inv_bar_cor_20_ku = sral_ds.inv_bar_cor_01.interp(time_01=sral_ds.time_20_ku)
hf_fluct_cor_20_ku = sral_ds.hf_fluct_cor_01.interp(time_01=sral_ds.time_20_ku)
# Calculate the sea surface height
ssh = sral_ds.alt_20_ku - sral_ds.range_water_20_ku - iono_cor_alt_filtered_20_ku - mod_dry_tropo_cor_zero_altitude_20_ku - rad_wet_tropo_cor_20_ku - sea_state_bias_20_ku - solid_earth_tide_20_ku - ocean_tide_sol2_20_ku - pole_tide_20_ku - inv_bar_cor_20_ku - hf_fluct_cor_20_ku
ssh.attrs = {
"long_name": "Sea Surface Height",
"units": "m",
"standard_name": "sea_surface_height_above_sea_leved",
"comment": "Calculated as SSH = altitude of satellite (alt_20_ku) - Ku band corrected ocean altimeter range (range_ocean_20_ku) - filtered altimeter ionospheric correction on Ku band (iono_cor_alt_filtered_01_ku) - model dry tropospheric correction (mod_dry_tropo_cor_zero_altitude_01) - radiometer wet tropospheric correction (rad_wet_tropo_cor_01_ku) - sea state bias correction in Ku band (sea_state_bias_01_ku) - solid earth tide height (solid_earth_tide_01) - geocentric ocean tide height solution 2 = FES (ocean_tide_sol2_01) - geocentric pole tide height (pole_tide_01) - inverted barometer height correction (inv_bar_cor_01) - high frequency fluctuations of the sea surface topography (hf_fluct_cor_01 for NTC/STC off line products only)"
}
sral_ds['ssh_20_ku'] = ssh
# Calculate the sea surface height anomaly
ssha = ssh - sral_ds.mean_sea_surf_sol2_20_ku
ssha.attrs = {
"long_name": "Sea Surface Height Anomaly",
"units": "m",
"standard_name": "sea_surface_height_above_sea_leved",
"comment": "Calculated as SSHA = ssha - mean sea surface (mean_sea_surf_sol2_20_ku)"
}
sral_ds['ssha_20_ku'] = ssha
return sral_ds
Let’s iterate through the Sentinel-3 SRAL data we downloaded in part 1 of this practical, load it, calculate the SSH and SSHA, and do some filtering to reduce the memory load
eddy_sral_dir = week8_path / 'S3_SRAL' / f'Eddy_num_{eddy_number}'
#load in the data
sral_filepaths = list(eddy_sral_dir.glob('**/*enhanced_measurement.nc'))
vars_of_interest = ['ssha_20_ku', 'ssh_20_ku', 'range_water_20_ku', 'alt_20_ku', 'sea_ice_ssha_20_ku', 'int_sea_ice_ssha_20_ku', 'mean_sea_surf_sol2_20_ku', 'sea_ice_sea_surf_20_ku', 'surf_type_class_20_ku', 'sea_ice_concentration_20_ku', 'freeboard_20_ku', 'dist_coast_20_ku']
sral_datasets = []
for sral_filepath in sral_filepaths:
ds = xr.open_dataset(sral_filepath)
ds = add_sea_surface_height_and_anomaly(ds)
ds = ds[vars_of_interest]
#crop to 50deg N
ds = ds.where(ds.lat_20_ku >= 50, drop=True)
#buffer by coast
ds = ds.where(ds.dist_coast_20_ku >= 25e3, drop=True)
sral_datasets.append(ds)
sral_ds = xr.concat(sral_datasets, dim='time_20_ku')
del sral_datasets, ds
Now we can define our training window for GPSat and ensure that the S3 data we have only covers the relevant dates. We will also filter out any obvious SSHA outliers (> 1m in amplitude)
training_window = pd.Timedelta(days=7)
#Crop the sral data to the date ± training window
sral_ds = sral_ds.where((sral_ds.time_20_ku.dt.date >= date - training_window) & (sral_ds.time_20_ku.dt.date <= date + training_window), drop=True)
#set to nan any sshas > 1m
sral_ds['ssha_20_ku'] = sral_ds['ssha_20_ku'].where(((sral_ds['ssha_20_ku'] <= 1) & (sral_ds['ssha_20_ku'] >= -1)), np.nan)
Plot some of the key variables relevant to this practical. Note that the dataset also includes sea_ice_ssha_20_ku and int_sea_ice_20_ku, which represent the sea surface height anomaly (SSHA) and interpolated SSHA over sea ice, respectively. These variables are derived using a specialised retracking algorithm for the sea ice environment. However, in this study, we have chosen to use the SAMOSA retracker and compute SSHA ourselves, as SAMOSA is targeted toward open ocean applications, likely providing more accurate sea level estimates in non-ice-covered regions.
#Plot each of the variables of interest in the general vicinity of the eddy
vars_to_plot = ['sea_ice_concentration_20_ku', 'mean_sea_surf_sol2_20_ku', 'ssh_20_ku', 'ssha_20_ku', 'sea_ice_ssha_20_ku', 'int_sea_ice_ssha_20_ku', 'sea_ice_sea_surf_20_ku']
%matplotlib inline
fig, axs = plt.subplots(3, 3, figsize=(18, 15), subplot_kw={'projection': ccrs.NorthPolarStereo()})
axs = axs.flatten()
eddy_proxy = mlines.Line2D([], [], color='black', linewidth=2, linestyle='--', alpha=0.8, label="Selected known eddy")
for i, var in enumerate(vars_to_plot):
ax = axs[i]
ax.set_extent([-15, -70, 55, 85], crs=ccrs.PlateCarree())
ax.coastlines()
ax.add_feature(cfeature.LAND, color='lightgray')
ax.gridlines(alpha=0.5)
if 'ssha' in var:
sm = ax.scatter(sral_ds['lon_20_ku'], sral_ds['lat_20_ku'], c=sral_ds[var], cmap='coolwarm', transform=ccrs.PlateCarree(), s=1, vmin=-0.5, vmax=0.5)
else:
sm = ax.scatter(sral_ds['lon_20_ku'], sral_ds['lat_20_ku'], c=sral_ds[var], cmap='jet', transform=ccrs.PlateCarree(), s=1)
cbar_label = sral_ds[var].long_name.split(': 20 Hz Ku band')[0]
if 'units' in sral_ds[var].attrs:
cbar_label += f" ({sral_ds[var].units})"
fig.colorbar(sm, ax=ax, label=cbar_label)
ax.set_title(var)
#add the polygon of the selected eddy
ax.add_geometries(
[selected_eddy.iloc[0]['geometry']], crs=ccrs.PlateCarree(),
facecolor="none", edgecolor="black", linewidth=1, linestyle='--', alpha=0.8,
)
ax.legend(handles=[eddy_proxy], loc='lower left')
#turn off any unused axes
for ax in axs[len(vars_to_plot):]:
ax.axis('off')
plt.tight_layout()
Optimally interpolate SSHA#
Format the dataset#
#convert sral_ds to a pandas dataframe
sral_df = (sral_ds
.to_dataframe()
.reset_index())
#To make it simpler to retieve this eddy without worrying about ice-floe-contaminated data, we will just take points where the sea ice concentration is zero.
#We are therefore negleting any SSH measurements coming from leads in the nearby icepack.
sral_df = sral_df[sral_df['sea_ice_concentration_20_ku'] == 0]
sral_df.head()
| time_20_ku | ssha_20_ku | ssh_20_ku | range_water_20_ku | alt_20_ku | sea_ice_ssha_20_ku | int_sea_ice_ssha_20_ku | mean_sea_surf_sol2_20_ku | sea_ice_sea_surf_20_ku | surf_type_class_20_ku | sea_ice_concentration_20_ku | freeboard_20_ku | dist_coast_20_ku | lat_20_ku | lon_20_ku | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-01-11 00:46:43.718905216 | -0.043255 | 15.422045 | 810295.0859 | 810308.3728 | NaN | NaN | 15.4653 | NaN | 0.0 | 0.0 | NaN | 129431.0 | 50.000394 | 307.913031 |
| 1 | 2019-01-11 00:46:43.768337920 | 0.027871 | 15.485271 | 810295.6626 | 810309.0124 | NaN | NaN | 15.4574 | NaN | 0.0 | 0.0 | NaN | 129564.0 | 50.003252 | 307.911763 |
| 2 | 2019-01-11 00:46:43.817770752 | -0.057003 | 15.392497 | 810296.3953 | 810309.6520 | NaN | NaN | 15.4495 | NaN | 0.0 | 0.0 | NaN | 129693.0 | 50.006110 | 307.910496 |
| 3 | 2019-01-11 00:46:43.867203456 | -0.063476 | 15.378124 | 810297.0496 | 810310.2916 | NaN | NaN | 15.4416 | NaN | 0.0 | 0.0 | NaN | 129820.0 | 50.008968 | 307.909228 |
| 4 | 2019-01-11 00:46:43.916623616 | -0.123850 | 15.309750 | 810297.7578 | 810310.9311 | NaN | NaN | 15.4336 | NaN | 0.0 | 0.0 | NaN | 129944.0 | 50.011825 | 307.907961 |
# Format the dataframe to the convention used for the GPSat package
sral_df['t'] = sral_df['time_20_ku'].values.astype("datetime64[D]").astype(float)
sral_df['x'], sral_df['y'] = WGS84toEASE2_New(sral_df['lon_20_ku'].values, sral_df['lat_20_ku'].values, lat_0=90)
<ipython-input-14-5bb43984339a>:3: DeprecationWarning: Call to deprecated function (or staticmethod) WGS84toEASE2_New. (This function will be removed in future versions. Use `WGS84toEASE2` instead.)
sral_df['x'], sral_df['y'] = WGS84toEASE2_New(sral_df['lon_20_ku'].values, sral_df['lat_20_ku'].values, lat_0=90)
Define a region of interest#
# We need to define some grids, one for our expert locations and one for our prediction locations
# To help us create a grid in the relevant location and coordinate system, we'll start by making a buffer zone around the eddy and obtain its bounds
buffer = 200e3 #m
buffered_eddy = selected_eddy['geometry'].to_crs('EPSG:6931').iloc[0].buffer(buffer) #note that we must change the polygon crs to get a buffer in metres
minx, miny, maxx, maxy = buffered_eddy.bounds
selected_eddy = selected_eddy.to_crs('EPSG:4326')
#plot the eddy, buffer and the bounds
fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={'projection': ccrs.NorthPolarStereo()})
ax.set_extent([-25, -90, 60, 80], crs=ccrs.PlateCarree())
ax.add_geometries([selected_eddy.iloc[0].geometry], crs=ccrs.PlateCarree(), facecolor='red', edgecolor='red')
ax.add_geometries([buffered_eddy], crs=ccrs.epsg(6931), facecolor='none', edgecolor='red', linewidth=2, linestyle='--', alpha=0.8)
buffer_boundary = ax.plot([minx, maxx, maxx, minx, minx], [miny, miny, maxy, maxy, miny], transform=ccrs.epsg(6931), color='black', linestyle='--', linewidth=2, label='Grid bounds')
ax.coastlines()
ax.gridlines(alpha=0.5)
eddy_proxy = mlines.Line2D([], [], color='red', linewidth=4, alpha=0.8, label="Selected known eddy")
buffer_proxy = mlines.Line2D([], [], color='red', linewidth=2, linestyle='--', alpha=0.8, label=f"{int(buffer/1e3)} km Buffer zone")
ax.legend(handles=[eddy_proxy, buffer_proxy, buffer_boundary[0]], loc='lower left')
plt.show()
Define local experts#
#Make a grid of expert locations using the bounds
expert_grid_res = 25e3
grid_xcs = np.arange(minx, maxx+expert_grid_res/2, expert_grid_res) #remember that the start is included and the stop is not
grid_ycs = np.arange(miny, maxy+expert_grid_res/2, expert_grid_res)
grid_xcs_2d, grid_ycs_2d = np.meshgrid(grid_xcs, grid_ycs)
expert_locs = pd.DataFrame({'x': grid_xcs_2d.flatten(), 'y': grid_ycs_2d.flatten()})
# Add lon and lat
expert_locs['lon'], expert_locs['lat'] = EASE2toWGS84_New(expert_locs['x'], expert_locs['y'], lat_0=90)
expert_locs['t'] = np.floor(sral_df['t'].mean())
# Identify if a position is in the ocean (water) or not
expert_locs["is_in_ocean"] = globe.is_ocean(expert_locs['lat'], expert_locs['lon'])
# keep only prediction locations in ocean
expert_locs = expert_locs.loc[expert_locs["is_in_ocean"]]
print(f'Number of expert locations: {len(expert_locs)}')
print("Local expert locations:")
expert_locs.head()
Number of expert locations: 495
Local expert locations:
<ipython-input-16-4486cd431d78>:8: DeprecationWarning: Call to deprecated function (or staticmethod) EASE2toWGS84_New. (This function will be removed in future versions. Use `EASE2toWGS84` instead.)
expert_locs['lon'], expert_locs['lat'] = EASE2toWGS84_New(expert_locs['x'], expert_locs['y'], lat_0=90)
| x | y | lon | lat | t | is_in_ocean | |
|---|---|---|---|---|---|---|
| 0 | -2.867486e+06 | -2.111906e+06 | -53.628321 | 57.656521 | 17913.0 | True |
| 1 | -2.842486e+06 | -2.111906e+06 | -53.388445 | 57.844369 | 17913.0 | True |
| 2 | -2.817486e+06 | -2.111906e+06 | -53.145834 | 58.031535 | 17913.0 | True |
| 3 | -2.792486e+06 | -2.111906e+06 | -52.900452 | 58.218013 | 17913.0 | True |
| 4 | -2.767486e+06 | -2.111906e+06 | -52.652258 | 58.403793 | 17913.0 | True |
# Plot the eddy, buffer and the bounds
fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={'projection': ccrs.NorthPolarStereo()})
ax.set_extent([-40, -70, 55, 70], crs=ccrs.PlateCarree())
ax.add_geometries(selected_eddy.geometry, crs=ccrs.PlateCarree(), facecolor='red', edgecolor='red', alpha=0.4)
ax.add_geometries([buffered_eddy], crs=ccrs.epsg(6931), facecolor='none', edgecolor='red', linewidth=2, linestyle='--', alpha=0.8)
elocs = ax.scatter(expert_locs['x'], expert_locs['y'], transform=ccrs.epsg(6931), color='black', s=10, label='Expert locations')
ax.coastlines()
ax.gridlines(alpha=0.5)
eddy_proxy = mlines.Line2D([], [], color='red', linewidth=4, alpha=0.8, label="Selected known eddy")
buffer_proxy = mlines.Line2D([], [], color='red', linewidth=2, linestyle='--', alpha=0.8, label=f"{int(buffer/1e3)} km Buffer zone")
ax.legend(handles=[eddy_proxy, buffer_proxy, elocs], loc='lower left')
plt.show()
Define prediction locations#
#Define a grid for the prediction locations
pred_grid_res = 5e3
pred_grid_xcs = np.arange(minx+pred_grid_res, maxx+pred_grid_res/2, pred_grid_res) #remember that the start is included and the stop is not
pred_grid_ycs = np.arange(miny+pred_grid_res, maxy+pred_grid_res/2, pred_grid_res)
pred_grid_xcs_2d, pred_grid_ycs_2d = np.meshgrid(pred_grid_xcs, pred_grid_ycs)
pred_locs = pd.DataFrame({'x': pred_grid_xcs_2d.flatten(), 'y': pred_grid_ycs_2d.flatten()})
# Add lon and lat columns
pred_locs['lon'], pred_locs['lat'] = EASE2toWGS84_New(pred_locs['x'].values, pred_locs['y'].values, lat_0=90)
<ipython-input-18-3b9a63d0d6f8>:8: DeprecationWarning: Call to deprecated function (or staticmethod) EASE2toWGS84_New. (This function will be removed in future versions. Use `EASE2toWGS84` instead.)
pred_locs['lon'], pred_locs['lat'] = EASE2toWGS84_New(pred_locs['x'].values, pred_locs['y'].values, lat_0=90)
Bin observations (to reduce GPSat runtime)#
To reduce the number of input data points and ultimately speed up getting some results, we will bin our observations. We will perform drop-in-a-bucket resampling to do this, whereby we average the data that falls within each bin (grid cell)
#Let us use the grid coordinates that we defined earlier for our prediction locations and use a KDTree to help bin the S3 data within each grid cell
gridded_obvs_dfs = []
for date_val, date_df in sral_df.groupby('t'):
date_df = date_df.copy()
gridded_observations = pd.DataFrame({'x': pred_grid_xcs_2d.flatten(), 'y': pred_grid_ycs_2d.flatten()})
#get the nearest grid cell to each observation
tree = KDTree(np.vstack([gridded_observations['x'].values, gridded_observations['y'].values]).T)
distances, indices = tree.query(np.vstack([date_df['x'].values, date_df['y'].values]).T, k=1)
#nan where dist > max dist
max_dist = 2.5e3
indices[distances > max_dist] = -9999
#get the mean of the observations in each grid cell
date_df['nearest_grid_cell_index'] = indices
date_df = date_df.groupby('nearest_grid_cell_index').mean()
#drop x and y from the averaged date_df as we don't want the mean coords for the grid cell data- we will obtain the actual grid cell x and y when we merge the two datasets
date_df = date_df.drop(columns=['x', 'y'])
#merge the binned date_df with the gridded_observations dataframe
gridded_observations=gridded_observations.merge(date_df, left_index=True, right_index=True, how='left')
#drop rows with nan ssha values
ssha_var = 'ssha_20_ku'
# ssha_var = 'sea_ice_ssha_20_ku'
gridded_observations = gridded_observations.dropna(subset=[ssha_var])
#add the t column
gridded_observations['t'] = date_val
gridded_obvs_dfs.append(gridded_observations)
del date_df, gridded_observations, tree, distances, indices
gridded_obvs_df = pd.concat(gridded_obvs_dfs)
gridded_obvs_df = gridded_obvs_df.reset_index(drop=True)
del gridded_obvs_dfs
gridded_obvs_df.head()
print(f'We have reduced the number of observations from {len(sral_df)} to {len(gridded_obvs_df)}')
<ipython-input-19-5922f35496c4>:26: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
gridded_observations['t'] = date_val
<ipython-input-19-5922f35496c4>:26: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
gridded_observations['t'] = date_val
We have reduced the number of observations from 153030 to 2466
sral_df = sral_df.dropna(subset=['x', 'y', 't','ssha_20_ku']).reset_index(drop=True)
print(sral_df.shape)
Set GPSat configuration parameters#
# Set training and inference radius
training_radius = 100_000
inference_radius = 50_000
# Local expert locations config
local_expert = ExpertLocsConfig(source = expert_locs)
# Model config
model = ModelConfig(oi_model = "GPflowSGPRModel", # Use GPflow SGPR model
# model = ModelConfig(oi_model = "GPflowGPRModel", # Use GPflow GPR model
init_params = {
# normalise xy coordinates
"coords_scale": [10_000, 10_000, 1],
'num_inducing_points':100
},
constraints = {
# set bounds on the lengthscale hyperparameters
"lengthscales": {
"low": [1e-08, 1e-08, 1e-08],
"high": [600_000, 600_000, 9]
}
}
)
# Data config
data = DataConfig(data_source = sral_df,
obs_col = ssha_var,
coords_col = ["x", "y", "t"],
local_select = [
# Select data within the training window and radius
{"col": "t", "comp": "<=", "val": training_window.days},
{"col": "t", "comp": ">=", "val": -training_window.days},
{"col": ["x", "y"], "comp": "<", "val": training_radius}
]
)
# Prediction locs config
pred_loc = PredictionLocsConfig(method = "from_dataframe",
df = pred_locs,
max_dist = inference_radius)
locexp = LocalExpertOI(expert_loc_config = local_expert,
data_config = data,
model_config = model,
pred_loc_config = pred_loc)
in json_serializable - key: 'source' has value DataFrame/Series, but is too long: 495 > 100
storing as str
'data_select': 0.001 seconds
'load': 0.001 seconds
in json_serializable - key: 'data_source' has value DataFrame/Series, but is too long: 2466 > 100
storing as str
in json_serializable - key: 'df' has value DataFrame/Series, but is too long: 11766 > 100
storing as str
Run GPSat#
# path to store results
store_path = week8_path / 'OI_results' / f'Eddy_num_{eddy_number}_{date.strftime("%Y-%m-%d")}_{ssha_var}.h5'
if store_path.parent.exists() == False:
store_path.parent.mkdir(parents=True)
# for the purposes of a simple example, if store_path exists: delete it
if os.path.exists(store_path):
cprint(f"removing: {store_path}")
os.remove(store_path)
# run optimal interpolation
locexp.run(store_path=str(store_path))
Streaming output truncated to the last 5000 lines.
'optimise_parameters': 0.873 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([13.76382547, 4.58616132, 2.48405714])
kernel_variance: 0.005845132444405152
likelihood_variance: 0.0011317213965883514
'predict': 0.099 seconds
total run time : 2.57 seconds
--------------------------------------------------
238 / 495
current local expert:
x y lon lat t is_in_ocean
248 -2.717486e+06 -1.836906e+06 -55.942964 60.276206 17913.0 True
'local_data_select': 0.003 seconds
number obs: 280
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.955 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.25852907e+01, 4.32898608e+00, 2.34274228e-02])
kernel_variance: 0.004000643813710293
likelihood_variance: 0.0011587707621332902
'predict': 0.098 seconds
total run time : 2.60 seconds
--------------------------------------------------
239 / 495
current local expert:
x y lon lat t is_in_ocean
249 -2.692486e+06 -1.836906e+06 -55.696855 60.468209 17913.0 True
'local_data_select': 0.003 seconds
number obs: 281
setting lengthscales to: [1. 1. 1.]
'__init__': 0.040 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.925 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([12.7624226 , 5.3791735 , 4.87395617])
kernel_variance: 0.00694313105735306
likelihood_variance: 0.001251755714946632
'predict': 0.116 seconds
total run time : 2.60 seconds
--------------------------------------------------
240 / 495
current local expert:
x y lon lat t is_in_ocean
250 -2.667486e+06 -1.836906e+06 -55.447607 60.659555 17913.0 True
'local_data_select': 0.003 seconds
number obs: 296
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.144 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([12.77255161, 3.31092672, 0.6485549 ])
kernel_variance: 0.006510979060383519
likelihood_variance: 0.0012740344259374607
'predict': 0.092 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.40 seconds
--------------------------------------------------
241 / 495
current local expert:
x y lon lat t is_in_ocean
251 -2.642486e+06 -1.836906e+06 -55.19517 60.850238 17913.0 True
'local_data_select': 0.003 seconds
number obs: 284
setting lengthscales to: [1. 1. 1.]
'__init__': 0.051 seconds
'set_lengthscales_constraints': 0.014 seconds
**********
optimization failed!
'optimise_parameters': 1.344 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([5.81885165, 4.09410699, 2.37241612])
kernel_variance: 0.006127837770309026
likelihood_variance: 0.0014140802424044917
'predict': 0.101 seconds
total run time : 2.90 seconds
--------------------------------------------------
242 / 495
current local expert:
x y lon lat t is_in_ocean
252 -2.617486e+06 -1.836906e+06 -54.939492 61.040246 17913.0 True
'local_data_select': 0.004 seconds
number obs: 299
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.164 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([5.95845561, 4.16684444, 2.62790248])
kernel_variance: 0.0061130123494537334
likelihood_variance: 0.0013497624719111083
'predict': 0.094 seconds
total run time : 2.58 seconds
--------------------------------------------------
243 / 495
current local expert:
x y lon lat t is_in_ocean
253 -2.592486e+06 -1.836906e+06 -54.680521 61.22957 17913.0 True
'local_data_select': 0.003 seconds
number obs: 292
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.231 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([5.82901106, 4.18544737, 2.9550731 ])
kernel_variance: 0.005745730359837202
likelihood_variance: 0.0011706551740099326
'predict': 0.087 seconds
total run time : 2.76 seconds
--------------------------------------------------
244 / 495
current local expert:
x y lon lat t is_in_ocean
254 -2.567486e+06 -1.836906e+06 -54.418202 61.418199 17913.0 True
'local_data_select': 0.003 seconds
number obs: 285
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.722 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([5.69522244, 4.91245144, 3.38087263])
kernel_variance: 0.008499369063858883
likelihood_variance: 0.0011913934411117433
'predict': 0.098 seconds
total run time : 2.41 seconds
--------------------------------------------------
245 / 495
current local expert:
x y lon lat t is_in_ocean
255 -2.542486e+06 -1.836906e+06 -54.152483 61.606124 17913.0 True
'local_data_select': 0.005 seconds
number obs: 281
setting lengthscales to: [1. 1. 1.]
'__init__': 0.046 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.703 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([5.37152079, 5.29319172, 3.76168173])
kernel_variance: 0.009996320875382504
likelihood_variance: 0.0011241927692164433
'predict': 0.103 seconds
total run time : 3.16 seconds
--------------------------------------------------
246 / 495
current local expert:
x y lon lat t is_in_ocean
256 -2.517486e+06 -1.836906e+06 -53.883309 61.793334 17913.0 True
'local_data_select': 0.002 seconds
number obs: 277
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.698 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([4.33861579, 4.15491773, 2.31139847])
kernel_variance: 0.008261058738877673
likelihood_variance: 0.0007806015469124933
'predict': 0.094 seconds
total run time : 2.27 seconds
--------------------------------------------------
247 / 495
current local expert:
x y lon lat t is_in_ocean
257 -2.492486e+06 -1.836906e+06 -53.610623 61.979817 17913.0 True
'local_data_select': 0.003 seconds
number obs: 262
setting lengthscales to: [1. 1. 1.]
'__init__': 0.044 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.707 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([4.18269425, 5.23365108, 1.76618707])
kernel_variance: 0.008670670309869843
likelihood_variance: 0.0008425948371160795
'predict': 0.085 seconds
total run time : 2.25 seconds
--------------------------------------------------
248 / 495
current local expert:
x y lon lat t is_in_ocean
258 -2.467486e+06 -1.836906e+06 -53.334369 62.165563 17913.0 True
'local_data_select': 0.003 seconds
number obs: 277
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 1.336 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.92500268, 4.11939316, 1.22213886])
kernel_variance: 0.006695390012592385
likelihood_variance: 0.000781408406574078
'predict': 0.088 seconds
total run time : 2.79 seconds
--------------------------------------------------
249 / 495
current local expert:
x y lon lat t is_in_ocean
259 -2.442486e+06 -1.836906e+06 -53.054489 62.350559 17913.0 True
'local_data_select': 0.004 seconds
number obs: 264
setting lengthscales to: [1. 1. 1.]
'__init__': 0.049 seconds
'set_lengthscales_constraints': 0.013 seconds
**********
optimization failed!
'optimise_parameters': 1.218 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([3.8403921 , 9.10934295, 2.24104515])
kernel_variance: 0.007112791252278244
likelihood_variance: 0.0007194869484516705
'predict': 0.156 seconds
total run time : 3.07 seconds
--------------------------------------------------
250 / 495
current local expert:
x y lon lat t is_in_ocean
260 -2.417486e+06 -1.836906e+06 -52.770926 62.534795 17913.0 True
'local_data_select': 0.003 seconds
number obs: 259
setting lengthscales to: [1. 1. 1.]
'__init__': 0.048 seconds
'set_lengthscales_constraints': 0.019 seconds
'optimise_parameters': 0.747 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([4.77700261, 3.46588253, 0.780786 ])
kernel_variance: 0.007291934234529429
likelihood_variance: 0.0005784888283546045
'predict': 0.085 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 2.81 seconds
--------------------------------------------------
251 / 495
current local expert:
x y lon lat t is_in_ocean
261 -2.392486e+06 -1.836906e+06 -52.483621 62.718259 17913.0 True
'local_data_select': 0.002 seconds
number obs: 215
setting lengthscales to: [1. 1. 1.]
'__init__': 0.031 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.807 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.55090059, 3.04597655, 0.69124611])
kernel_variance: 0.0068094454105605395
likelihood_variance: 0.0005962115635424414
'predict': 0.086 seconds
total run time : 2.30 seconds
--------------------------------------------------
252 / 495
current local expert:
x y lon lat t is_in_ocean
262 -2.367486e+06 -1.836906e+06 -52.192512 62.900937 17913.0 True
'local_data_select': 0.006 seconds
number obs: 182
setting lengthscales to: [1. 1. 1.]
'__init__': 0.043 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.975 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([6.57283981, 3.3008146 , 0.19983537])
kernel_variance: 0.004859920315037176
likelihood_variance: 0.0006088722005913923
'predict': 0.077 seconds
total run time : 2.56 seconds
--------------------------------------------------
253 / 495
current local expert:
x y lon lat t is_in_ocean
263 -2.342486e+06 -1.836906e+06 -51.897541 63.082817 17913.0 True
'local_data_select': 0.002 seconds
number obs: 151
setting lengthscales to: [1. 1. 1.]
'__init__': 0.040 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.688 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([5.84297325, 3.50647205, 1.01113451])
kernel_variance: 0.003582635851146411
likelihood_variance: 0.000673680910645918
'predict': 0.085 seconds
total run time : 2.11 seconds
--------------------------------------------------
254 / 495
current local expert:
x y lon lat t is_in_ocean
264 -2.867486e+06 -1.811906e+06 -57.712042 59.23589 17913.0 True
'local_data_select': 0.002 seconds
number obs: 143
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 0.640 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([ 3.02846559, 16.85485619, 2.17409861])
kernel_variance: 0.0030464463904889888
likelihood_variance: 0.0008317944407339647
'predict': 0.099 seconds
total run time : 2.59 seconds
--------------------------------------------------
255 / 495
current local expert:
x y lon lat t is_in_ocean
265 -2.842486e+06 -1.811906e+06 -57.485053 59.432356 17913.0 True
'local_data_select': 0.002 seconds
number obs: 185
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.749 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([ 3.0020926 , 32.7446911 , 2.25905199])
kernel_variance: 0.0030966669112262608
likelihood_variance: 0.0008843577131855701
'predict': 0.081 seconds
total run time : 2.35 seconds
--------------------------------------------------
256 / 495
current local expert:
x y lon lat t is_in_ocean
266 -2.817486e+06 -1.811906e+06 -57.255206 59.628226 17913.0 True
'local_data_select': 0.003 seconds
number obs: 224
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.643 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 2.99221583, 13.50052311, 2.23914293])
kernel_variance: 0.0027622088921785827
likelihood_variance: 0.0009473062183206426
'predict': 0.095 seconds
total run time : 2.19 seconds
--------------------------------------------------
257 / 495
current local expert:
x y lon lat t is_in_ocean
267 -2.792486e+06 -1.811906e+06 -57.022456 59.823491 17913.0 True
'local_data_select': 0.003 seconds
number obs: 254
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 0.830 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.15485666, 9.0547887 , 2.1491698 ])
kernel_variance: 0.002457903455683435
likelihood_variance: 0.0009458609474450805
'predict': 0.090 seconds
total run time : 2.33 seconds
--------------------------------------------------
258 / 495
current local expert:
x y lon lat t is_in_ocean
268 -2.767486e+06 -1.811906e+06 -56.786754 60.018145 17913.0 True
'local_data_select': 0.002 seconds
number obs: 275
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.793 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.2018981 , 7.82325451, 2.36094307])
kernel_variance: 0.0023942193477736666
likelihood_variance: 0.0009558185882309156
'predict': 0.108 seconds
total run time : 2.50 seconds
--------------------------------------------------
259 / 495
current local expert:
x y lon lat t is_in_ocean
269 -2.742486e+06 -1.811906e+06 -56.548053 60.212177 17913.0 True
'local_data_select': 0.004 seconds
number obs: 281
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 2.179 seconds
'get_parameters': 0.013 seconds
parameters:
lengthscales: array([29.85487953, 5.16055902, 0.23011034])
kernel_variance: 0.004910699608767565
likelihood_variance: 0.001109339486939771
'predict': 0.170 seconds
total run time : 4.00 seconds
--------------------------------------------------
260 / 495
current local expert:
x y lon lat t is_in_ocean
270 -2.717486e+06 -1.811906e+06 -56.306303 60.405581 17913.0 True
'local_data_select': 0.002 seconds
number obs: 289
setting lengthscales to: [1. 1. 1.]
'__init__': 0.030 seconds
'set_lengthscales_constraints': 0.007 seconds
**********
optimization failed!
'optimise_parameters': 1.514 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([59.94842321, 4.01963229, 0.48946768])
kernel_variance: 0.004661775367753582
likelihood_variance: 0.001179511179030453
'predict': 0.105 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.87 seconds
--------------------------------------------------
261 / 495
current local expert:
x y lon lat t is_in_ocean
271 -2.692486e+06 -1.811906e+06 -56.061454 60.598346 17913.0 True
'local_data_select': 0.003 seconds
number obs: 291
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.917 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([13.41908863, 4.35653664, 4.20772848])
kernel_variance: 0.006812850948503782
likelihood_variance: 0.001198925679053315
'predict': 0.100 seconds
total run time : 2.48 seconds
--------------------------------------------------
262 / 495
current local expert:
x y lon lat t is_in_ocean
272 -2.667486e+06 -1.811906e+06 -55.813456 60.790464 17913.0 True
'local_data_select': 0.003 seconds
number obs: 304
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.010 seconds
**********
optimization failed!
'optimise_parameters': 1.331 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([14.39470681, 3.89837213, 0.97584278])
kernel_variance: 0.006966540678509781
likelihood_variance: 0.0012067543498700433
'predict': 0.101 seconds
total run time : 3.02 seconds
--------------------------------------------------
263 / 495
current local expert:
x y lon lat t is_in_ocean
273 -2.642486e+06 -1.811906e+06 -55.562256 60.981925 17913.0 True
'local_data_select': 0.003 seconds
number obs: 307
setting lengthscales to: [1. 1. 1.]
'__init__': 0.043 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.625 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([5.83179092, 3.56428054, 1.64684554])
kernel_variance: 0.005503340627232728
likelihood_variance: 0.0012406753806456323
'predict': 0.151 seconds
total run time : 3.35 seconds
--------------------------------------------------
264 / 495
current local expert:
x y lon lat t is_in_ocean
274 -2.617486e+06 -1.811906e+06 -55.307802 61.17272 17913.0 True
'local_data_select': 0.002 seconds
number obs: 293
setting lengthscales to: [1. 1. 1.]
'__init__': 0.031 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.158 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([6.29907791, 3.46952723, 1.63171377])
kernel_variance: 0.006167537383759893
likelihood_variance: 0.0012501371082705907
'predict': 0.095 seconds
total run time : 2.77 seconds
--------------------------------------------------
265 / 495
current local expert:
x y lon lat t is_in_ocean
275 -2.592486e+06 -1.811906e+06 -55.05004 61.36284 17913.0 True
'local_data_select': 0.003 seconds
number obs: 295
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.744 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([6.107704 , 3.72046365, 2.6912274 ])
kernel_variance: 0.0057693565912792675
likelihood_variance: 0.0012884931042343802
'predict': 0.107 seconds
total run time : 2.53 seconds
--------------------------------------------------
266 / 495
current local expert:
x y lon lat t is_in_ocean
276 -2.567486e+06 -1.811906e+06 -54.788918 61.552273 17913.0 True
'local_data_select': 0.003 seconds
number obs: 280
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.070 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([5.90113063, 3.60097746, 2.78194529])
kernel_variance: 0.0057171110682977975
likelihood_variance: 0.001224049684489443
'predict': 0.103 seconds
total run time : 2.70 seconds
--------------------------------------------------
267 / 495
current local expert:
x y lon lat t is_in_ocean
277 -2.542486e+06 -1.811906e+06 -54.524378 61.74101 17913.0 True
'local_data_select': 0.003 seconds
number obs: 278
setting lengthscales to: [1. 1. 1.]
'__init__': 0.048 seconds
'set_lengthscales_constraints': 0.015 seconds
'optimise_parameters': 1.141 seconds
'get_parameters': 0.017 seconds
parameters:
lengthscales: array([5.04140809, 3.67264616, 2.998944 ])
kernel_variance: 0.0059695383284014405
likelihood_variance: 0.0012074445643739198
'predict': 0.199 seconds
total run time : 3.06 seconds
--------------------------------------------------
268 / 495
current local expert:
x y lon lat t is_in_ocean
278 -2.517486e+06 -1.811906e+06 -54.256366 61.929039 17913.0 True
'local_data_select': 0.002 seconds
number obs: 275
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.880 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([4.75023413, 3.75832946, 1.77185101])
kernel_variance: 0.008063671918046337
likelihood_variance: 0.0008595271184243593
'predict': 0.097 seconds
total run time : 2.45 seconds
--------------------------------------------------
269 / 495
current local expert:
x y lon lat t is_in_ocean
279 -2.492486e+06 -1.811906e+06 -53.984824 62.116351 17913.0 True
'local_data_select': 0.004 seconds
number obs: 271
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.013 seconds
**********
optimization failed!
'optimise_parameters': 1.045 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([4.34052503, 5.34029732, 2.07798267])
kernel_variance: 0.007901671232565475
likelihood_variance: 0.0007779091290957549
'predict': 0.100 seconds
total run time : 2.65 seconds
--------------------------------------------------
270 / 495
current local expert:
x y lon lat t is_in_ocean
280 -2.467486e+06 -1.811906e+06 -53.709694 62.302934 17913.0 True
'local_data_select': 0.003 seconds
number obs: 275
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.056 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([4.74971126, 3.84506612, 1.30852445])
kernel_variance: 0.00701707043625777
likelihood_variance: 0.0006696211142277939
'predict': 0.099 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.18 seconds
--------------------------------------------------
271 / 495
current local expert:
x y lon lat t is_in_ocean
281 -2.442486e+06 -1.811906e+06 -53.430919 62.488776 17913.0 True
'local_data_select': 0.003 seconds
number obs: 282
setting lengthscales to: [1. 1. 1.]
'__init__': 0.052 seconds
'set_lengthscales_constraints': 0.014 seconds
**********
optimization failed!
'optimise_parameters': 1.577 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([5.18590506, 3.04082895, 0.86404899])
kernel_variance: 0.006676975319891275
likelihood_variance: 0.0005604210409023996
'predict': 0.144 seconds
total run time : 3.61 seconds
--------------------------------------------------
272 / 495
current local expert:
x y lon lat t is_in_ocean
282 -2.417486e+06 -1.811906e+06 -53.148438 62.673866 17913.0 True
'local_data_select': 0.003 seconds
number obs: 273
setting lengthscales to: [1. 1. 1.]
'__init__': 0.031 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.191 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.82691388, 3.89930152, 0.66706903])
kernel_variance: 0.00630116835327452
likelihood_variance: 0.0006812547329871863
'predict': 0.105 seconds
total run time : 2.86 seconds
--------------------------------------------------
273 / 495
current local expert:
x y lon lat t is_in_ocean
283 -2.392486e+06 -1.811906e+06 -52.862191 62.858192 17913.0 True
'local_data_select': 0.002 seconds
number obs: 241
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.005 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([4.96481738, 2.9399111 , 0.13891374])
kernel_variance: 0.0042882020020421205
likelihood_variance: 0.0006700772553482606
'predict': 0.100 seconds
total run time : 2.77 seconds
--------------------------------------------------
274 / 495
current local expert:
x y lon lat t is_in_ocean
284 -2.367486e+06 -1.811906e+06 -52.572118 63.04174 17913.0 True
'local_data_select': 0.005 seconds
number obs: 190
setting lengthscales to: [1. 1. 1.]
'__init__': 0.081 seconds
'set_lengthscales_constraints': 0.018 seconds
**********
optimization failed!
'optimise_parameters': 1.613 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([5.65108283, 3.55660141, 2.02689099])
kernel_variance: 0.004012804801165899
likelihood_variance: 0.0006086919517447448
'predict': 0.085 seconds
total run time : 3.35 seconds
--------------------------------------------------
275 / 495
current local expert:
x y lon lat t is_in_ocean
285 -2.342486e+06 -1.811906e+06 -52.278155 63.2245 17913.0 True
'local_data_select': 0.003 seconds
number obs: 158
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.049 seconds
'get_parameters': 0.012 seconds
parameters:
lengthscales: array([5.42091706, 4.14625538, 2.32901212])
kernel_variance: 0.0038865421948613343
likelihood_variance: 0.0006678426684086762
'predict': 0.120 seconds
total run time : 2.94 seconds
--------------------------------------------------
276 / 495
current local expert:
x y lon lat t is_in_ocean
286 -2.867486e+06 -1.786906e+06 -58.070441 59.359584 17913.0 True
'local_data_select': 0.002 seconds
number obs: 135
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.518 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([ 2.67471926, 30.22175164, 1.21511105])
kernel_variance: 0.0022135440776126884
likelihood_variance: 0.0009517009938941202
'predict': 0.097 seconds
total run time : 2.08 seconds
--------------------------------------------------
277 / 495
current local expert:
x y lon lat t is_in_ocean
287 -2.842486e+06 -1.786906e+06 -57.844808 59.556766 17913.0 True
'local_data_select': 0.002 seconds
number obs: 188
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.007 seconds
**********
optimization failed!
'optimise_parameters': 0.721 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.86706388, 8.35453324, 1.53885883])
kernel_variance: 0.002075881404187328
likelihood_variance: 0.0009542444270691363
'predict': 0.081 seconds
total run time : 2.30 seconds
--------------------------------------------------
278 / 495
current local expert:
x y lon lat t is_in_ocean
288 -2.817486e+06 -1.786906e+06 -57.616312 59.753359 17913.0 True
'local_data_select': 0.004 seconds
number obs: 228
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.824 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([2.79096696, 7.15490067, 1.79799383])
kernel_variance: 0.0021262405883632225
likelihood_variance: 0.0009200332433553799
'predict': 0.082 seconds
total run time : 2.47 seconds
--------------------------------------------------
279 / 495
current local expert:
x y lon lat t is_in_ocean
289 -2.792486e+06 -1.786906e+06 -57.384906 59.949355 17913.0 True
'local_data_select': 0.002 seconds
number obs: 275
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.857 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([3.11650116, 8.84178397, 1.72541568])
kernel_variance: 0.0019602712804146593
likelihood_variance: 0.000907835595211456
'predict': 0.092 seconds
total run time : 2.62 seconds
--------------------------------------------------
280 / 495
current local expert:
x y lon lat t is_in_ocean
290 -2.767486e+06 -1.786906e+06 -57.150542 60.144747 17913.0 True
'local_data_select': 0.004 seconds
number obs: 291
setting lengthscales to: [1. 1. 1.]
'__init__': 0.071 seconds
'set_lengthscales_constraints': 0.019 seconds
'optimise_parameters': 1.289 seconds
'get_parameters': 0.015 seconds
parameters:
lengthscales: array([3.90180778, 8.55663212, 1.97257555])
kernel_variance: 0.00198917509988964
likelihood_variance: 0.0009135936150139689
'predict': 0.137 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.56 seconds
--------------------------------------------------
281 / 495
current local expert:
x y lon lat t is_in_ocean
291 -2.742486e+06 -1.786906e+06 -56.91317 60.339527 17913.0 True
'local_data_select': 0.002 seconds
number obs: 285
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.450 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([37.99718551, 12.04810674, 0.53922511])
kernel_variance: 0.003611901897642483
likelihood_variance: 0.0011450240416457694
'predict': 0.093 seconds
total run time : 3.07 seconds
--------------------------------------------------
282 / 495
current local expert:
x y lon lat t is_in_ocean
292 -2.717486e+06 -1.786906e+06 -56.672741 60.533685 17913.0 True
'local_data_select': 0.002 seconds
number obs: 286
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.912 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([24.83655197, 3.69710583, 0.45223854])
kernel_variance: 0.004802027887747402
likelihood_variance: 0.001064217565391327
'predict': 0.097 seconds
total run time : 2.59 seconds
--------------------------------------------------
283 / 495
current local expert:
x y lon lat t is_in_ocean
293 -2.692486e+06 -1.786906e+06 -56.429203 60.727213 17913.0 True
'local_data_select': 0.002 seconds
number obs: 291
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.427 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([59.99858071, 4.14748484, 0.4772678 ])
kernel_variance: 0.005097644463046522
likelihood_variance: 0.0012295166840178766
'predict': 0.101 seconds
total run time : 3.14 seconds
--------------------------------------------------
284 / 495
current local expert:
x y lon lat t is_in_ocean
294 -2.667486e+06 -1.786906e+06 -56.182505 60.920101 17913.0 True
'local_data_select': 0.005 seconds
number obs: 280
setting lengthscales to: [1. 1. 1.]
'__init__': 0.062 seconds
'set_lengthscales_constraints': 0.015 seconds
**********
optimization failed!
'optimise_parameters': 2.113 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([14.72524406, 4.26813942, 1.61221108])
kernel_variance: 0.006607172383044926
likelihood_variance: 0.0012312858653679489
'predict': 0.100 seconds
total run time : 3.84 seconds
--------------------------------------------------
285 / 495
current local expert:
x y lon lat t is_in_ocean
295 -2.642486e+06 -1.786906e+06 -55.932595 61.112342 17913.0 True
'local_data_select': 0.003 seconds
number obs: 299
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.765 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([6.64091036, 4.38575733, 2.7094299 ])
kernel_variance: 0.006124275158888001
likelihood_variance: 0.0013008290967091484
'predict': 0.121 seconds
total run time : 2.51 seconds
--------------------------------------------------
286 / 495
current local expert:
x y lon lat t is_in_ocean
296 -2.617486e+06 -1.786906e+06 -55.679419 61.303924 17913.0 True
'local_data_select': 0.002 seconds
number obs: 290
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.007 seconds
**********
optimization failed!
'optimise_parameters': 1.406 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([7.63481215, 3.19247193, 1.74131632])
kernel_variance: 0.005845875373073694
likelihood_variance: 0.0012452416345192603
'predict': 0.102 seconds
total run time : 3.29 seconds
--------------------------------------------------
287 / 495
current local expert:
x y lon lat t is_in_ocean
297 -2.592486e+06 -1.786906e+06 -55.422922 61.494839 17913.0 True
'local_data_select': 0.003 seconds
number obs: 306
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.753 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([6.66980753, 3.33097577, 1.82253282])
kernel_variance: 0.005761507221236465
likelihood_variance: 0.0011859496786672955
'predict': 0.113 seconds
total run time : 2.61 seconds
--------------------------------------------------
288 / 495
current local expert:
x y lon lat t is_in_ocean
298 -2.567486e+06 -1.786906e+06 -55.163049 61.685075 17913.0 True
'local_data_select': 0.010 seconds
number obs: 314
setting lengthscales to: [1. 1. 1.]
'__init__': 0.050 seconds
'set_lengthscales_constraints': 0.013 seconds
**********
optimization failed!
'optimise_parameters': 1.882 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([6.51079488, 3.97337247, 2.32070788])
kernel_variance: 0.005962033488980444
likelihood_variance: 0.0011412917396821925
'predict': 0.093 seconds
total run time : 3.59 seconds
--------------------------------------------------
289 / 495
current local expert:
x y lon lat t is_in_ocean
299 -2.542486e+06 -1.786906e+06 -54.899745 61.874624 17913.0 True
'local_data_select': 0.002 seconds
number obs: 300
setting lengthscales to: [1. 1. 1.]
'__init__': 0.043 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.862 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.67303023, 4.96677998, 3.39092256])
kernel_variance: 0.0054677820682199904
likelihood_variance: 0.00123743082966201
'predict': 0.098 seconds
total run time : 2.66 seconds
--------------------------------------------------
290 / 495
current local expert:
x y lon lat t is_in_ocean
300 -2.517486e+06 -1.786906e+06 -54.632952 62.063475 17913.0 True
'local_data_select': 0.003 seconds
number obs: 287
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.949 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([5.21353587, 6.09450927, 3.46166216])
kernel_variance: 0.0053976415270917165
likelihood_variance: 0.0010929111527504497
'predict': 0.108 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.02 seconds
--------------------------------------------------
291 / 495
current local expert:
x y lon lat t is_in_ocean
301 -2.492486e+06 -1.786906e+06 -54.362612 62.251616 17913.0 True
'local_data_select': 0.003 seconds
number obs: 271
setting lengthscales to: [1. 1. 1.]
'__init__': 0.044 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.819 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([4.70597524, 4.6545445 , 3.30707555])
kernel_variance: 0.004979394083137208
likelihood_variance: 0.0008404046068175365
'predict': 0.108 seconds
total run time : 2.59 seconds
--------------------------------------------------
292 / 495
current local expert:
x y lon lat t is_in_ocean
302 -2.467486e+06 -1.786906e+06 -54.088666 62.439036 17913.0 True
'local_data_select': 0.004 seconds
number obs: 246
setting lengthscales to: [1. 1. 1.]
'__init__': 0.053 seconds
'set_lengthscales_constraints': 0.019 seconds
'optimise_parameters': 1.067 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([4.43088979, 2.19230592, 1.87830182])
kernel_variance: 0.0031954852980053093
likelihood_variance: 0.0007283113423712183
'predict': 0.107 seconds
total run time : 2.96 seconds
--------------------------------------------------
293 / 495
current local expert:
x y lon lat t is_in_ocean
303 -2.442486e+06 -1.786906e+06 -53.811054 62.625725 17913.0 True
'local_data_select': 0.003 seconds
number obs: 268
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 1.213 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.16852363, 2.25313243, 1.06675806])
kernel_variance: 0.002970403308406231
likelihood_variance: 0.0006899546599682142
'predict': 0.092 seconds
total run time : 2.82 seconds
--------------------------------------------------
294 / 495
current local expert:
x y lon lat t is_in_ocean
304 -2.417486e+06 -1.786906e+06 -53.529716 62.81167 17913.0 True
'local_data_select': 0.003 seconds
number obs: 257
setting lengthscales to: [1. 1. 1.]
'__init__': 0.049 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.710 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.49702838, 4.95996795, 2.37672368])
kernel_variance: 0.003314421307747122
likelihood_variance: 0.0007366141956676956
'predict': 0.116 seconds
total run time : 2.39 seconds
--------------------------------------------------
295 / 495
current local expert:
x y lon lat t is_in_ocean
305 -2.392486e+06 -1.786906e+06 -53.244589 62.996859 17913.0 True
'local_data_select': 0.003 seconds
number obs: 234
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.871 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([4.65338265, 4.97230228, 1.77999858])
kernel_variance: 0.0031914328518716697
likelihood_variance: 0.0007130222071105141
'predict': 0.083 seconds
total run time : 2.59 seconds
--------------------------------------------------
296 / 495
current local expert:
x y lon lat t is_in_ocean
306 -2.367486e+06 -1.786906e+06 -52.955612 63.181281 17913.0 True
'local_data_select': 0.003 seconds
number obs: 199
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.893 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([5.07043214, 5.69263383, 1.83271822])
kernel_variance: 0.003875329652735341
likelihood_variance: 0.0007324554057313467
'predict': 0.081 seconds
total run time : 2.99 seconds
--------------------------------------------------
297 / 495
current local expert:
x y lon lat t is_in_ocean
307 -2.342486e+06 -1.786906e+06 -52.66272 63.364922 17913.0 True
'local_data_select': 0.003 seconds
number obs: 154
setting lengthscales to: [1. 1. 1.]
'__init__': 0.056 seconds
'set_lengthscales_constraints': 0.017 seconds
'optimise_parameters': 0.709 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([4.1934014 , 3.34804743, 1.65876483])
kernel_variance: 0.001959380979482705
likelihood_variance: 0.0007917376195966801
'predict': 0.090 seconds
total run time : 2.39 seconds
--------------------------------------------------
298 / 495
current local expert:
x y lon lat t is_in_ocean
308 -2.867486e+06 -1.761906e+06 -58.431656 59.482 17913.0 True
'local_data_select': 0.002 seconds
number obs: 144
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.012 seconds
**********
optimization failed!
'optimise_parameters': 0.683 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([2.84838748, 9.01202276, 1.38911416])
kernel_variance: 0.002382092887905431
likelihood_variance: 0.0008808421696045515
'predict': 0.081 seconds
total run time : 2.35 seconds
--------------------------------------------------
299 / 495
current local expert:
x y lon lat t is_in_ocean
309 -2.842486e+06 -1.761906e+06 -58.207426 59.679896 17913.0 True
'local_data_select': 0.003 seconds
number obs: 189
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.658 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 2.69171601, 10.34586412, 1.26651162])
kernel_variance: 0.0018861116063208156
likelihood_variance: 0.0008389024377234451
'predict': 0.089 seconds
total run time : 2.36 seconds
--------------------------------------------------
300 / 495
current local expert:
x y lon lat t is_in_ocean
310 -2.817486e+06 -1.761906e+06 -57.980328 59.877211 17913.0 True
'local_data_select': 0.003 seconds
number obs: 231
setting lengthscales to: [1. 1. 1.]
'__init__': 0.043 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.968 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([2.72959623, 8.90203462, 1.47706835])
kernel_variance: 0.0018188671676792095
likelihood_variance: 0.0008754768863750461
'predict': 0.114 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.42 seconds
--------------------------------------------------
301 / 495
current local expert:
x y lon lat t is_in_ocean
311 -2.792486e+06 -1.761906e+06 -57.750315 60.073937 17913.0 True
'local_data_select': 0.007 seconds
number obs: 257
setting lengthscales to: [1. 1. 1.]
'__init__': 0.074 seconds
'set_lengthscales_constraints': 0.024 seconds
'optimise_parameters': 1.210 seconds
'get_parameters': 0.013 seconds
parameters:
lengthscales: array([3.23233088, 7.46885316, 1.7202119 ])
kernel_variance: 0.0019953285760111517
likelihood_variance: 0.0008800717414389709
'predict': 0.210 seconds
total run time : 3.23 seconds
--------------------------------------------------
302 / 495
current local expert:
x y lon lat t is_in_ocean
312 -2.767486e+06 -1.761906e+06 -57.517337 60.270067 17913.0 True
'local_data_select': 0.004 seconds
number obs: 292
setting lengthscales to: [1. 1. 1.]
'__init__': 0.052 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 0.904 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([5.16533977, 9.53466423, 2.18319082])
kernel_variance: 0.002062700674462203
likelihood_variance: 0.0009551775539466095
'predict': 0.097 seconds
total run time : 2.56 seconds
--------------------------------------------------
303 / 495
current local expert:
x y lon lat t is_in_ocean
313 -2.742486e+06 -1.761906e+06 -57.281344 60.465592 17913.0 True
'local_data_select': 0.004 seconds
number obs: 279
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.897 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([28.69290432, 14.47659374, 2.71094855])
kernel_variance: 0.003228583618516625
likelihood_variance: 0.0012291976954591536
'predict': 0.088 seconds
total run time : 2.69 seconds
--------------------------------------------------
304 / 495
current local expert:
x y lon lat t is_in_ocean
314 -2.717486e+06 -1.761906e+06 -57.042286 60.660504 17913.0 True
'local_data_select': 0.004 seconds
number obs: 294
setting lengthscales to: [1. 1. 1.]
'__init__': 0.044 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.131 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([9.12930058, 3.40871648, 0.71866187])
kernel_variance: 0.004727045309205284
likelihood_variance: 0.0009511003844733709
'predict': 0.103 seconds
total run time : 2.91 seconds
--------------------------------------------------
305 / 495
current local expert:
x y lon lat t is_in_ocean
315 -2.692486e+06 -1.761906e+06 -56.800111 60.854794 17913.0 True
'local_data_select': 0.002 seconds
number obs: 298
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.014 seconds
**********
optimization failed!
'optimise_parameters': 2.095 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([59.91545567, 4.39205827, 0.5373849 ])
kernel_variance: 0.004598265171438012
likelihood_variance: 0.0012327018477979639
'predict': 0.153 seconds
total run time : 3.76 seconds
--------------------------------------------------
306 / 495
current local expert:
x y lon lat t is_in_ocean
316 -2.667486e+06 -1.761906e+06 -56.554767 61.048453 17913.0 True
'local_data_select': 0.003 seconds
number obs: 301
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.982 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([14.81959642, 3.99225551, 2.40068343])
kernel_variance: 0.005448240803308237
likelihood_variance: 0.0011909437641489243
'predict': 0.097 seconds
total run time : 2.71 seconds
--------------------------------------------------
307 / 495
current local expert:
x y lon lat t is_in_ocean
317 -2.642486e+06 -1.761906e+06 -56.3062 61.241472 17913.0 True
'local_data_select': 0.003 seconds
number obs: 299
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.205 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([9.26072688, 4.16295722, 2.50855978])
kernel_variance: 0.006506622530016271
likelihood_variance: 0.001135122938648063
'predict': 0.093 seconds
total run time : 3.03 seconds
--------------------------------------------------
308 / 495
current local expert:
x y lon lat t is_in_ocean
318 -2.617486e+06 -1.761906e+06 -56.054356 61.433841 17913.0 True
'local_data_select': 0.003 seconds
number obs: 309
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.963 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([7.05574445, 4.18852154, 2.55555863])
kernel_variance: 0.005874147634868582
likelihood_variance: 0.0011791902431617727
'predict': 0.083 seconds
total run time : 2.67 seconds
--------------------------------------------------
309 / 495
current local expert:
x y lon lat t is_in_ocean
319 -2.592486e+06 -1.761906e+06 -55.799179 61.625551 17913.0 True
'local_data_select': 0.002 seconds
number obs: 286
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.917 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([7.98018071, 3.65740996, 2.85144545])
kernel_variance: 0.006318018923577569
likelihood_variance: 0.0012490838798194302
'predict': 0.124 seconds
total run time : 2.93 seconds
--------------------------------------------------
310 / 495
current local expert:
x y lon lat t is_in_ocean
320 -2.567486e+06 -1.761906e+06 -55.540613 61.816591 17913.0 True
'local_data_select': 0.003 seconds
number obs: 300
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.059 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([6.6421174 , 4.09600019, 2.36987044])
kernel_variance: 0.0057366308441471675
likelihood_variance: 0.0011899817225545556
'predict': 0.091 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.12 seconds
--------------------------------------------------
311 / 495
current local expert:
x y lon lat t is_in_ocean
321 -2.542486e+06 -1.761906e+06 -55.278601 62.006953 17913.0 True
'local_data_select': 0.003 seconds
number obs: 290
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.830 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([5.33691068, 7.81381317, 5.63314256])
kernel_variance: 0.004382911991489233
likelihood_variance: 0.001321635926349299
'predict': 0.098 seconds
total run time : 2.67 seconds
--------------------------------------------------
312 / 495
current local expert:
x y lon lat t is_in_ocean
322 -2.517486e+06 -1.761906e+06 -55.013085 62.196624 17913.0 True
'local_data_select': 0.005 seconds
number obs: 287
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.775 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.57297941, 3.62439263, 4.26983822])
kernel_variance: 0.0032717261124055293
likelihood_variance: 0.00107816103340151
'predict': 0.115 seconds
total run time : 2.55 seconds
--------------------------------------------------
313 / 495
current local expert:
x y lon lat t is_in_ocean
323 -2.492486e+06 -1.761906e+06 -54.744006 62.385595 17913.0 True
'local_data_select': 0.003 seconds
number obs: 297
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.015 seconds
'optimise_parameters': 0.776 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([4.64727975, 1.97263142, 1.82113227])
kernel_variance: 0.0029390939269415117
likelihood_variance: 0.0008554782194542879
'predict': 0.091 seconds
total run time : 2.73 seconds
--------------------------------------------------
314 / 495
current local expert:
x y lon lat t is_in_ocean
324 -2.467486e+06 -1.761906e+06 -54.471303 62.573854 17913.0 True
'local_data_select': 0.005 seconds
number obs: 283
setting lengthscales to: [1. 1. 1.]
'__init__': 0.051 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 0.919 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([4.22972213, 2.9774019 , 3.28475063])
kernel_variance: 0.0029013777846925715
likelihood_variance: 0.0007558099614775757
'predict': 0.085 seconds
total run time : 2.78 seconds
--------------------------------------------------
315 / 495
current local expert:
x y lon lat t is_in_ocean
325 -2.442486e+06 -1.761906e+06 -54.194915 62.761389 17913.0 True
'local_data_select': 0.003 seconds
number obs: 277
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.776 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.84637191, 6.95172968, 2.62211494])
kernel_variance: 0.0031657735063510316
likelihood_variance: 0.0008247799413302287
'predict': 0.091 seconds
total run time : 2.53 seconds
--------------------------------------------------
316 / 495
current local expert:
x y lon lat t is_in_ocean
326 -2.417486e+06 -1.761906e+06 -53.914781 62.94819 17913.0 True
'local_data_select': 0.002 seconds
number obs: 272
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.174 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.41151484, 8.51064749, 0.83745873])
kernel_variance: 0.0033731710962676856
likelihood_variance: 0.0007772283343382908
'predict': 0.095 seconds
total run time : 2.83 seconds
--------------------------------------------------
317 / 495
current local expert:
x y lon lat t is_in_ocean
327 -2.392486e+06 -1.761906e+06 -53.630838 63.134245 17913.0 True
'local_data_select': 0.005 seconds
number obs: 223
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.878 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.30560444, 7.37528268, 0.733696 ])
kernel_variance: 0.0037387061323012937
likelihood_variance: 0.0007730359414568615
'predict': 0.088 seconds
total run time : 2.62 seconds
--------------------------------------------------
318 / 495
current local expert:
x y lon lat t is_in_ocean
328 -2.367486e+06 -1.761906e+06 -53.34302 63.31954 17913.0 True
'local_data_select': 0.003 seconds
number obs: 183
setting lengthscales to: [1. 1. 1.]
'__init__': 0.045 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.904 seconds
'get_parameters': 0.016 seconds
parameters:
lengthscales: array([3.69290758, 4.96618661, 2.51617431])
kernel_variance: 0.002248050388865046
likelihood_variance: 0.000806007652118191
'predict': 0.122 seconds
total run time : 2.78 seconds
--------------------------------------------------
319 / 495
current local expert:
x y lon lat t is_in_ocean
329 -2.342486e+06 -1.761906e+06 -53.051265 63.504064 17913.0 True
'local_data_select': 0.002 seconds
number obs: 135
setting lengthscales to: [1. 1. 1.]
'__init__': 0.047 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 0.533 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([5.43449162, 3.4529766 , 1.97156251])
kernel_variance: 0.0018440068002291863
likelihood_variance: 0.0008565600048412612
'predict': 0.077 seconds
total run time : 2.32 seconds
--------------------------------------------------
320 / 495
current local expert:
x y lon lat t is_in_ocean
330 -2.867486e+06 -1.736906e+06 -58.795691 59.603125 17913.0 True
'local_data_select': 0.003 seconds
number obs: 129
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 0.599 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 2.56672071, 38.28539534, 0.07433317])
kernel_variance: 0.002284043024473543
likelihood_variance: 0.0008978163923495194
'predict': 0.080 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 2.73 seconds
--------------------------------------------------
321 / 495
current local expert:
x y lon lat t is_in_ocean
331 -2.842486e+06 -1.736906e+06 -58.572912 59.801733 17913.0 True
'local_data_select': 0.003 seconds
number obs: 163
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.845 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.6939231 , 7.31073781, 1.26814623])
kernel_variance: 0.00200455361704313
likelihood_variance: 0.0008587168574390574
'predict': 0.076 seconds
total run time : 2.59 seconds
--------------------------------------------------
322 / 495
current local expert:
x y lon lat t is_in_ocean
332 -2.817486e+06 -1.736906e+06 -58.34726 59.999768 17913.0 True
'local_data_select': 0.003 seconds
number obs: 214
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.107 seconds
'get_parameters': 0.014 seconds
parameters:
lengthscales: array([2.84837891, 9.928227 , 1.74484609])
kernel_variance: 0.0017971737658840177
likelihood_variance: 0.0008283276819919271
'predict': 0.128 seconds
total run time : 3.26 seconds
--------------------------------------------------
323 / 495
current local expert:
x y lon lat t is_in_ocean
333 -2.792486e+06 -1.736906e+06 -58.118688 60.197223 17913.0 True
'local_data_select': 0.002 seconds
number obs: 249
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.054 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.93437519, 9.27187964, 2.01733957])
kernel_variance: 0.0018478518143898687
likelihood_variance: 0.001003046849284364
'predict': 0.122 seconds
total run time : 2.77 seconds
--------------------------------------------------
324 / 495
current local expert:
x y lon lat t is_in_ocean
334 -2.767486e+06 -1.736906e+06 -57.887146 60.39409 17913.0 True
'local_data_select': 0.002 seconds
number obs: 276
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.799 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([5.2085657 , 9.44097774, 1.94546771])
kernel_variance: 0.0019959115447546736
likelihood_variance: 0.0009750796416432259
'predict': 0.108 seconds
total run time : 2.51 seconds
--------------------------------------------------
325 / 495
current local expert:
x y lon lat t is_in_ocean
335 -2.742486e+06 -1.736906e+06 -57.652584 60.590359 17913.0 True
'local_data_select': 0.002 seconds
number obs: 286
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.928 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.09867571, 8.23944607, 1.74965119])
kernel_variance: 0.002355795204136707
likelihood_variance: 0.0008655756793843895
'predict': 0.092 seconds
total run time : 2.70 seconds
--------------------------------------------------
326 / 495
current local expert:
x y lon lat t is_in_ocean
336 -2.717486e+06 -1.736906e+06 -57.414949 60.786024 17913.0 True
'local_data_select': 0.003 seconds
number obs: 278
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.998 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 7.87291248, 10.21329034, 1.49594118])
kernel_variance: 0.0024498931005921334
likelihood_variance: 0.0009145374034470695
'predict': 0.093 seconds
total run time : 2.94 seconds
--------------------------------------------------
327 / 495
current local expert:
x y lon lat t is_in_ocean
337 -2.692486e+06 -1.736906e+06 -57.174189 60.981075 17913.0 True
'local_data_select': 0.006 seconds
number obs: 287
setting lengthscales to: [1. 1. 1.]
'__init__': 0.051 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 1.552 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([59.92245455, 4.74950131, 0.57878811])
kernel_variance: 0.005055991060122779
likelihood_variance: 0.0011068645852782596
'predict': 0.090 seconds
total run time : 3.34 seconds
--------------------------------------------------
328 / 495
current local expert:
x y lon lat t is_in_ocean
338 -2.667486e+06 -1.736906e+06 -56.930252 61.175504 17913.0 True
'local_data_select': 0.002 seconds
number obs: 287
setting lengthscales to: [1. 1. 1.]
'__init__': 0.043 seconds
'set_lengthscales_constraints': 0.016 seconds
'optimise_parameters': 1.213 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([59.99968357, 4.90730118, 2.34501312])
kernel_variance: 0.0062633473069999245
likelihood_variance: 0.0012509743081609126
'predict': 0.113 seconds
total run time : 2.98 seconds
--------------------------------------------------
329 / 495
current local expert:
x y lon lat t is_in_ocean
339 -2.642486e+06 -1.736906e+06 -56.683082 61.3693 17913.0 True
'local_data_select': 0.003 seconds
number obs: 282
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.472 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([59.96959706, 5.02236227, 2.16462157])
kernel_variance: 0.006046684066344967
likelihood_variance: 0.0012890801439941295
'predict': 0.097 seconds
total run time : 3.19 seconds
--------------------------------------------------
330 / 495
current local expert:
x y lon lat t is_in_ocean
340 -2.617486e+06 -1.736906e+06 -56.432625 61.562456 17913.0 True
'local_data_select': 0.003 seconds
number obs: 297
setting lengthscales to: [1. 1. 1.]
'__init__': 0.055 seconds
'set_lengthscales_constraints': 0.018 seconds
**********
optimization failed!
'optimise_parameters': 1.517 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([16.35667662, 4.35458801, 0.77196683])
kernel_variance: 0.005538147317038485
likelihood_variance: 0.0010770344876880286
'predict': 0.128 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 4.26 seconds
--------------------------------------------------
331 / 495
current local expert:
x y lon lat t is_in_ocean
341 -2.592486e+06 -1.736906e+06 -56.178824 61.754961 17913.0 True
'local_data_select': 0.002 seconds
number obs: 310
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.885 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([13.33419675, 3.35734721, 3.19761082])
kernel_variance: 0.005544364499495737
likelihood_variance: 0.0011744536202939124
'predict': 0.096 seconds
total run time : 2.71 seconds
--------------------------------------------------
332 / 495
current local expert:
x y lon lat t is_in_ocean
342 -2.567486e+06 -1.736906e+06 -55.921623 61.946805 17913.0 True
'local_data_select': 0.003 seconds
number obs: 302
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.810 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([8.51635248, 2.84247763, 2.21856496])
kernel_variance: 0.0035496351202844753
likelihood_variance: 0.0012666829990587315
'predict': 0.085 seconds
total run time : 2.58 seconds
--------------------------------------------------
333 / 495
current local expert:
x y lon lat t is_in_ocean
343 -2.542486e+06 -1.736906e+06 -55.660961 62.137979 17913.0 True
'local_data_select': 0.003 seconds
number obs: 316
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.941 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([6.42417053, 3.58196134, 2.70482988])
kernel_variance: 0.003659021170872279
likelihood_variance: 0.0011271621672692337
'predict': 0.115 seconds
total run time : 2.79 seconds
--------------------------------------------------
334 / 495
current local expert:
x y lon lat t is_in_ocean
344 -2.517486e+06 -1.736906e+06 -55.396782 62.328471 17913.0 True
'local_data_select': 0.003 seconds
number obs: 298
setting lengthscales to: [1. 1. 1.]
'__init__': 0.052 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 0.898 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([4.55920147, 1.59329043, 1.15364195])
kernel_variance: 0.0012497273280177626
likelihood_variance: 0.0009093718022038864
'predict': 0.161 seconds
total run time : 3.23 seconds
--------------------------------------------------
335 / 495
current local expert:
x y lon lat t is_in_ocean
345 -2.492486e+06 -1.736906e+06 -55.129023 62.518272 17913.0 True
'local_data_select': 0.005 seconds
number obs: 288
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.128 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([5.72085401, 1.52852795, 0.4829593 ])
kernel_variance: 0.0013937504040180387
likelihood_variance: 0.000805267427296906
'predict': 0.111 seconds
total run time : 3.04 seconds
--------------------------------------------------
336 / 495
current local expert:
x y lon lat t is_in_ocean
346 -2.467486e+06 -1.736906e+06 -54.857624 62.707369 17913.0 True
'local_data_select': 0.003 seconds
number obs: 280
setting lengthscales to: [1. 1. 1.]
'__init__': 0.031 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.830 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.35046898, 2.61497611, 1.39511922])
kernel_variance: 0.0011741269170683227
likelihood_variance: 0.000867392734986022
'predict': 0.108 seconds
total run time : 2.75 seconds
--------------------------------------------------
337 / 495
current local expert:
x y lon lat t is_in_ocean
347 -2.442486e+06 -1.736906e+06 -54.582523 62.895753 17913.0 True
'local_data_select': 0.002 seconds
number obs: 261
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.007 seconds
'optimise_parameters': 1.134 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.28694333, 6.65443622, 2.37361338])
kernel_variance: 0.0013711733448813115
likelihood_variance: 0.0009063080261171618
'predict': 0.093 seconds
total run time : 2.96 seconds
--------------------------------------------------
338 / 495
current local expert:
x y lon lat t is_in_ocean
348 -2.417486e+06 -1.736906e+06 -54.303657 63.083411 17913.0 True
'local_data_select': 0.003 seconds
number obs: 262
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.715 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([2.94202795, 4.14831476, 2.30375474])
kernel_variance: 0.0013478300536731002
likelihood_variance: 0.0007362396504472713
'predict': 0.206 seconds
total run time : 5.45 seconds
--------------------------------------------------
339 / 495
current local expert:
x y lon lat t is_in_ocean
349 -2.392486e+06 -1.736906e+06 -54.02096 63.270331 17913.0 True
'local_data_select': 0.011 seconds
number obs: 236
setting lengthscales to: [1. 1. 1.]
'__init__': 0.190 seconds
'set_lengthscales_constraints': 0.025 seconds
'optimise_parameters': 2.096 seconds
'get_parameters': 0.036 seconds
parameters:
lengthscales: array([2.8005409 , 4.38313568, 2.53720502])
kernel_variance: 0.0015509049086264348
likelihood_variance: 0.0007264115818069243
'predict': 0.175 seconds
total run time : 4.97 seconds
--------------------------------------------------
340 / 495
current local expert:
x y lon lat t is_in_ocean
350 -2.367486e+06 -1.736906e+06 -53.734367 63.456501 17913.0 True
'local_data_select': 0.003 seconds
number obs: 190
setting lengthscales to: [1. 1. 1.]
'__init__': 0.044 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.608 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([2.69265635, 3.76973638, 2.35932325])
kernel_variance: 0.0015705908858898922
likelihood_variance: 0.0007323790113860843
'predict': 0.105 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.08 seconds
--------------------------------------------------
341 / 495
current local expert:
x y lon lat t is_in_ocean
351 -2.342486e+06 -1.736906e+06 -53.443813 63.641909 17913.0 True
'local_data_select': 0.003 seconds
number obs: 151
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.545 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([2.85367709, 3.62866496, 2.28314721])
kernel_variance: 0.001629527301014766
likelihood_variance: 0.0007735894902511568
'predict': 0.082 seconds
total run time : 2.47 seconds
--------------------------------------------------
342 / 495
current local expert:
x y lon lat t is_in_ocean
352 -2.867486e+06 -1.711906e+06 -59.16255 59.722943 17913.0 True
'local_data_select': 0.003 seconds
number obs: 105
setting lengthscales to: [1. 1. 1.]
'__init__': 0.056 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.768 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([ 2.40936656, 59.99340778, 1.95973593])
kernel_variance: 0.0016869224032271424
likelihood_variance: 0.0006560193022887911
'predict': 0.085 seconds
total run time : 2.90 seconds
--------------------------------------------------
343 / 495
current local expert:
x y lon lat t is_in_ocean
353 -2.842486e+06 -1.711906e+06 -58.941269 59.922263 17913.0 True
'local_data_select': 0.003 seconds
number obs: 145
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.550 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 2.49817811, 59.97227998, 0.96689525])
kernel_variance: 0.002394159371688534
likelihood_variance: 0.0007287592316127251
'predict': 0.080 seconds
total run time : 2.37 seconds
--------------------------------------------------
344 / 495
current local expert:
x y lon lat t is_in_ocean
354 -2.817486e+06 -1.711906e+06 -58.717112 60.121017 17913.0 True
'local_data_select': 0.003 seconds
number obs: 179
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.607 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 2.78306083, 59.96505387, 0.59981048])
kernel_variance: 0.003482796889072476
likelihood_variance: 0.0008033158946625859
'predict': 0.078 seconds
total run time : 2.42 seconds
--------------------------------------------------
345 / 495
current local expert:
x y lon lat t is_in_ocean
355 -2.792486e+06 -1.711906e+06 -58.490031 60.319199 17913.0 True
'local_data_select': 0.004 seconds
number obs: 226
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.010 seconds
**********
optimization failed!
'optimise_parameters': 1.331 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 2.8564583 , 59.94208382, 0.71926769])
kernel_variance: 0.003612385073161243
likelihood_variance: 0.0007821275542576315
'predict': 0.076 seconds
total run time : 3.19 seconds
--------------------------------------------------
346 / 495
current local expert:
x y lon lat t is_in_ocean
356 -2.767486e+06 -1.711906e+06 -58.259976 60.516801 17913.0 True
'local_data_select': 0.004 seconds
number obs: 249
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.289 seconds
'get_parameters': 0.014 seconds
parameters:
lengthscales: array([ 2.29779342, 59.94191981, 0.68314057])
kernel_variance: 0.0023625445929643402
likelihood_variance: 0.0006543072180667279
'predict': 0.126 seconds
total run time : 3.41 seconds
--------------------------------------------------
347 / 495
current local expert:
x y lon lat t is_in_ocean
357 -2.742486e+06 -1.711906e+06 -58.026895 60.713814 17913.0 True
'local_data_select': 0.004 seconds
number obs: 262
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.897 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 3.05854085, 59.99977986, 1.2993243 ])
kernel_variance: 0.002051303211227955
likelihood_variance: 0.0007566770203073154
'predict': 0.093 seconds
total run time : 2.67 seconds
--------------------------------------------------
348 / 495
current local expert:
x y lon lat t is_in_ocean
358 -2.717486e+06 -1.711906e+06 -57.790735 60.910231 17913.0 True
'local_data_select': 0.003 seconds
number obs: 262
setting lengthscales to: [1. 1. 1.]
'__init__': 0.030 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.740 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([4.27586801, 5.96641891, 1.13513175])
kernel_variance: 0.0019909647659494913
likelihood_variance: 0.0006640040608095599
'predict': 0.092 seconds
total run time : 2.55 seconds
--------------------------------------------------
349 / 495
current local expert:
x y lon lat t is_in_ocean
359 -2.692486e+06 -1.711906e+06 -57.551445 61.106042 17913.0 True
'local_data_select': 0.002 seconds
number obs: 259
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.186 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([9.22542565, 6.87300634, 1.48321277])
kernel_variance: 0.0022663480120563625
likelihood_variance: 0.0007542809110917611
'predict': 0.090 seconds
total run time : 3.00 seconds
--------------------------------------------------
350 / 495
current local expert:
x y lon lat t is_in_ocean
360 -2.667486e+06 -1.711906e+06 -57.308969 61.301239 17913.0 True
'local_data_select': 0.002 seconds
number obs: 280
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.165 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([59.99831598, 7.98246243, 1.3675968 ])
kernel_variance: 0.0022850635545445712
likelihood_variance: 0.001283568576973377
'predict': 0.141 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.96 seconds
--------------------------------------------------
351 / 495
current local expert:
x y lon lat t is_in_ocean
361 -2.642486e+06 -1.711906e+06 -57.063252 61.495812 17913.0 True
'local_data_select': 0.003 seconds
number obs: 282
setting lengthscales to: [1. 1. 1.]
'__init__': 0.049 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.909 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([11.31291867, 4.54209189, 1.62084192])
kernel_variance: 0.002450553126041372
likelihood_variance: 0.0012728319896078471
'predict': 0.104 seconds
total run time : 2.66 seconds
--------------------------------------------------
352 / 495
current local expert:
x y lon lat t is_in_ocean
362 -2.617486e+06 -1.711906e+06 -56.814239 61.689754 17913.0 True
'local_data_select': 0.003 seconds
number obs: 277
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.884 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([10.12142249, 3.64856292, 1.75404947])
kernel_variance: 0.0031152192683810893
likelihood_variance: 0.0011759852993318686
'predict': 0.093 seconds
total run time : 2.65 seconds
--------------------------------------------------
353 / 495
current local expert:
x y lon lat t is_in_ocean
363 -2.592486e+06 -1.711906e+06 -56.561871 61.883053 17913.0 True
'local_data_select': 0.003 seconds
number obs: 292
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.830 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([31.77464422, 5.83627596, 1.24063825])
kernel_variance: 0.002308979759697544
likelihood_variance: 0.0012443639185140993
'predict': 0.092 seconds
total run time : 2.62 seconds
--------------------------------------------------
354 / 495
current local expert:
x y lon lat t is_in_ocean
364 -2.567486e+06 -1.711906e+06 -56.306091 62.0757 17913.0 True
'local_data_select': 0.003 seconds
number obs: 290
setting lengthscales to: [1. 1. 1.]
'__init__': 0.047 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.102 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([23.55487168, 6.71926183, 1.20545418])
kernel_variance: 0.0019982837880088444
likelihood_variance: 0.0012297548682116172
'predict': 0.131 seconds
total run time : 3.55 seconds
--------------------------------------------------
355 / 495
current local expert:
x y lon lat t is_in_ocean
365 -2.542486e+06 -1.711906e+06 -56.04684 62.267686 17913.0 True
'local_data_select': 0.002 seconds
number obs: 293
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.263 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 5.79869671, 44.82214459, 1.76189717])
kernel_variance: 0.0025041016991098008
likelihood_variance: 0.0010400671414225605
'predict': 0.094 seconds
total run time : 3.03 seconds
--------------------------------------------------
356 / 495
current local expert:
x y lon lat t is_in_ocean
366 -2.517486e+06 -1.711906e+06 -55.784057 62.458999 17913.0 True
'local_data_select': 0.003 seconds
number obs: 308
setting lengthscales to: [1. 1. 1.]
'__init__': 0.044 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 1.055 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([4.87427295, 2.47716092, 1.05344478])
kernel_variance: 0.0014118066248199532
likelihood_variance: 0.0008507563844713907
'predict': 0.089 seconds
total run time : 2.96 seconds
--------------------------------------------------
357 / 495
current local expert:
x y lon lat t is_in_ocean
367 -2.492486e+06 -1.711906e+06 -55.51768 62.64963 17913.0 True
'local_data_select': 0.003 seconds
number obs: 295
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.579 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.16785552, 2.37834824, 0.97399408])
kernel_variance: 0.0014630618845132453
likelihood_variance: 0.0008340376832042481
'predict': 0.094 seconds
total run time : 3.41 seconds
--------------------------------------------------
358 / 495
current local expert:
x y lon lat t is_in_ocean
368 -2.467486e+06 -1.711906e+06 -55.247648 62.839566 17913.0 True
'local_data_select': 0.003 seconds
number obs: 302
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 2.595 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 3.08601563, 59.98499663, 0.90808247])
kernel_variance: 0.0013199841670417096
likelihood_variance: 0.0008634338033617034
'predict': 0.088 seconds
total run time : 4.48 seconds
--------------------------------------------------
359 / 495
current local expert:
x y lon lat t is_in_ocean
369 -2.442486e+06 -1.711906e+06 -54.973897 63.028798 17913.0 True
'local_data_select': 0.003 seconds
number obs: 287
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.135 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([ 3.1210871 , 59.99682435, 1.1217805 ])
kernel_variance: 0.001589656703734207
likelihood_variance: 0.0008421222354984873
'predict': 0.093 seconds
total run time : 3.07 seconds
--------------------------------------------------
360 / 495
current local expert:
x y lon lat t is_in_ocean
370 -2.417486e+06 -1.711906e+06 -54.696362 63.217313 17913.0 True
'local_data_select': 0.004 seconds
number obs: 259
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 0.884 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([2.68061656, 4.95495891, 3.19072533])
kernel_variance: 0.0011738139531979976
likelihood_variance: 0.0007054719232839534
'predict': 0.093 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.38 seconds
--------------------------------------------------
361 / 495
current local expert:
x y lon lat t is_in_ocean
371 -2.392486e+06 -1.711906e+06 -54.414977 63.4051 17913.0 True
'local_data_select': 0.004 seconds
number obs: 234
setting lengthscales to: [1. 1. 1.]
'__init__': 0.043 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.735 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.59035546, 3.38617562, 3.79216037])
kernel_variance: 0.0012887158128837162
likelihood_variance: 0.0006877829825644127
'predict': 0.106 seconds
total run time : 2.82 seconds
--------------------------------------------------
362 / 495
current local expert:
x y lon lat t is_in_ocean
372 -2.367486e+06 -1.711906e+06 -54.129676 63.592146 17913.0 True
'local_data_select': 0.003 seconds
number obs: 189
setting lengthscales to: [1. 1. 1.]
'__init__': 0.046 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 1.019 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([2.62766081, 3.49183534, 3.70518483])
kernel_variance: 0.001485495714429249
likelihood_variance: 0.0007107575563969004
'predict': 0.094 seconds
total run time : 3.04 seconds
--------------------------------------------------
363 / 495
current local expert:
x y lon lat t is_in_ocean
373 -2.342486e+06 -1.711906e+06 -53.840391 63.778439 17913.0 True
'local_data_select': 0.002 seconds
number obs: 145
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.529 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([2.26484945, 3.16565644, 2.82517891])
kernel_variance: 0.0015411222231809095
likelihood_variance: 0.0007039956467707003
'predict': 0.077 seconds
total run time : 2.44 seconds
--------------------------------------------------
364 / 495
current local expert:
x y lon lat t is_in_ocean
374 -2.867486e+06 -1.686906e+06 -59.532236 59.841444 17913.0 True
'local_data_select': 0.003 seconds
number obs: 86
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.493 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 3.65122741, 59.99973134, 4.42700185])
kernel_variance: 0.000797483187601191
likelihood_variance: 0.0007314273606400288
'predict': 0.090 seconds
total run time : 2.43 seconds
--------------------------------------------------
365 / 495
current local expert:
x y lon lat t is_in_ocean
375 -2.842486e+06 -1.686906e+06 -59.312501 60.041472 17913.0 True
'local_data_select': 0.002 seconds
number obs: 133
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.742 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 1.87061278, 35.74203864, 0.03712759])
kernel_variance: 0.0024927728546763925
likelihood_variance: 0.000650892765770564
'predict': 0.095 seconds
total run time : 2.68 seconds
--------------------------------------------------
366 / 495
current local expert:
x y lon lat t is_in_ocean
376 -2.817486e+06 -1.686906e+06 -59.089888 60.240944 17913.0 True
'local_data_select': 0.003 seconds
number obs: 168
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 0.995 seconds
'get_parameters': 0.015 seconds
parameters:
lengthscales: array([1.87168101e+00, 2.99463304e+01, 1.80493315e-02])
kernel_variance: 0.002134517208167082
likelihood_variance: 0.0006292832975024818
'predict': 0.125 seconds
total run time : 3.50 seconds
--------------------------------------------------
367 / 495
current local expert:
x y lon lat t is_in_ocean
377 -2.792486e+06 -1.686906e+06 -58.864349 60.439851 17913.0 True
'local_data_select': 0.005 seconds
number obs: 196
setting lengthscales to: [1. 1. 1.]
'__init__': 0.030 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.772 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.12230237, 7.1975322 , 2.72575547])
kernel_variance: 0.0024477854606915774
likelihood_variance: 0.0006592522567674561
'predict': 0.091 seconds
total run time : 2.78 seconds
--------------------------------------------------
368 / 495
current local expert:
x y lon lat t is_in_ocean
378 -2.767486e+06 -1.686906e+06 -58.635832 60.638187 17913.0 True
'local_data_select': 0.003 seconds
number obs: 226
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.790 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([2.07229968, 6.14923605, 2.69905128])
kernel_variance: 0.002193726575200834
likelihood_variance: 0.0006188331938747195
'predict': 0.086 seconds
total run time : 2.78 seconds
--------------------------------------------------
369 / 495
current local expert:
x y lon lat t is_in_ocean
379 -2.742486e+06 -1.686906e+06 -58.404284 60.835942 17913.0 True
'local_data_select': 0.002 seconds
number obs: 233
setting lengthscales to: [1. 1. 1.]
'__init__': 0.040 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.903 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 2.25418752, 59.04379136, 0.56182904])
kernel_variance: 0.0020972661555484795
likelihood_variance: 0.000604055000276823
'predict': 0.103 seconds
total run time : 3.08 seconds
--------------------------------------------------
370 / 495
current local expert:
x y lon lat t is_in_ocean
380 -2.717486e+06 -1.686906e+06 -58.169653 61.033108 17913.0 True
'local_data_select': 0.003 seconds
number obs: 254
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.048 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([2.49689437, 8.54474584, 1.26142296])
kernel_variance: 0.0026234398918369463
likelihood_variance: 0.0006389167835842482
'predict': 0.138 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.93 seconds
--------------------------------------------------
371 / 495
current local expert:
x y lon lat t is_in_ocean
381 -2.692486e+06 -1.686906e+06 -57.931886 61.229678 17913.0 True
'local_data_select': 0.003 seconds
number obs: 248
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 0.769 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 5.22666587, 10.88119887, 1.21444527])
kernel_variance: 0.002092922774475095
likelihood_variance: 0.0006588832894345924
'predict': 0.089 seconds
total run time : 2.66 seconds
--------------------------------------------------
372 / 495
current local expert:
x y lon lat t is_in_ocean
382 -2.667486e+06 -1.686906e+06 -57.690926 61.425643 17913.0 True
'local_data_select': 0.003 seconds
number obs: 260
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.961 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([8.78535223, 9.52378077, 1.47109282])
kernel_variance: 0.0014779109452801472
likelihood_variance: 0.0009825576564328098
'predict': 0.097 seconds
total run time : 3.06 seconds
--------------------------------------------------
373 / 495
current local expert:
x y lon lat t is_in_ocean
383 -2.642486e+06 -1.686906e+06 -57.446718 61.620992 17913.0 True
'local_data_select': 0.004 seconds
number obs: 268
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.897 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([9.76060166, 9.0455739 , 1.38471789])
kernel_variance: 0.0015003635369254657
likelihood_variance: 0.001007612331141231
'predict': 0.112 seconds
total run time : 2.97 seconds
--------------------------------------------------
374 / 495
current local expert:
x y lon lat t is_in_ocean
384 -2.617486e+06 -1.686906e+06 -57.199206 61.815718 17913.0 True
'local_data_select': 0.004 seconds
number obs: 278
setting lengthscales to: [1. 1. 1.]
'__init__': 0.050 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 1.038 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([8.00029647, 5.53582598, 1.31569859])
kernel_variance: 0.0016592779274229596
likelihood_variance: 0.001048463898872524
'predict': 0.155 seconds
total run time : 3.32 seconds
--------------------------------------------------
375 / 495
current local expert:
x y lon lat t is_in_ocean
385 -2.592486e+06 -1.686906e+06 -56.94833 62.009811 17913.0 True
'local_data_select': 0.002 seconds
number obs: 270
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.774 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([8.99363357, 3.72695675, 0.86755349])
kernel_variance: 0.0017165777812752664
likelihood_variance: 0.0009128099059116793
'predict': 0.100 seconds
total run time : 2.70 seconds
--------------------------------------------------
376 / 495
current local expert:
x y lon lat t is_in_ocean
386 -2.567486e+06 -1.686906e+06 -56.694031 62.203261 17913.0 True
'local_data_select': 0.002 seconds
number obs: 290
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 1.229 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([5.87273781, 9.83029112, 1.01166923])
kernel_variance: 0.0020428611785717355
likelihood_variance: 0.00095424373395939
'predict': 0.092 seconds
total run time : 3.15 seconds
--------------------------------------------------
377 / 495
current local expert:
x y lon lat t is_in_ocean
387 -2.542486e+06 -1.686906e+06 -56.43625 62.396058 17913.0 True
'local_data_select': 0.003 seconds
number obs: 275
setting lengthscales to: [1. 1. 1.]
'__init__': 0.048 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.340 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([5.12131843, 2.64284021, 0.91353157])
kernel_variance: 0.0016885720945957914
likelihood_variance: 0.0009117151012575628
'predict': 0.097 seconds
total run time : 3.43 seconds
--------------------------------------------------
378 / 495
current local expert:
x y lon lat t is_in_ocean
388 -2.517486e+06 -1.686906e+06 -56.174925 62.588192 17913.0 True
'local_data_select': 0.003 seconds
number obs: 297
setting lengthscales to: [1. 1. 1.]
'__init__': 0.049 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 1.369 seconds
'get_parameters': 0.017 seconds
parameters:
lengthscales: array([4.3790631 , 9.95124538, 1.49116304])
kernel_variance: 0.0018967488691071087
likelihood_variance: 0.0008351515345545707
'predict': 0.185 seconds
total run time : 3.50 seconds
--------------------------------------------------
379 / 495
current local expert:
x y lon lat t is_in_ocean
389 -2.492486e+06 -1.686906e+06 -55.909993 62.779652 17913.0 True
'local_data_select': 0.003 seconds
number obs: 294
setting lengthscales to: [1. 1. 1.]
'__init__': 0.040 seconds
'set_lengthscales_constraints': 0.012 seconds
**********
optimization failed!
'optimise_parameters': 1.354 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.62770337, 3.58921404, 0.95957234])
kernel_variance: 0.0013521757435139999
likelihood_variance: 0.0008315270350966569
'predict': 0.088 seconds
total run time : 3.37 seconds
--------------------------------------------------
380 / 495
current local expert:
x y lon lat t is_in_ocean
390 -2.467486e+06 -1.686906e+06 -55.641391 62.970428 17913.0 True
'local_data_select': 0.003 seconds
number obs: 277
setting lengthscales to: [1. 1. 1.]
'__init__': 0.046 seconds
'set_lengthscales_constraints': 0.018 seconds
**********
optimization failed!
'optimise_parameters': 1.372 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.73267339, 2.67958905, 0.88341693])
kernel_variance: 0.0012937710482951782
likelihood_variance: 0.0008099280242152
'predict': 0.111 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 4.06 seconds
--------------------------------------------------
381 / 495
current local expert:
x y lon lat t is_in_ocean
391 -2.442486e+06 -1.686906e+06 -55.369054 63.160507 17913.0 True
'local_data_select': 0.008 seconds
number obs: 279
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.862 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([2.62407943, 4.22140784, 1.09115385])
kernel_variance: 0.001404652376679276
likelihood_variance: 0.0007565804588225481
'predict': 0.135 seconds
total run time : 4.24 seconds
--------------------------------------------------
382 / 495
current local expert:
x y lon lat t is_in_ocean
392 -2.417486e+06 -1.686906e+06 -55.092916 63.34988 17913.0 True
'local_data_select': 0.003 seconds
number obs: 257
setting lengthscales to: [1. 1. 1.]
'__init__': 0.050 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.850 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.39462166, 3.75360286, 1.4513456 ])
kernel_variance: 0.0012863478847930624
likelihood_variance: 0.0007003276564619959
'predict': 0.098 seconds
total run time : 2.97 seconds
--------------------------------------------------
383 / 495
current local expert:
x y lon lat t is_in_ocean
393 -2.392486e+06 -1.686906e+06 -54.81291 63.538534 17913.0 True
'local_data_select': 0.003 seconds
number obs: 224
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.721 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([2.57722833, 3.59150529, 3.71155998])
kernel_variance: 0.0012769814642442066
likelihood_variance: 0.0007163680517869698
'predict': 0.116 seconds
total run time : 2.85 seconds
--------------------------------------------------
384 / 495
current local expert:
x y lon lat t is_in_ocean
394 -2.367486e+06 -1.686906e+06 -54.528969 63.726456 17913.0 True
'local_data_select': 0.003 seconds
number obs: 193
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.012 seconds
**********
optimization failed!
'optimise_parameters': 0.805 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.47132912, 3.4295769 , 3.76638323])
kernel_variance: 0.001235346650873897
likelihood_variance: 0.0006928401051754051
'predict': 0.073 seconds
total run time : 2.87 seconds
--------------------------------------------------
385 / 495
current local expert:
x y lon lat t is_in_ocean
395 -2.342486e+06 -1.686906e+06 -54.241022 63.913635 17913.0 True
'local_data_select': 0.003 seconds
number obs: 144
setting lengthscales to: [1. 1. 1.]
'__init__': 0.054 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 0.848 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([2.12329594, 3.19578035, 3.41752254])
kernel_variance: 0.0012219280851585154
likelihood_variance: 0.000751414070592439
'predict': 0.107 seconds
total run time : 3.16 seconds
--------------------------------------------------
386 / 495
current local expert:
x y lon lat t is_in_ocean
396 -2.867486e+06 -1.661906e+06 -59.904749 59.958612 17913.0 True
'local_data_select': 0.002 seconds
number obs: 60
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.010 seconds
**********
optimization failed!
'optimise_parameters': 0.460 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([5.98441193e+01, 2.04144331e+01, 1.04528016e-02])
kernel_variance: 0.003218922590831933
likelihood_variance: 0.0014025765170279452
'predict': 0.084 seconds
total run time : 2.52 seconds
--------------------------------------------------
387 / 495
current local expert:
x y lon lat t is_in_ocean
397 -2.842486e+06 -1.661906e+06 -59.686609 60.159348 17913.0 True
'local_data_select': 0.007 seconds
number obs: 102
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.498 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([1.52467303e+00, 3.99694751e+01, 3.99676749e-02])
kernel_variance: 0.0017557851810379372
likelihood_variance: 0.0007719508590962543
'predict': 0.101 seconds
total run time : 2.59 seconds
--------------------------------------------------
388 / 495
current local expert:
x y lon lat t is_in_ocean
398 -2.817486e+06 -1.661906e+06 -59.465592 60.359535 17913.0 True
'local_data_select': 0.003 seconds
number obs: 152
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.712 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([2.05131495e+00, 4.35039986e+01, 3.97241569e-02])
kernel_variance: 0.0025025766061207632
likelihood_variance: 0.0006276592492238038
'predict': 0.083 seconds
total run time : 2.77 seconds
--------------------------------------------------
389 / 495
current local expert:
x y lon lat t is_in_ocean
399 -2.792486e+06 -1.661906e+06 -59.241645 60.559165 17913.0 True
'local_data_select': 0.004 seconds
number obs: 178
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.726 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([3.40619289e+00, 5.07697163e+01, 4.15081725e-02])
kernel_variance: 0.00469444523619023
likelihood_variance: 0.0007329904334081534
'predict': 0.101 seconds
total run time : 3.29 seconds
--------------------------------------------------
390 / 495
current local expert:
x y lon lat t is_in_ocean
400 -2.767486e+06 -1.661906e+06 -59.014717 60.758232 17913.0 True
'local_data_select': 0.002 seconds
number obs: 208
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.034 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([ 2.50675072, 59.98978247, 0.36770325])
kernel_variance: 0.0021813254527285513
likelihood_variance: 0.001035959465660847
'predict': 0.105 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.73 seconds
--------------------------------------------------
391 / 495
current local expert:
x y lon lat t is_in_ocean
401 -2.742486e+06 -1.661906e+06 -58.784756 60.956728 17913.0 True
'local_data_select': 0.003 seconds
number obs: 228
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.013 seconds
**********
optimization failed!
'optimise_parameters': 0.986 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 4.0459191 , 59.92533338, 0.41583815])
kernel_variance: 0.002458113152262813
likelihood_variance: 0.0010315596573866226
'predict': 0.093 seconds
total run time : 3.10 seconds
--------------------------------------------------
392 / 495
current local expert:
x y lon lat t is_in_ocean
402 -2.717486e+06 -1.661906e+06 -58.551708 61.154643 17913.0 True
'local_data_select': 0.002 seconds
number obs: 228
setting lengthscales to: [1. 1. 1.]
'__init__': 0.057 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 0.935 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([ 4.3008106 , 59.98922338, 0.7002133 ])
kernel_variance: 0.0030260755409798707
likelihood_variance: 0.0007558674710747132
'predict': 0.114 seconds
total run time : 3.18 seconds
--------------------------------------------------
393 / 495
current local expert:
x y lon lat t is_in_ocean
403 -2.692486e+06 -1.661906e+06 -58.315518 61.35197 17913.0 True
'local_data_select': 0.006 seconds
number obs: 244
setting lengthscales to: [1. 1. 1.]
'__init__': 0.048 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.650 seconds
'get_parameters': 0.017 seconds
parameters:
lengthscales: array([ 4.01134185, 59.99040145, 0.76524518])
kernel_variance: 0.0018821130850988916
likelihood_variance: 0.0007086826182974847
'predict': 0.174 seconds
total run time : 3.81 seconds
--------------------------------------------------
394 / 495
current local expert:
x y lon lat t is_in_ocean
404 -2.667486e+06 -1.661906e+06 -58.076131 61.548701 17913.0 True
'local_data_select': 0.002 seconds
number obs: 230
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.069 seconds
'get_parameters': 0.012 seconds
parameters:
lengthscales: array([4.46521268, 7.3948441 , 0.94377401])
kernel_variance: 0.0019965539106867555
likelihood_variance: 0.0005960503727388427
'predict': 0.134 seconds
total run time : 3.16 seconds
--------------------------------------------------
395 / 495
current local expert:
x y lon lat t is_in_ocean
405 -2.642486e+06 -1.661906e+06 -57.83349 61.744825 17913.0 True
'local_data_select': 0.003 seconds
number obs: 243
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.732 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([5.53817445, 5.62177685, 1.00055111])
kernel_variance: 0.001810890248249947
likelihood_variance: 0.000721773292656044
'predict': 0.099 seconds
total run time : 2.71 seconds
--------------------------------------------------
396 / 495
current local expert:
x y lon lat t is_in_ocean
406 -2.617486e+06 -1.661906e+06 -57.587536 61.940335 17913.0 True
'local_data_select': 0.004 seconds
number obs: 265
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.178 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([5.67389176, 6.15437926, 1.03577601])
kernel_variance: 0.0021597694548956183
likelihood_variance: 0.0007789440806553003
'predict': 0.105 seconds
total run time : 3.31 seconds
--------------------------------------------------
397 / 495
current local expert:
x y lon lat t is_in_ocean
407 -2.592486e+06 -1.661906e+06 -57.33821 62.13522 17913.0 True
'local_data_select': 0.003 seconds
number obs: 269
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.846 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([8.01086731, 6.95021864, 0.76293809])
kernel_variance: 0.0021529695662017715
likelihood_variance: 0.0007204574758282813
'predict': 0.078 seconds
total run time : 4.13 seconds
--------------------------------------------------
398 / 495
current local expert:
x y lon lat t is_in_ocean
408 -2.567486e+06 -1.661906e+06 -57.085454 62.329471 17913.0 True
'local_data_select': 0.004 seconds
number obs: 278
setting lengthscales to: [1. 1. 1.]
'__init__': 0.050 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.992 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([5.70266283, 4.15199399, 0.97443026])
kernel_variance: 0.0018687397337855233
likelihood_variance: 0.0007102025581668375
'predict': 0.113 seconds
total run time : 3.11 seconds
--------------------------------------------------
399 / 495
current local expert:
x y lon lat t is_in_ocean
409 -2.542486e+06 -1.661906e+06 -56.829204 62.523079 17913.0 True
'local_data_select': 0.005 seconds
number obs: 285
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.014 seconds
**********
optimization failed!
'optimise_parameters': 1.198 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([4.75737398, 3.45851198, 1.10913179])
kernel_variance: 0.0015144163121223071
likelihood_variance: 0.0007329669791767764
'predict': 0.103 seconds
total run time : 3.35 seconds
--------------------------------------------------
400 / 495
current local expert:
x y lon lat t is_in_ocean
410 -2.517486e+06 -1.661906e+06 -56.569399 62.716033 17913.0 True
'local_data_select': 0.003 seconds
number obs: 279
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.007 seconds
'optimise_parameters': 1.820 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([4.0622609 , 3.04191629, 1.07382872])
kernel_variance: 0.0015249445266650808
likelihood_variance: 0.0006371453680229498
'predict': 0.129 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 4.70 seconds
--------------------------------------------------
401 / 495
current local expert:
x y lon lat t is_in_ocean
411 -2.492486e+06 -1.661906e+06 -56.305976 62.908322 17913.0 True
'local_data_select': 0.004 seconds
number obs: 297
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.017 seconds
**********
optimization failed!
'optimise_parameters': 1.493 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.00220933, 3.02638637, 0.98645504])
kernel_variance: 0.0013659477070240554
likelihood_variance: 0.0006248314189081296
'predict': 0.118 seconds
total run time : 3.70 seconds
--------------------------------------------------
402 / 495
current local expert:
x y lon lat t is_in_ocean
412 -2.467486e+06 -1.661906e+06 -56.038868 63.099936 17913.0 True
'local_data_select': 0.005 seconds
number obs: 285
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.367 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.01531202, 2.9294893 , 0.90832165])
kernel_variance: 0.0011856693376300542
likelihood_variance: 0.0006978625811168618
'predict': 0.118 seconds
total run time : 3.55 seconds
--------------------------------------------------
403 / 495
current local expert:
x y lon lat t is_in_ocean
413 -2.442486e+06 -1.661906e+06 -55.768012 63.290864 17913.0 True
'local_data_select': 0.003 seconds
number obs: 272
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.305 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([2.46752121, 2.18606466, 0.85916481])
kernel_variance: 0.0011760346333946103
likelihood_variance: 0.0007127111487367597
'predict': 0.162 seconds
total run time : 4.05 seconds
--------------------------------------------------
404 / 495
current local expert:
x y lon lat t is_in_ocean
414 -2.417486e+06 -1.661906e+06 -55.493338 63.481094 17913.0 True
'local_data_select': 0.003 seconds
number obs: 261
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 0.959 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([2.15914558, 3.31197538, 1.01172722])
kernel_variance: 0.0011652203242521865
likelihood_variance: 0.0007256947342156858
'predict': 0.112 seconds
total run time : 3.10 seconds
--------------------------------------------------
405 / 495
current local expert:
x y lon lat t is_in_ocean
415 -2.392486e+06 -1.661906e+06 -55.21478 63.670615 17913.0 True
'local_data_select': 0.003 seconds
number obs: 214
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.774 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([2.33436335, 3.1394838 , 3.35203871])
kernel_variance: 0.001156097643943085
likelihood_variance: 0.000729100592485119
'predict': 0.109 seconds
total run time : 2.97 seconds
--------------------------------------------------
406 / 495
current local expert:
x y lon lat t is_in_ocean
416 -2.367486e+06 -1.661906e+06 -54.932267 63.859414 17913.0 True
'local_data_select': 0.003 seconds
number obs: 175
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.639 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.03279862, 3.48902765, 3.15321375])
kernel_variance: 0.0010537220422033725
likelihood_variance: 0.0007298459698344409
'predict': 0.090 seconds
total run time : 2.81 seconds
--------------------------------------------------
407 / 495
current local expert:
x y lon lat t is_in_ocean
417 -2.342486e+06 -1.661906e+06 -54.645728 64.04748 17913.0 True
'local_data_select': 0.005 seconds
number obs: 139
setting lengthscales to: [1. 1. 1.]
'__init__': 0.053 seconds
'set_lengthscales_constraints': 0.016 seconds
'optimise_parameters': 0.841 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([1.85117171, 3.47440827, 3.52356078])
kernel_variance: 0.0011015202224370297
likelihood_variance: 0.0007454251465409867
'predict': 0.134 seconds
total run time : 3.44 seconds
--------------------------------------------------
408 / 495
current local expert:
x y lon lat t is_in_ocean
418 -2.867486e+06 -1.636906e+06 -60.28009 60.074435 17913.0 True
'local_data_select': 0.004 seconds
number obs: 55
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.013 seconds
'optimise_parameters': 0.578 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([ 1.45398399, 59.99745286, 1.82419405])
kernel_variance: 0.0026672365921342252
likelihood_variance: 0.0010969075839172278
'predict': 0.085 seconds
total run time : 2.55 seconds
--------------------------------------------------
409 / 495
current local expert:
x y lon lat t is_in_ocean
419 -2.842486e+06 -1.636906e+06 -60.063596 60.275875 17913.0 True
'local_data_select': 0.002 seconds
number obs: 83
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.464 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.04550497e+00, 5.57569074e+01, 1.00000000e-08])
kernel_variance: 0.0073762509334078154
likelihood_variance: 0.0007046698166861187
'predict': 0.085 seconds
total run time : 2.52 seconds
--------------------------------------------------
410 / 495
current local expert:
x y lon lat t is_in_ocean
420 -2.817486e+06 -1.636906e+06 -59.844224 60.476775 17913.0 True
'local_data_select': 0.004 seconds
number obs: 115
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.606 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([4.68215795e+00, 4.51831394e+01, 9.62671753e-03])
kernel_variance: 0.0010380003796908622
likelihood_variance: 0.0015418189764276119
'predict': 0.085 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.42 seconds
--------------------------------------------------
411 / 495
current local expert:
x y lon lat t is_in_ocean
421 -2.792486e+06 -1.636906e+06 -59.621921 60.677127 17913.0 True
'local_data_select': 0.003 seconds
number obs: 150
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.017 seconds
**********
optimization failed!
'optimise_parameters': 1.009 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([ 3.11448906, 49.51609915, 0.04995545])
kernel_variance: 0.0012737923240723748
likelihood_variance: 0.0013443535156269664
'predict': 0.097 seconds
total run time : 3.57 seconds
--------------------------------------------------
412 / 495
current local expert:
x y lon lat t is_in_ocean
422 -2.767486e+06 -1.636906e+06 -59.396636 60.876924 17913.0 True
'local_data_select': 0.003 seconds
number obs: 171
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.599 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([1.01450108e+01, 6.00000000e+01, 1.00000000e-08])
kernel_variance: 0.0029799782066730635
likelihood_variance: 0.0017849540482908857
'predict': 0.093 seconds
total run time : 2.66 seconds
--------------------------------------------------
413 / 495
current local expert:
x y lon lat t is_in_ocean
423 -2.742486e+06 -1.636906e+06 -59.168315 61.076158 17913.0 True
'local_data_select': 0.003 seconds
number obs: 196
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.909 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 3.78790426, 59.99367492, 1.03045067])
kernel_variance: 0.0016304335472477024
likelihood_variance: 0.0011727331274837354
'predict': 0.089 seconds
total run time : 3.01 seconds
--------------------------------------------------
414 / 495
current local expert:
x y lon lat t is_in_ocean
424 -2.717486e+06 -1.636906e+06 -58.936905 61.27482 17913.0 True
'local_data_select': 0.004 seconds
number obs: 216
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.185 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([ 4.6452281 , 59.99882463, 1.8090801 ])
kernel_variance: 0.0016697638328857904
likelihood_variance: 0.001125903130735954
'predict': 0.083 seconds
total run time : 3.30 seconds
--------------------------------------------------
415 / 495
current local expert:
x y lon lat t is_in_ocean
425 -2.692486e+06 -1.636906e+06 -58.702349 61.472902 17913.0 True
'local_data_select': 0.003 seconds
number obs: 237
setting lengthscales to: [1. 1. 1.]
'__init__': 0.068 seconds
'set_lengthscales_constraints': 0.020 seconds
'optimise_parameters': 1.866 seconds
'get_parameters': 0.012 seconds
parameters:
lengthscales: array([ 4.34593471, 59.99755329, 1.51593488])
kernel_variance: 0.0022986792994582573
likelihood_variance: 0.0010262347319465839
'predict': 0.140 seconds
total run time : 3.95 seconds
--------------------------------------------------
416 / 495
current local expert:
x y lon lat t is_in_ocean
426 -2.667486e+06 -1.636906e+06 -58.46459 61.670397 17913.0 True
'local_data_select': 0.003 seconds
number obs: 239
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 0.931 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 6.60513584, 24.2066052 , 1.2068255 ])
kernel_variance: 0.0018522281915312345
likelihood_variance: 0.0006572663979961873
'predict': 0.093 seconds
total run time : 2.99 seconds
--------------------------------------------------
417 / 495
current local expert:
x y lon lat t is_in_ocean
427 -2.642486e+06 -1.636906e+06 -58.223573 61.867295 17913.0 True
'local_data_select': 0.003 seconds
number obs: 225
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.013 seconds
'optimise_parameters': 0.803 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 7.92579086, 23.03724114, 1.18750932])
kernel_variance: 0.0021502622522232193
likelihood_variance: 0.0006693133042499924
'predict': 0.097 seconds
total run time : 2.91 seconds
--------------------------------------------------
418 / 495
current local expert:
x y lon lat t is_in_ocean
428 -2.617486e+06 -1.636906e+06 -57.979237 62.063586 17913.0 True
'local_data_select': 0.003 seconds
number obs: 238
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.020 seconds
**********
optimization failed!
'optimise_parameters': 1.264 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([ 6.58977051, 15.64271385, 1.29143832])
kernel_variance: 0.0031705685391669775
likelihood_variance: 0.0006153485090518671
'predict': 0.103 seconds
total run time : 3.62 seconds
--------------------------------------------------
419 / 495
current local expert:
x y lon lat t is_in_ocean
429 -2.592486e+06 -1.636906e+06 -57.731522 62.259263 17913.0 True
'local_data_select': 0.005 seconds
number obs: 252
setting lengthscales to: [1. 1. 1.]
'__init__': 0.057 seconds
'set_lengthscales_constraints': 0.023 seconds
**********
optimization failed!
'optimise_parameters': 1.551 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([6.79064713, 8.74664059, 0.75105056])
kernel_variance: 0.0018859786304849307
likelihood_variance: 0.0006406283956363598
'predict': 0.105 seconds
total run time : 3.77 seconds
--------------------------------------------------
420 / 495
current local expert:
x y lon lat t is_in_ocean
430 -2.567486e+06 -1.636906e+06 -57.480369 62.454315 17913.0 True
'local_data_select': 0.003 seconds
number obs: 250
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 1.035 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([5.17118142, 4.25404502, 1.26165774])
kernel_variance: 0.001623745820536636
likelihood_variance: 0.0006141703789857767
'predict': 0.134 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.75 seconds
--------------------------------------------------
421 / 495
current local expert:
x y lon lat t is_in_ocean
431 -2.542486e+06 -1.636906e+06 -57.225713 62.648732 17913.0 True
'local_data_select': 0.003 seconds
number obs: 257
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 0.924 seconds
'get_parameters': 0.014 seconds
parameters:
lengthscales: array([4.13343311, 3.56821269, 1.15442625])
kernel_variance: 0.0013576430509613738
likelihood_variance: 0.0006403597466119683
'predict': 0.110 seconds
total run time : 3.19 seconds
--------------------------------------------------
422 / 495
current local expert:
x y lon lat t is_in_ocean
432 -2.517486e+06 -1.636906e+06 -56.967492 62.842505 17913.0 True
'local_data_select': 0.005 seconds
number obs: 256
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 1.180 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([3.98907473, 3.47076941, 0.99629576])
kernel_variance: 0.0013080953339694203
likelihood_variance: 0.0005705648095369926
'predict': 0.137 seconds
total run time : 3.63 seconds
--------------------------------------------------
423 / 495
current local expert:
x y lon lat t is_in_ocean
433 -2.492486e+06 -1.636906e+06 -56.705642 63.035623 17913.0 True
'local_data_select': 0.003 seconds
number obs: 265
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 1.024 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.05606737, 2.92679416, 0.81226267])
kernel_variance: 0.0011602265831891093
likelihood_variance: 0.000563656868376394
'predict': 0.116 seconds
total run time : 3.21 seconds
--------------------------------------------------
424 / 495
current local expert:
x y lon lat t is_in_ocean
434 -2.467486e+06 -1.636906e+06 -56.440095 63.228075 17913.0 True
'local_data_select': 0.003 seconds
number obs: 275
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.087 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.9669746 , 2.1300406 , 0.72455226])
kernel_variance: 0.001022350167027428
likelihood_variance: 0.0006787369304492669
'predict': 0.104 seconds
total run time : 3.30 seconds
--------------------------------------------------
425 / 495
current local expert:
x y lon lat t is_in_ocean
435 -2.442486e+06 -1.636906e+06 -56.170786 63.41985 17913.0 True
'local_data_select': 0.003 seconds
number obs: 255
setting lengthscales to: [1. 1. 1.]
'__init__': 0.031 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.676 seconds
'get_parameters': 0.012 seconds
parameters:
lengthscales: array([2.47716649, 2.55459657, 0.83730703])
kernel_variance: 0.0010797536633167027
likelihood_variance: 0.0006685235574516185
'predict': 0.124 seconds
total run time : 3.03 seconds
--------------------------------------------------
426 / 495
current local expert:
x y lon lat t is_in_ocean
436 -2.417486e+06 -1.636906e+06 -55.897645 63.610937 17913.0 True
'local_data_select': 0.004 seconds
number obs: 247
setting lengthscales to: [1. 1. 1.]
'__init__': 0.056 seconds
'set_lengthscales_constraints': 0.019 seconds
'optimise_parameters': 1.612 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([2.11539301, 2.50429233, 0.95710553])
kernel_variance: 0.0010008685511652773
likelihood_variance: 0.0006548645289971558
'predict': 0.108 seconds
total run time : 3.85 seconds
--------------------------------------------------
427 / 495
current local expert:
x y lon lat t is_in_ocean
437 -2.392486e+06 -1.636906e+06 -55.620603 63.801325 17913.0 True
'local_data_select': 0.003 seconds
number obs: 213
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 0.722 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([2.1583417 , 3.71947758, 3.76930022])
kernel_variance: 0.0010119439428607831
likelihood_variance: 0.0007293084751575381
'predict': 0.104 seconds
total run time : 2.85 seconds
--------------------------------------------------
428 / 495
current local expert:
x y lon lat t is_in_ocean
438 -2.367486e+06 -1.636906e+06 -55.33959 63.991001 17913.0 True
'local_data_select': 0.003 seconds
number obs: 168
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.665 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([2.25413966, 3.86374724, 4.68294942])
kernel_variance: 0.0011352525663895917
likelihood_variance: 0.0007405477614111485
'predict': 0.083 seconds
total run time : 2.84 seconds
--------------------------------------------------
429 / 495
current local expert:
x y lon lat t is_in_ocean
439 -2.342486e+06 -1.636906e+06 -55.054532 64.179954 17913.0 True
'local_data_select': 0.003 seconds
number obs: 128
setting lengthscales to: [1. 1. 1.]
'__init__': 0.047 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.593 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([0.90116916, 4.19999947, 0.04490674])
kernel_variance: 0.0011329216213193506
likelihood_variance: 0.0003902208846624115
'predict': 0.076 seconds
total run time : 2.86 seconds
--------------------------------------------------
430 / 495
current local expert:
x y lon lat t is_in_ocean
440 -2.867486e+06 -1.611906e+06 -60.658261 60.188898 17913.0 True
'local_data_select': 0.003 seconds
number obs: 33
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.016 seconds
**********
optimization failed!
'optimise_parameters': 0.870 seconds
'get_parameters': 0.016 seconds
parameters:
lengthscales: array([ 1.33408569, 59.99139173, 1.46674764])
kernel_variance: 0.0034418649720913265
likelihood_variance: 0.0012013291008945806
'predict': 0.128 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.74 seconds
--------------------------------------------------
431 / 495
current local expert:
x y lon lat t is_in_ocean
441 -2.842486e+06 -1.611906e+06 -60.443462 60.391041 17913.0 True
'local_data_select': 0.003 seconds
number obs: 56
setting lengthscales to: [1. 1. 1.]
'__init__': 0.044 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.467 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([ 0.89595499, 55.04607054, 0.39094194])
kernel_variance: 0.002570557367167802
likelihood_variance: 0.001485809662750091
'predict': 0.072 seconds
total run time : 2.66 seconds
--------------------------------------------------
432 / 495
current local expert:
x y lon lat t is_in_ocean
442 -2.817486e+06 -1.611906e+06 -60.225786 60.592651 17913.0 True
'local_data_select': 0.003 seconds
number obs: 93
setting lengthscales to: [1. 1. 1.]
'__init__': 0.044 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.525 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 1.67234498, 59.96339413, 1.50140636])
kernel_variance: 0.0015184277412973956
likelihood_variance: 0.0015265982579813371
'predict': 0.086 seconds
total run time : 2.63 seconds
--------------------------------------------------
433 / 495
current local expert:
x y lon lat t is_in_ocean
443 -2.792486e+06 -1.611906e+06 -60.005179 60.793722 17913.0 True
'local_data_select': 0.005 seconds
number obs: 120
setting lengthscales to: [1. 1. 1.]
'__init__': 0.042 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.764 seconds
'get_parameters': 0.015 seconds
parameters:
lengthscales: array([ 3.12809236, 59.98247417, 1.01148596])
kernel_variance: 0.0015849263487273879
likelihood_variance: 0.001346091058682156
'predict': 0.093 seconds
total run time : 3.03 seconds
--------------------------------------------------
434 / 495
current local expert:
x y lon lat t is_in_ocean
444 -2.767486e+06 -1.611906e+06 -59.78159 60.994247 17913.0 True
'local_data_select': 0.002 seconds
number obs: 147
setting lengthscales to: [1. 1. 1.]
'__init__': 0.051 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.153 seconds
'get_parameters': 0.074 seconds
parameters:
lengthscales: array([3.92119071e+00, 3.89399419e+01, 6.77706961e-03])
kernel_variance: 0.001913607439629639
likelihood_variance: 0.0013742436567682345
'predict': 0.146 seconds
total run time : 3.42 seconds
--------------------------------------------------
435 / 495
current local expert:
x y lon lat t is_in_ocean
445 -2.742486e+06 -1.611906e+06 -59.554964 61.194216 17913.0 True
'local_data_select': 0.003 seconds
number obs: 161
setting lengthscales to: [1. 1. 1.]
'__init__': 0.040 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.734 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.41729804e+00, 4.45906666e+01, 1.43293525e-02])
kernel_variance: 0.0015277644134472454
likelihood_variance: 0.0013536053449515505
'predict': 0.079 seconds
total run time : 2.87 seconds
--------------------------------------------------
436 / 495
current local expert:
x y lon lat t is_in_ocean
446 -2.717486e+06 -1.611906e+06 -59.325246 61.393623 17913.0 True
'local_data_select': 0.003 seconds
number obs: 185
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.662 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 3.37396299, 59.9988878 , 2.29383045])
kernel_variance: 0.0017056384028949702
likelihood_variance: 0.0012228413062127382
'predict': 0.103 seconds
total run time : 2.93 seconds
--------------------------------------------------
437 / 495
current local expert:
x y lon lat t is_in_ocean
447 -2.692486e+06 -1.611906e+06 -59.09238 61.592459 17913.0 True
'local_data_select': 0.003 seconds
number obs: 193
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.772 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 3.58986509, 59.98525206, 3.37435083])
kernel_variance: 0.0020215039879752645
likelihood_variance: 0.0012202684334974144
'predict': 0.093 seconds
total run time : 3.00 seconds
--------------------------------------------------
438 / 495
current local expert:
x y lon lat t is_in_ocean
448 -2.667486e+06 -1.611906e+06 -58.856309 61.790716 17913.0 True
'local_data_select': 0.003 seconds
number obs: 203
setting lengthscales to: [1. 1. 1.]
'__init__': 0.058 seconds
'set_lengthscales_constraints': 0.020 seconds
'optimise_parameters': 1.209 seconds
'get_parameters': 0.012 seconds
parameters:
lengthscales: array([ 7.03757305, 59.9980698 , 1.66774687])
kernel_variance: 0.0014843139071370381
likelihood_variance: 0.000802848375951314
'predict': 0.121 seconds
total run time : 3.64 seconds
--------------------------------------------------
439 / 495
current local expert:
x y lon lat t is_in_ocean
449 -2.642486e+06 -1.611906e+06 -58.616974 61.988386 17913.0 True
'local_data_select': 0.003 seconds
number obs: 196
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.012 seconds
'optimise_parameters': 0.710 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 6.53145068, 59.99610848, 1.80078467])
kernel_variance: 0.0023564838561330737
likelihood_variance: 0.0006398638960259932
'predict': 0.090 seconds
total run time : 2.95 seconds
--------------------------------------------------
440 / 495
current local expert:
x y lon lat t is_in_ocean
450 -2.617486e+06 -1.611906e+06 -58.374316 62.185458 17913.0 True
'local_data_select': 0.003 seconds
number obs: 196
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.645 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 9.80419425, 11.63615397, 2.60545341])
kernel_variance: 0.002182045990640739
likelihood_variance: 0.0006350958062882285
'predict': 0.076 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.48 seconds
--------------------------------------------------
441 / 495
current local expert:
x y lon lat t is_in_ocean
451 -2.592486e+06 -1.611906e+06 -58.128274 62.381924 17913.0 True
'local_data_select': 0.003 seconds
number obs: 212
setting lengthscales to: [1. 1. 1.]
'__init__': 0.040 seconds
'set_lengthscales_constraints': 0.014 seconds
**********
optimization failed!
'optimise_parameters': 0.853 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 6.7063912 , 10.51966264, 1.15935876])
kernel_variance: 0.001945130449915233
likelihood_variance: 0.0005878657199234806
'predict': 0.085 seconds
total run time : 3.42 seconds
--------------------------------------------------
442 / 495
current local expert:
x y lon lat t is_in_ocean
452 -2.567486e+06 -1.611906e+06 -57.878785 62.577775 17913.0 True
'local_data_select': 0.003 seconds
number obs: 227
setting lengthscales to: [1. 1. 1.]
'__init__': 0.047 seconds
'set_lengthscales_constraints': 0.012 seconds
**********
optimization failed!
'optimise_parameters': 0.959 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.70301383, 5.21806727, 1.41104283])
kernel_variance: 0.0015949187242860722
likelihood_variance: 0.0005654047019754044
'predict': 0.095 seconds
total run time : 3.14 seconds
--------------------------------------------------
443 / 495
current local expert:
x y lon lat t is_in_ocean
453 -2.542486e+06 -1.611906e+06 -57.625787 62.773001 17913.0 True
'local_data_select': 0.003 seconds
number obs: 220
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 0.998 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.90937088, 3.8654082 , 0.83692518])
kernel_variance: 0.0012966950165633005
likelihood_variance: 0.0005980328830314021
'predict': 0.080 seconds
total run time : 3.14 seconds
--------------------------------------------------
444 / 495
current local expert:
x y lon lat t is_in_ocean
454 -2.517486e+06 -1.611906e+06 -57.369215 62.967591 17913.0 True
'local_data_select': 0.004 seconds
number obs: 235
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 1.239 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([4.53212285, 2.53875872, 0.13417896])
kernel_variance: 0.0011237829684811532
likelihood_variance: 0.0005391790361832886
'predict': 0.097 seconds
total run time : 3.34 seconds
--------------------------------------------------
445 / 495
current local expert:
x y lon lat t is_in_ocean
455 -2.492486e+06 -1.611906e+06 -57.109003 63.161536 17913.0 True
'local_data_select': 0.003 seconds
number obs: 226
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.574 seconds
'get_parameters': 0.015 seconds
parameters:
lengthscales: array([3.95091692, 2.18645095, 0.11966887])
kernel_variance: 0.0010059200967491512
likelihood_variance: 0.0005824906921543859
'predict': 0.163 seconds
total run time : 3.87 seconds
--------------------------------------------------
446 / 495
current local expert:
x y lon lat t is_in_ocean
456 -2.467486e+06 -1.611906e+06 -56.845085 63.354825 17913.0 True
'local_data_select': 0.002 seconds
number obs: 235
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 1.183 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([2.64661029, 2.18790025, 0.12979386])
kernel_variance: 0.0010272601280964919
likelihood_variance: 0.0005411816734105052
'predict': 0.085 seconds
total run time : 3.35 seconds
--------------------------------------------------
447 / 495
current local expert:
x y lon lat t is_in_ocean
457 -2.442486e+06 -1.611906e+06 -56.577391 63.547448 17913.0 True
'local_data_select': 0.002 seconds
number obs: 223
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.015 seconds
**********
optimization failed!
'optimise_parameters': 1.055 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([2.37594613, 1.73968646, 0.12806369])
kernel_variance: 0.0010940732906156501
likelihood_variance: 0.0004625832765123479
'predict': 0.122 seconds
total run time : 3.22 seconds
--------------------------------------------------
448 / 495
current local expert:
x y lon lat t is_in_ocean
458 -2.417486e+06 -1.611906e+06 -56.305853 63.739392 17913.0 True
'local_data_select': 0.002 seconds
number obs: 198
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.687 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([1.61461574, 1.95307652, 0.82006006])
kernel_variance: 0.0008968040874595301
likelihood_variance: 0.0004183040215034876
'predict': 0.093 seconds
total run time : 2.78 seconds
--------------------------------------------------
449 / 495
current local expert:
x y lon lat t is_in_ocean
459 -2.392486e+06 -1.611906e+06 -56.030399 63.930646 17913.0 True
'local_data_select': 0.003 seconds
number obs: 177
setting lengthscales to: [1. 1. 1.]
'__init__': 0.044 seconds
'set_lengthscales_constraints': 0.013 seconds
'optimise_parameters': 0.997 seconds
'get_parameters': 0.012 seconds
parameters:
lengthscales: array([1.22950177, 2.12206064, 2.15308559])
kernel_variance: 0.0009106193807658596
likelihood_variance: 0.0004669583185873366
'predict': 0.122 seconds
total run time : 3.23 seconds
--------------------------------------------------
450 / 495
current local expert:
x y lon lat t is_in_ocean
460 -2.367486e+06 -1.611906e+06 -55.750957 64.121199 17913.0 True
'local_data_select': 0.002 seconds
number obs: 142
setting lengthscales to: [1. 1. 1.]
'__init__': 0.028 seconds
'set_lengthscales_constraints': 0.007 seconds
'optimise_parameters': 0.467 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([8.06544608e-01, 1.22403026e-08, 1.00000000e-08])
kernel_variance: 0.0007246198431521897
likelihood_variance: 0.00019098120681240862
'predict': 0.089 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.23 seconds
--------------------------------------------------
451 / 495
current local expert:
x y lon lat t is_in_ocean
461 -2.342486e+06 -1.611906e+06 -55.467453 64.311038 17913.0 True
'local_data_select': 0.003 seconds
number obs: 108
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.548 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([1.96397835e+00, 8.00810162e+00, 2.70670995e-07])
kernel_variance: 0.004936624886558249
likelihood_variance: 1.0789444962734316e-06
'predict': 0.075 seconds
total run time : 2.74 seconds
--------------------------------------------------
452 / 495
current local expert:
x y lon lat t is_in_ocean
462 -2.867486e+06 -1.586906e+06 -61.039258 60.301988 17913.0 True
'local_data_select': 0.003 seconds
number obs: 17
setting lengthscales to: [1. 1. 1.]
'__init__': 0.041 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 0.495 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([1.44181533e+00, 5.78139880e+01, 2.58577032e-05])
kernel_variance: 0.005002523719253376
likelihood_variance: 0.002347924110850838
'predict': 0.075 seconds
total run time : 2.65 seconds
--------------------------------------------------
453 / 495
current local expert:
x y lon lat t is_in_ocean
463 -2.842486e+06 -1.586906e+06 -60.826206 60.504831 17913.0 True
'local_data_select': 0.008 seconds
number obs: 41
setting lengthscales to: [1. 1. 1.]
'__init__': 0.053 seconds
'set_lengthscales_constraints': 0.015 seconds
'optimise_parameters': 0.769 seconds
'get_parameters': 0.014 seconds
parameters:
lengthscales: array([4.74913004e-01, 2.92704759e-05, 1.07808297e+00])
kernel_variance: 0.004089425530609028
likelihood_variance: 1.3060306766394774e-05
'predict': 0.136 seconds
total run time : 3.15 seconds
--------------------------------------------------
454 / 495
current local expert:
x y lon lat t is_in_ocean
464 -2.817486e+06 -1.586906e+06 -60.610277 60.707149 17913.0 True
'local_data_select': 0.002 seconds
number obs: 55
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.010 seconds
**********
optimization failed!
'optimise_parameters': 0.438 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 0.47903133, 26.60292804, 0.34575856])
kernel_variance: 0.0016255567826742887
likelihood_variance: 0.0019086191068946195
'predict': 0.081 seconds
total run time : 2.69 seconds
--------------------------------------------------
455 / 495
current local expert:
x y lon lat t is_in_ocean
465 -2.792486e+06 -1.586906e+06 -60.39142 60.908937 17913.0 True
'local_data_select': 0.002 seconds
number obs: 78
setting lengthscales to: [1. 1. 1.]
'__init__': 0.038 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.497 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 2.33796841, 59.96903093, 1.25284833])
kernel_variance: 0.001683067297277442
likelihood_variance: 0.001873481763989505
'predict': 0.082 seconds
total run time : 2.72 seconds
--------------------------------------------------
456 / 495
current local expert:
x y lon lat t is_in_ocean
466 -2.767486e+06 -1.586906e+06 -60.169581 61.110186 17913.0 True
'local_data_select': 0.003 seconds
number obs: 106
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 0.629 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.59126430e+00, 5.92463358e+01, 2.92478929e-04])
kernel_variance: 0.0013780774384487663
likelihood_variance: 0.0017626137559596682
'predict': 0.068 seconds
total run time : 2.74 seconds
--------------------------------------------------
457 / 495
current local expert:
x y lon lat t is_in_ocean
467 -2.742486e+06 -1.586906e+06 -59.944704 61.310889 17913.0 True
'local_data_select': 0.003 seconds
number obs: 122
setting lengthscales to: [1. 1. 1.]
'__init__': 0.046 seconds
'set_lengthscales_constraints': 0.015 seconds
**********
optimization failed!
'optimise_parameters': 0.874 seconds
'get_parameters': 0.012 seconds
parameters:
lengthscales: array([ 5.13423259, 59.99812563, 1.51399985])
kernel_variance: 0.0023895842961383306
likelihood_variance: 0.0015516143403993701
'predict': 0.115 seconds
total run time : 3.33 seconds
--------------------------------------------------
458 / 495
current local expert:
x y lon lat t is_in_ocean
468 -2.717486e+06 -1.586906e+06 -59.716736 61.511039 17913.0 True
'local_data_select': 0.002 seconds
number obs: 139
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.618 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 2.53340954, 59.99960403, 2.67569885])
kernel_variance: 0.0018828421459397998
likelihood_variance: 0.0013904291127229856
'predict': 0.095 seconds
total run time : 2.85 seconds
--------------------------------------------------
459 / 495
current local expert:
x y lon lat t is_in_ocean
469 -2.692486e+06 -1.586906e+06 -59.485617 61.710626 17913.0 True
'local_data_select': 0.003 seconds
number obs: 162
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.553 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([ 5.1723747 , 59.96607766, 3.0992283 ])
kernel_variance: 0.001578928322032824
likelihood_variance: 0.0012075816912033531
'predict': 0.083 seconds
total run time : 2.68 seconds
--------------------------------------------------
460 / 495
current local expert:
x y lon lat t is_in_ocean
470 -2.667486e+06 -1.586906e+06 -59.251292 61.909643 17913.0 True
'local_data_select': 0.003 seconds
number obs: 169
setting lengthscales to: [1. 1. 1.]
'__init__': 0.036 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 0.739 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 7.29856854, 59.99018672, 0.90383372])
kernel_variance: 0.001302367441182494
likelihood_variance: 0.0010480779685435651
'predict': 0.080 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.92 seconds
--------------------------------------------------
461 / 495
current local expert:
x y lon lat t is_in_ocean
471 -2.642486e+06 -1.586906e+06 -59.013699 62.108082 17913.0 True
'local_data_select': 0.006 seconds
number obs: 172
setting lengthscales to: [1. 1. 1.]
'__init__': 0.056 seconds
'set_lengthscales_constraints': 0.013 seconds
**********
optimization failed!
'optimise_parameters': 1.187 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([ 8.69507023, 59.97749586, 0.61780944])
kernel_variance: 0.0013771540685234074
likelihood_variance: 0.0006384007383457433
'predict': 0.111 seconds
total run time : 3.42 seconds
--------------------------------------------------
462 / 495
current local expert:
x y lon lat t is_in_ocean
472 -2.617486e+06 -1.586906e+06 -58.77278 62.305933 17913.0 True
'local_data_select': 0.002 seconds
number obs: 162
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.014 seconds
'optimise_parameters': 0.582 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([9.70591254e+00, 5.66214699e+01, 3.93889627e-02])
kernel_variance: 0.001388677289527771
likelihood_variance: 0.0006715014515426138
'predict': 0.089 seconds
total run time : 2.65 seconds
--------------------------------------------------
463 / 495
current local expert:
x y lon lat t is_in_ocean
473 -2.592486e+06 -1.586906e+06 -58.528471 62.503187 17913.0 True
'local_data_select': 0.002 seconds
number obs: 172
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.015 seconds
'optimise_parameters': 0.930 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([12.65735228, 9.22265171, 0.48751472])
kernel_variance: 0.001709779842972213
likelihood_variance: 0.000634868105792636
'predict': 0.081 seconds
total run time : 2.94 seconds
--------------------------------------------------
464 / 495
current local expert:
x y lon lat t is_in_ocean
474 -2.567486e+06 -1.586906e+06 -58.280711 62.699835 17913.0 True
'local_data_select': 0.003 seconds
number obs: 182
setting lengthscales to: [1. 1. 1.]
'__init__': 0.034 seconds
'set_lengthscales_constraints': 0.011 seconds
**********
optimization failed!
'optimise_parameters': 0.713 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([3.794965 , 4.33652967, 0.14648879])
kernel_variance: 0.0014808262959220251
likelihood_variance: 0.0005387453983601634
'predict': 0.106 seconds
total run time : 2.96 seconds
--------------------------------------------------
465 / 495
current local expert:
x y lon lat t is_in_ocean
475 -2.542486e+06 -1.586906e+06 -58.029435 62.895868 17913.0 True
'local_data_select': 0.004 seconds
number obs: 179
setting lengthscales to: [1. 1. 1.]
'__init__': 0.053 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.986 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([4.06818401, 3.65272579, 0.139748 ])
kernel_variance: 0.0012887028993765236
likelihood_variance: 0.0005375132090923791
'predict': 0.131 seconds
total run time : 3.21 seconds
--------------------------------------------------
466 / 495
current local expert:
x y lon lat t is_in_ocean
476 -2.517486e+06 -1.586906e+06 -57.774577 63.091275 17913.0 True
'local_data_select': 0.003 seconds
number obs: 184
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.782 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([3.97741826, 3.09626953, 0.12745345])
kernel_variance: 0.0011083055544942315
likelihood_variance: 0.0005025826857369257
'predict': 0.104 seconds
total run time : 2.96 seconds
--------------------------------------------------
467 / 495
current local expert:
x y lon lat t is_in_ocean
477 -2.492486e+06 -1.586906e+06 -57.516071 63.286046 17913.0 True
'local_data_select': 0.004 seconds
number obs: 189
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.632 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([3.47912163, 2.46613267, 0.14353171])
kernel_variance: 0.0010184130808461736
likelihood_variance: 0.0005128220313857285
'predict': 0.069 seconds
total run time : 2.71 seconds
--------------------------------------------------
468 / 495
current local expert:
x y lon lat t is_in_ocean
478 -2.467486e+06 -1.586906e+06 -57.253849 63.480171 17913.0 True
'local_data_select': 0.002 seconds
number obs: 192
setting lengthscales to: [1. 1. 1.]
'__init__': 0.031 seconds
'set_lengthscales_constraints': 0.007 seconds
**********
optimization failed!
'optimise_parameters': 0.918 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([4.45045541, 2.3675543 , 0.15553225])
kernel_variance: 0.001119066020387362
likelihood_variance: 0.0005263336257259174
'predict': 0.116 seconds
total run time : 3.22 seconds
--------------------------------------------------
469 / 495
current local expert:
x y lon lat t is_in_ocean
479 -2.442486e+06 -1.586906e+06 -56.987841 63.673639 17913.0 True
'local_data_select': 0.003 seconds
number obs: 195
setting lengthscales to: [1. 1. 1.]
'__init__': 0.045 seconds
'set_lengthscales_constraints': 0.015 seconds
**********
optimization failed!
'optimise_parameters': 1.013 seconds
'get_parameters': 0.015 seconds
parameters:
lengthscales: array([2.12257768, 1.7529628 , 0.69563067])
kernel_variance: 0.001042008971203492
likelihood_variance: 0.0004161545970026669
'predict': 0.146 seconds
total run time : 3.32 seconds
--------------------------------------------------
470 / 495
current local expert:
x y lon lat t is_in_ocean
480 -2.417486e+06 -1.586906e+06 -56.717976 63.866439 17913.0 True
'local_data_select': 0.003 seconds
number obs: 174
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.007 seconds
'optimise_parameters': 0.630 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([1.38696549, 1.7947595 , 0.68550101])
kernel_variance: 0.0009529058137853384
likelihood_variance: 0.00037381292960915864
'predict': 0.075 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.41 seconds
--------------------------------------------------
471 / 495
current local expert:
x y lon lat t is_in_ocean
481 -2.392486e+06 -1.586906e+06 -56.444182 64.058559 17913.0 True
'local_data_select': 0.002 seconds
number obs: 147
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.013 seconds
'optimise_parameters': 0.557 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([3.63779853e+01, 1.00000000e-12, 1.00002556e-08])
kernel_variance: 0.0018506692610041333
likelihood_variance: 0.003878105405853477
'predict': 0.098 seconds
total run time : 2.80 seconds
--------------------------------------------------
472 / 495
current local expert:
x y lon lat t is_in_ocean
482 -2.367486e+06 -1.586906e+06 -56.166385 64.249988 17913.0 True
'local_data_select': 0.004 seconds
number obs: 115
setting lengthscales to: [1. 1. 1.]
'__init__': 0.053 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.473 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([4.71830051e+00, 1.00000020e-12, 1.00000000e-08])
kernel_variance: 0.005204219445847416
likelihood_variance: 3.7157496802588255e-05
'predict': 0.084 seconds
total run time : 2.72 seconds
--------------------------------------------------
473 / 495
current local expert:
x y lon lat t is_in_ocean
483 -2.342486e+06 -1.586906e+06 -55.884511 64.440714 17913.0 True
'local_data_select': 0.003 seconds
number obs: 88
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.820 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([0.59370547, 0.66522937, 0.15565854])
kernel_variance: 0.0009374092944720076
likelihood_variance: 1.0027008746194013e-06
'predict': 0.094 seconds
total run time : 3.00 seconds
--------------------------------------------------
474 / 495
current local expert:
x y lon lat t is_in_ocean
484 -2.867486e+06 -1.561906e+06 -61.42308 60.413692 17913.0 True
'local_data_select': 0.003 seconds
number obs: 5
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.415 seconds
'get_parameters': 0.011 seconds
parameters:
lengthscales: array([59.99999953, 59.99997268, 6.56826557])
kernel_variance: 0.02092536551962384
likelihood_variance: 0.0002826959099766019
'predict': 0.082 seconds
total run time : 2.70 seconds
--------------------------------------------------
475 / 495
current local expert:
x y lon lat t is_in_ocean
485 -2.842486e+06 -1.561906e+06 -61.211826 60.617231 17913.0 True
'local_data_select': 0.002 seconds
number obs: 28
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.371 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([1.10835388e+00, 6.00000000e+01, 1.00000000e-08])
kernel_variance: 0.009131037263202923
likelihood_variance: 0.005141458809589963
'predict': 0.072 seconds
total run time : 2.48 seconds
--------------------------------------------------
476 / 495
current local expert:
x y lon lat t is_in_ocean
486 -2.817486e+06 -1.561906e+06 -60.997698 60.820254 17913.0 True
'local_data_select': 0.003 seconds
number obs: 48
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.010 seconds
'optimise_parameters': 0.401 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([ 0.47698909, 23.36778248, 0.37076543])
kernel_variance: 0.002395809226760686
likelihood_variance: 0.001827642969634268
'predict': 0.072 seconds
total run time : 2.47 seconds
--------------------------------------------------
477 / 495
current local expert:
x y lon lat t is_in_ocean
487 -2.792486e+06 -1.561906e+06 -60.780643 61.022756 17913.0 True
'local_data_select': 0.003 seconds
number obs: 56
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.007 seconds
'optimise_parameters': 0.350 seconds
'get_parameters': 0.005 seconds
parameters:
lengthscales: array([ 1.37806837, 22.5895535 , 1.09268035])
kernel_variance: 0.0013368401946303684
likelihood_variance: 0.002499473000243612
'predict': 0.085 seconds
total run time : 2.90 seconds
--------------------------------------------------
478 / 495
current local expert:
x y lon lat t is_in_ocean
488 -2.767486e+06 -1.561906e+06 -60.560608 61.224727 17913.0 True
'local_data_select': 0.002 seconds
number obs: 78
setting lengthscales to: [1. 1. 1.]
'__init__': 0.031 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.474 seconds
'get_parameters': 0.005 seconds
parameters:
lengthscales: array([ 3.02274866, 59.99440277, 1.12534965])
kernel_variance: 0.0016366446617081005
likelihood_variance: 0.002134039526440331
'predict': 0.069 seconds
total run time : 2.67 seconds
--------------------------------------------------
479 / 495
current local expert:
x y lon lat t is_in_ocean
489 -2.742486e+06 -1.561906e+06 -60.337537 61.426161 17913.0 True
'local_data_select': 0.003 seconds
number obs: 101
setting lengthscales to: [1. 1. 1.]
'__init__': 0.051 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.475 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([3.24558885e+00, 5.26511533e+01, 3.62291793e-02])
kernel_variance: 0.0015387959720624042
likelihood_variance: 0.0016972886005987193
'predict': 0.090 seconds
total run time : 2.60 seconds
--------------------------------------------------
480 / 495
current local expert:
x y lon lat t is_in_ocean
490 -2.717486e+06 -1.561906e+06 -60.111374 61.627051 17913.0 True
'local_data_select': 0.004 seconds
number obs: 109
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.447 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([4.03140004e+00, 5.97382323e+01, 1.00004991e-08])
kernel_variance: 0.002111753096020236
likelihood_variance: 0.0014105966655590433
'predict': 0.067 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.09 seconds
--------------------------------------------------
481 / 495
current local expert:
x y lon lat t is_in_ocean
491 -2.692486e+06 -1.561906e+06 -59.882061 61.827387 17913.0 True
'local_data_select': 0.002 seconds
number obs: 121
setting lengthscales to: [1. 1. 1.]
'__init__': 0.027 seconds
'set_lengthscales_constraints': 0.007 seconds
'optimise_parameters': 0.447 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.69727081e+01, 6.00000000e+01, 1.00000000e-08])
kernel_variance: 0.0007206671499706863
likelihood_variance: 0.0014170028227017918
'predict': 0.071 seconds
total run time : 2.87 seconds
--------------------------------------------------
482 / 495
current local expert:
x y lon lat t is_in_ocean
492 -2.667486e+06 -1.561906e+06 -59.64954 62.027162 17913.0 True
'local_data_select': 0.003 seconds
number obs: 126
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.579 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([ 6.26987562, 59.98995565, 0.83006086])
kernel_variance: 0.0011022001103039763
likelihood_variance: 0.0010954696401346714
'predict': 0.108 seconds
total run time : 2.85 seconds
--------------------------------------------------
483 / 495
current local expert:
x y lon lat t is_in_ocean
493 -2.642486e+06 -1.561906e+06 -59.413751 62.226368 17913.0 True
'local_data_select': 0.002 seconds
number obs: 116
setting lengthscales to: [1. 1. 1.]
'__init__': 0.032 seconds
'set_lengthscales_constraints': 0.008 seconds
**********
optimization failed!
'optimise_parameters': 0.566 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([8.48079197e+00, 5.08039931e+01, 1.55942168e-02])
kernel_variance: 0.001063397546586959
likelihood_variance: 0.0005392329866364618
'predict': 0.075 seconds
total run time : 2.75 seconds
--------------------------------------------------
484 / 495
current local expert:
x y lon lat t is_in_ocean
494 -2.617486e+06 -1.561906e+06 -59.174631 62.424995 17913.0 True
'local_data_select': 0.003 seconds
number obs: 124
setting lengthscales to: [1. 1. 1.]
'__init__': 0.043 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.600 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([1.31926247e+01, 5.58887548e+01, 3.83314054e-02])
kernel_variance: 0.0012308631467978976
likelihood_variance: 0.000579318659291564
'predict': 0.074 seconds
total run time : 2.81 seconds
--------------------------------------------------
485 / 495
current local expert:
x y lon lat t is_in_ocean
495 -2.592486e+06 -1.561906e+06 -58.93212 62.623035 17913.0 True
'local_data_select': 0.003 seconds
number obs: 120
setting lengthscales to: [1. 1. 1.]
'__init__': 0.056 seconds
'set_lengthscales_constraints': 0.009 seconds
**********
optimization failed!
'optimise_parameters': 0.625 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([ 8.12262706, 59.99905791, 0.64316349])
kernel_variance: 0.0013439653119276282
likelihood_variance: 0.0005113918068001172
'predict': 0.084 seconds
total run time : 3.20 seconds
--------------------------------------------------
486 / 495
current local expert:
x y lon lat t is_in_ocean
496 -2.567486e+06 -1.561906e+06 -58.686153 62.820479 17913.0 True
'local_data_select': 0.005 seconds
number obs: 133
setting lengthscales to: [1. 1. 1.]
'__init__': 0.057 seconds
'set_lengthscales_constraints': 0.016 seconds
'optimise_parameters': 0.488 seconds
'get_parameters': 0.007 seconds
parameters:
lengthscales: array([11.28576048, 2.92786576, 4.59487801])
kernel_variance: 0.0008279784939356656
likelihood_variance: 0.00048092705838064274
'predict': 0.087 seconds
total run time : 2.76 seconds
--------------------------------------------------
487 / 495
current local expert:
x y lon lat t is_in_ocean
497 -2.542486e+06 -1.561906e+06 -58.436664 63.017316 17913.0 True
'local_data_select': 0.003 seconds
number obs: 147
setting lengthscales to: [1. 1. 1.]
'__init__': 0.040 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.524 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.73040369, 3.80536546, 0.71933818])
kernel_variance: 0.0014018117642773137
likelihood_variance: 0.0004938855050379736
'predict': 0.069 seconds
total run time : 2.58 seconds
--------------------------------------------------
488 / 495
current local expert:
x y lon lat t is_in_ocean
498 -2.517486e+06 -1.561906e+06 -58.183588 63.213538 17913.0 True
'local_data_select': 0.003 seconds
number obs: 147
setting lengthscales to: [1. 1. 1.]
'__init__': 0.031 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.520 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.67892021, 2.8730484 , 0.81262706])
kernel_variance: 0.0013060024445348396
likelihood_variance: 0.0004778917680078771
'predict': 0.076 seconds
total run time : 2.69 seconds
--------------------------------------------------
489 / 495
current local expert:
x y lon lat t is_in_ocean
499 -2.492486e+06 -1.561906e+06 -57.926856 63.409134 17913.0 True
'local_data_select': 0.002 seconds
number obs: 152
setting lengthscales to: [1. 1. 1.]
'__init__': 0.033 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.546 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([3.95830519, 2.27724692, 0.67628071])
kernel_variance: 0.0013439954283959437
likelihood_variance: 0.0004417826678328265
'predict': 0.078 seconds
total run time : 2.85 seconds
--------------------------------------------------
490 / 495
current local expert:
x y lon lat t is_in_ocean
500 -2.467486e+06 -1.561906e+06 -57.666399 63.604093 17913.0 True
'local_data_select': 0.005 seconds
number obs: 142
setting lengthscales to: [1. 1. 1.]
'__init__': 0.043 seconds
'set_lengthscales_constraints': 0.012 seconds
**********
optimization failed!
'optimise_parameters': 0.934 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([4.1824067 , 2.55113444, 0.51394899])
kernel_variance: 0.0011825762555126578
likelihood_variance: 0.0005177890453678962
'predict': 0.100 seconds
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
total run time : 3.70 seconds
--------------------------------------------------
491 / 495
current local expert:
x y lon lat t is_in_ocean
501 -2.442486e+06 -1.561906e+06 -57.402147 63.798406 17913.0 True
'local_data_select': 0.003 seconds
number obs: 146
setting lengthscales to: [1. 1. 1.]
'__init__': 0.035 seconds
'set_lengthscales_constraints': 0.009 seconds
'optimise_parameters': 0.558 seconds
'get_parameters': 0.008 seconds
parameters:
lengthscales: array([4.21953779, 2.07403218, 0.48756011])
kernel_variance: 0.0010389304240884893
likelihood_variance: 0.0004742903207904785
'predict': 0.102 seconds
total run time : 2.94 seconds
--------------------------------------------------
492 / 495
current local expert:
x y lon lat t is_in_ocean
502 -2.417486e+06 -1.561906e+06 -57.134028 63.992061 17913.0 True
'local_data_select': 0.002 seconds
number obs: 132
setting lengthscales to: [1. 1. 1.]
'__init__': 0.039 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.516 seconds
'get_parameters': 0.009 seconds
parameters:
lengthscales: array([2.69099088, 1.74089332, 7.86986923])
kernel_variance: 0.0010122351023803748
likelihood_variance: 0.00042261041552623513
'predict': 0.099 seconds
total run time : 2.88 seconds
--------------------------------------------------
493 / 495
current local expert:
x y lon lat t is_in_ocean
503 -2.392486e+06 -1.561906e+06 -56.861967 64.185046 17913.0 True
'local_data_select': 0.003 seconds
number obs: 110
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.008 seconds
'optimise_parameters': 0.502 seconds
'get_parameters': 0.005 seconds
parameters:
lengthscales: array([0.57009676, 0.67923109, 8.99999015])
kernel_variance: 0.0009155032739472132
likelihood_variance: 4.1198780447164944e-05
'predict': 0.087 seconds
total run time : 2.78 seconds
--------------------------------------------------
494 / 495
current local expert:
x y lon lat t is_in_ocean
504 -2.367486e+06 -1.561906e+06 -56.585891 64.377351 17913.0 True
'local_data_select': 0.009 seconds
number obs: 94
setting lengthscales to: [1. 1. 1.]
'__init__': 0.057 seconds
'set_lengthscales_constraints': 0.020 seconds
'optimise_parameters': 0.809 seconds
'get_parameters': 0.010 seconds
parameters:
lengthscales: array([0.51602831, 0.56861145, 8.99922735])
kernel_variance: 0.0008718106755273208
likelihood_variance: 1.0049343676459276e-06
'predict': 0.085 seconds
total run time : 3.27 seconds
--------------------------------------------------
495 / 495
current local expert:
x y lon lat t is_in_ocean
505 -2.342486e+06 -1.561906e+06 -56.305722 64.568962 17913.0 True
'local_data_select': 0.002 seconds
number obs: 71
setting lengthscales to: [1. 1. 1.]
'__init__': 0.037 seconds
'set_lengthscales_constraints': 0.011 seconds
'optimise_parameters': 0.479 seconds
'get_parameters': 0.006 seconds
parameters:
lengthscales: array([0.49320731, 0.66161075, 8.99999729])
kernel_variance: 0.0008377683514306252
likelihood_variance: 1e-06
'predict': 0.067 seconds
total run time : 2.63 seconds
storing any remaining tables
SAVING RESULTS TO TABLES:
run_details
preds
lengthscales
kernel_variance
likelihood_variance
'run': 1313.648 seconds
Glue initial results#
# extract, store in dict
dfs, _ = get_results_from_h5file(store_path)
print(f"tables in results file: {list(dfs.keys())}")
reading in results
getting all tables
merging on expert location data
table: 'oi_config' does not have all coords_col: ['x', 'y', 't'] in columns, not merging on expert_locations
tables in results file: ['expert_locs', 'kernel_variance', 'lengthscales', 'likelihood_variance', 'oi_config', 'preds', 'run_details']
preds_data = dfs["preds"]
preds_data.head()
inference_radius = 50_000
# multiple local experts may make predictions at the same prediction location (pred_loc).
# - for each prediction at a given location, take we weighted combination
# - weights being a function of the distance to each local expert that made a prediction at a given location.
plt_data = glue_local_predictions_2d(preds_df=preds_data,
pred_loc_cols=["pred_loc_x", "pred_loc_y"],
xprt_loc_cols=["x", "y"],
vars_to_glue=["f*", "f*_var"],
inference_radius=inference_radius)
plt_data['lon'],plt_data['lat'] = EASE2toWGS84_New(plt_data['pred_loc_x'],plt_data['pred_loc_y'],lat_0=90)
#Set to nan predictions that are not over the ocean
plt_data['is_ocean'] = globe.is_ocean(plt_data['lat'], plt_data['lon'])
plt_data.loc[plt_data['is_ocean'] == False, 'f*'] = np.nan
plt_data.head()
<ipython-input-26-b5e1824e6ef0>:12: DeprecationWarning: Call to deprecated function (or staticmethod) EASE2toWGS84_New. (This function will be removed in future versions. Use `EASE2toWGS84` instead.)
plt_data['lon'],plt_data['lat'] = EASE2toWGS84_New(plt_data['pred_loc_x'],plt_data['pred_loc_y'],lat_0=90)
| pred_loc_x | pred_loc_y | f* | f*_var | lon | lat | is_ocean | |
|---|---|---|---|---|---|---|---|
| 0 | -2.862486e+06 | -2.106906e+06 | 0.001823 | 0.000695 | -53.645422 | 57.721871 | True |
| 1 | -2.862486e+06 | -2.101906e+06 | 0.001931 | 0.000839 | -53.710389 | 57.749552 | True |
| 2 | -2.862486e+06 | -2.096906e+06 | 0.001677 | 0.000975 | -53.775465 | 57.777188 | True |
| 3 | -2.862486e+06 | -2.091906e+06 | 0.000999 | 0.001076 | -53.840650 | 57.804780 | True |
| 4 | -2.862486e+06 | -2.086906e+06 | 0.000035 | 0.001128 | -53.905942 | 57.832326 | True |
Visualise OI results#
fig, ax = plt.subplots()
sla_preds = ax.scatter(plt_data['pred_loc_x'],plt_data['pred_loc_y'],c=plt_data['f*'],s=10,cmap='seismic',vmin=-0.2,vmax=0.2)
fig.colorbar(sla_preds, ax=ax, label='SSHA (m)', pad=0.01)
ax.set_title('GPSat predictions')
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()
fig,axs = plt.subplots(2,3,figsize=(10,10),subplot_kw=dict(projection=ccrs.NorthPolarStereo()))
axs = axs.flatten()
eddy_proxy = mlines.Line2D([], [], color='black', linestyle='--', linewidth=1, alpha=0.9, label="Selected AVISO eddy")
for i, ax in enumerate(axs):
if i == 2:
continue # We will leave ax[2] empty
ax.set_extent([-35, -75, 55, 60], crs=ccrs.PlateCarree())
ax.coastlines()
ax.add_feature(cfeature.LAND, color='lightgray')
ax.add_geometries(
selected_eddy['geometry'], crs=ccrs.PlateCarree(),
facecolor="none", edgecolor="black", linewidth=1, linestyle='--', alpha=0.9, zorder=100)
axs[0].scatter(sral_ds.where(sral_ds.time_20_ku.dt.date == selected_eddy['time'].values[0].astype("datetime64[D]"))['lon_20_ku'],\
sral_ds.where(sral_ds.time_20_ku.dt.date == selected_eddy['time'].values[0].astype("datetime64[D]"))['lat_20_ku'],\
c=sral_ds.where(sral_ds.time_20_ku.dt.date == selected_eddy['time'].values[0].astype("datetime64[D]"))['ssha_20_ku'],\
s=1, cmap='seismic', vmin=-0.2, vmax=0.2, transform=ccrs.PlateCarree())
axs[0].set_title(f'SSHA Along-track observations\n{selected_eddy["time"].values[0].astype("datetime64[D]")}')
axs[0].legend(handles=[eddy_proxy], loc='lower left', fontsize=10)
axs[1].scatter(gridded_obvs_df[gridded_obvs_df['t'] == selected_eddy['time'].values[0].astype("datetime64[D]").astype(float)]['x'],\
gridded_obvs_df[gridded_obvs_df['t'] == selected_eddy['time'].values[0].astype("datetime64[D]").astype(float)]['y'],\
c=gridded_obvs_df[gridded_obvs_df['t'] == selected_eddy['time'].values[0].astype("datetime64[D]").astype(float)]['ssha_20_ku'],\
s=1, cmap='seismic', vmin=-0.2, vmax=0.2, transform=ccrs.epsg(6931))
axs[1].set_title(f'SSHA Binned observations:\n{selected_eddy["time"].values[0].astype("datetime64[D]")}')
axs[2].axis('off')
axs[3].scatter(sral_df['lon_20_ku'],sral_df['lat_20_ku'],c=sral_df['ssha_20_ku'],s=1,cmap='seismic',vmin=-0.2,vmax=0.2,transform=ccrs.PlateCarree())
axs[3].set_title(f'Along-track observations\n± {training_window.days} days (stacked)')
axs[4].scatter(gridded_obvs_df['x'],gridded_obvs_df['y'],c=gridded_obvs_df['ssha_20_ku'],s=1,cmap='seismic',vmin=-0.2,vmax=0.2, transform=ccrs.epsg(6931))
axs[4].set_title(f'Binned observations\n± {training_window.days} days (stacked)')
axs[5].scatter(plt_data['lon'],plt_data['lat'],c=plt_data['f*'],s=1,cmap='seismic',vmin=-0.2,vmax=0.2,transform=ccrs.PlateCarree())
axs[5].set_title(f'OI predictions {selected_eddy["time"].values[0].astype("datetime64[D]")}')
plt.tight_layout()
plt.show()
Qualitatively, we have been able to construct an SSH field using optimal interpolation that appears to capture well the known eddy in terms of its position and height. This is despite not having a single direct overpass on the date of interest.
The next step would be to process this SSH field to extract the eddy bounds and more of its characteristics (vorticity, inner height vs effective height, etc). See literature such as: Mason, E., A. Pascual, and J. C. McWilliams, 2014: A New Sea Surface Height–Based Code for Oceanic Mesoscale Eddy Tracking. J. Atmos. Oceanic Technol., 31, 1181–1188, https://doi.org/10.1175/JTECH-D-14-00019.1 and its associated python package, https://py-eddy-tracker.readthedocs.io/en/latest/python_module/index.html, for the steps taken to do achieve this.