diff --git a/tools/icenet/.shed.yml b/tools/icenet/.shed.yml
new file mode 100755
index 0000000..c2e3d52
--- /dev/null
+++ b/tools/icenet/.shed.yml
@@ -0,0 +1,14 @@
+name: icenet
+owner: climate
+suite:
+ name: "suite_icenet"
+ description: "A suite of icenet tools"
+ type: repository_suite_definition
+description: A collection of tools to create a forecast of the sea ice concentration with IceNet and visualize it
+homepage_url: https://zenodo.org/record/5176573
+long_description: |
+ IceNet is a probabilistic, deep learning sea ice forecasting system. It consists of an ensemble of U-Net networks, which learns how sea ice changes from climate simulations and observational data to forecast up to 6 months of monthly-averaged sea ice concentration maps at 25 km resolution. The forecast tool takes pretrained IceNet models downloaded from the Polar Data Centre and Observational Data generated with the "Preprocess Icenet Data Tool" as an input and creates one netcdf forecast file as an output. This forecast file can be used as an input for the "Visualize Icenet Forecast Tool" to plot an interactive figure.
+remote_repository_url: https://github.com/NordicESMhub/galaxy-tools/tree/master/tools/icenet
+type: unrestricted
+categories:
+- Climate Analysis
diff --git a/tools/icenet/2021_07_01_183913_forecast_results.csv b/tools/icenet/2021_07_01_183913_forecast_results.csv
new file mode 100755
index 0000000..3b56e48
--- /dev/null
+++ b/tools/icenet/2021_07_01_183913_forecast_results.csv
@@ -0,0 +1,35648 @@
+# Model: Name of the sea ice forecast model
+# Ensemble member: Name of the ensemble member. Only relevant for IceNet models - for SEAS5 and the linear trend model this is 'NA'. IceNet's temperature-scaled ensemble mean is labelled 'ensemble_tempscaled'. The non-temperature-scaled ensemble mean is labelled 'ensemble'
+# Leadtime: Lead time of the forecast (number of months)
+# Forecast date: Monthly-average target forecast date (day set to 01 by default)
+# Binary accuracy: Percentage of grid cells with the correct binary prediction for SIC > 15% from the model, computed within a maximum sea ice climatology mask (%)
+# SIE error: Sea ice extent (area covered by grid cells with SIC > 15%) error between model and OSI-SAF ground truth (km2)
+#
+Model,Ensemble member,Leadtime,Forecast date,Binary accuracy,SIE error
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-01-01,96.92559,57500.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-02-01,96.46002,-16250.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-03-01,96.31853000000001,-400625.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-04-01,96.77914399999999,-135625.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-05-01,96.8483,-98750.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-06-01,94.62413000000001,123750.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-07-01,93.18614000000001,-475000.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-08-01,92.539055,-281250.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-09-01,94.09713,98750.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-10-01,88.146355,-1119375.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-11-01,95.01507,-412500.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2012-12-01,96.6627,-255625.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-01-01,97.20398,-296250.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-02-01,97.17099,10625.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-03-01,96.69292,-94375.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-04-01,97.12799,-11875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-05-01,96.00676,-206875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-06-01,96.3073,-116875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-07-01,94.07509,216875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-08-01,95.34156999999999,-191875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-09-01,92.82182,-34375.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-10-01,93.62055,-420000.0
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+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2013-12-01,96.414635,-135000.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-01-01,96.28711,-196875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-02-01,95.927536,38125.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-03-01,96.81177,-121875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-04-01,96.58667,-21875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-05-01,97.374664,-41250.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-06-01,95.73891,-72500.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-07-01,93.76086,-239375.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-08-01,93.187225,-521875.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-09-01,93.69632,-654375.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-10-01,90.0801,-958125.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2014-11-01,95.65677,-258750.0
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+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2016-02-01,95.859116,-33750.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2016-03-01,97.64670600000001,-126250.0
+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2016-04-01,97.84374,129375.0
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+IceNet__2021_06_15_1854_icenet_nature_communications__unet_tempscale,36,1,2018-01-01,96.94375,-58750.0
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diff --git a/tools/icenet/2021_09_03_1300_icenet_config.json b/tools/icenet/2021_09_03_1300_icenet_config.json
new file mode 100755
index 0000000..47691d7
--- /dev/null
+++ b/tools/icenet/2021_09_03_1300_icenet_config.json
@@ -0,0 +1,207 @@
+{
+ "dataloader_name": "icenet_demo",
+ "dataset_name": "dataset1",
+ "input_data": {
+ "siconca": {
+ "abs": {
+ "include": true,
+ "max_lag": 12
+ },
+ "anom": {
+ "include": false,
+ "max_lag": 3
+ },
+ "linear_trend": {
+ "include": true
+ }
+ },
+ "tas": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "ta500": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "tos": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "rsds": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "rsus": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "psl": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "zg500": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "zg250": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "ua10": {
+ "abs": {
+ "include": true,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": false,
+ "max_lag": 3
+ }
+ },
+ "uas": {
+ "abs": {
+ "include": true,
+ "max_lag": 1
+ },
+ "anom": {
+ "include": false,
+ "max_lag": 1
+ }
+ },
+ "vas": {
+ "abs": {
+ "include": true,
+ "max_lag": 1
+ },
+ "anom": {
+ "include": false,
+ "max_lag": 1
+ }
+ },
+ "land": {
+ "metadata": true,
+ "include": true
+ },
+ "circmonth": {
+ "metadata": true,
+ "include": true
+ }
+ },
+ "batch_size": 2,
+ "shuffle": true,
+ "n_forecast_months": 6,
+ "sample_IDs": {
+ "obs_train_dates": [
+ "1980-1-1",
+ "2011-6-1"
+ ],
+ "obs_val_dates": [
+ "2012-1-1",
+ "2017-6-1"
+ ],
+ "obs_test_dates": [
+ "2018-1-1",
+ "2019-6-1"
+ ]
+ },
+ "cmip6_run_dict": {
+ "EC-Earth3": {
+ "r2i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r7i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r10i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r12i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r14i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ]
+ },
+ "MRI-ESM2-0": {
+ "r1i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r2i1p1f1": [
+ "1851-1-1",
+ "2029-6-1"
+ ],
+ "r3i1p1f1": [
+ "1851-1-1",
+ "2029-6-1"
+ ],
+ "r4i1p1f1": [
+ "1851-1-1",
+ "2029-6-1"
+ ],
+ "r5i1p1f1": [
+ "1851-1-1",
+ "2029-6-1"
+ ]
+ }
+ },
+ "raw_data_shape": [
+ 432,
+ 432
+ ],
+ "default_seed": 42,
+ "loss_weight_months": true,
+ "verbose_level": 0
+}
diff --git a/tools/icenet/concatenate.py b/tools/icenet/concatenate.py
new file mode 100644
index 0000000..f0207c6
--- /dev/null
+++ b/tools/icenet/concatenate.py
@@ -0,0 +1,17 @@
+import xarray as xr
+import argparse
+'''
+Concatenates monthly averaged OSI-SAF SIC data from 1979-present. The data was downloaded from the following sites:
+ - OSI-450 (1979-2016): https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_v2p0_nh_agg.html
+ - OSI-430-b (2016-present): https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_crb_nh_agg.html
+'''
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input", type=str, nargs="+", help="input files")
+args = parser.parse_args()
+
+pathlist = args.input
+
+concat_data = xr.open_mfdataset(pathlist, preprocess=lambda f: f["ice_conc"], data_vars=["ice_conc"])
+concat_data = concat_data.resample(time="1MS").mean(dim="time")
+
+concat_data.to_netcdf('siconca.nc')
diff --git a/tools/icenet/config.py b/tools/icenet/config.py
new file mode 100755
index 0000000..9fa6f12
--- /dev/null
+++ b/tools/icenet/config.py
@@ -0,0 +1,89 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+
+import os
+import pandas as pd
+'''
+Defines globals used throughout the codebase.
+'''
+
+###############################################################################
+# Folder structure naming system
+###############################################################################
+
+data_folder = 'data'
+obs_data_folder = os.path.join(data_folder, 'obs')
+cmip6_data_folder = os.path.join(data_folder, 'cmip6')
+mask_data_folder = os.path.join(data_folder, 'masks')
+forecast_data_folder = os.path.join(data_folder, 'forecasts')
+network_dataset_folder = os.path.join(data_folder, 'network_datasets')
+
+dataloader_config_folder = 'dataloader_configs'
+
+networks_folder = 'trained_networks'
+
+results_folder = 'results'
+forecast_results_folder = os.path.join(results_folder, 'forecast_results')
+permute_and_predict_results_folder = os.path.join(results_folder, 'permute_and_predict_results')
+uncertainty_results_folder = os.path.join(results_folder, 'uncertainty_results')
+
+figure_folder = 'figures'
+
+video_folder = 'videos'
+
+active_grid_cell_file_format = 'active_grid_cell_mask_{}.npy'
+land_mask_filename = 'land_mask.npy'
+region_mask_filename = 'region_mask.npy'
+
+###############################################################################
+# Polar hole/missing months
+###############################################################################
+
+# Pre-defined polar hole radii (in number of 25km x 25km grid cells)
+# The polar hole radii were determined from Sections 2.1, 2.2, and 2.3 of
+# http://osisaf.met.no/docs/osisaf_cdop3_ss2_pum_sea-ice-conc-climate-data-record_v2p0.pdf
+polarhole1_radius = 28
+polarhole2_radius = 11
+polarhole3_radius = 3
+
+# Whether or not to mask out the 3rd polar hole mask from
+# Nov 2005 to Dec 2015 with a radius of only 3 grid cells. Including it creates
+# some complications when analysing performance on a validation set that
+# overlaps with the 3rd polar hole period.
+use_polarhole3 = False
+
+polarhole1_fname = 'polarhole1_mask.npy'
+polarhole2_fname = 'polarhole2_mask.npy'
+polarhole3_fname = 'polarhole3_mask.npy'
+
+# Final month that each of the polar holes apply
+# NOTE: 1st of the month chosen arbitrarily throughout as always working wit
+# monthly averages
+polarhole1_final_date = pd.Timestamp('1987-06-01') # 1987 June
+polarhole2_final_date = pd.Timestamp('2005-10-01') # 2005 Oct
+polarhole3_final_date = pd.Timestamp('2015-12-01') # 2015 Dec
+
+missing_dates = [pd.Timestamp('1986-4-1'), pd.Timestamp('1986-5-1'),
+ pd.Timestamp('1986-6-1'), pd.Timestamp('1987-12-1')]
+
+###############################################################################
+# Weights and biases config (https://docs.wandb.ai/guides/track/advanced/environment-variables)
+###############################################################################
+
+# Get API key from https://wandb.ai/authorize
+WANDB_API_KEY = 'YOUR-KEY-HERE'
+# Absolute path to store wandb generated files (folder must exist)
+# Note: user must have write access
+WANDB_DIR = '/path/to/wandb/dir'
+# Absolute path to wandb config dir (
+WANDB_CONFIG_DIR = '/path/to/wandb/config/dir'
+WANDB_CACHE_DIR = '/path/to/wandb/cache/dir'
+
+###############################################################################
+# ECMWF details
+###############################################################################
+
+ECMWF_API_KEY = 'YOUR-KEY-HERE'
+ECMWF_API_EMAIL = 'YOUR-KEY-HERE'
diff --git a/tools/icenet/createRequests.py b/tools/icenet/createRequests.py
new file mode 100644
index 0000000..2b76cb9
--- /dev/null
+++ b/tools/icenet/createRequests.py
@@ -0,0 +1,150 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and adjusted to fit the galaxy interface.
+"""
+import argparse
+import json
+
+import pandas as pd
+"""
+Creates API request text files for downloading Icenet Data with the Copernicus Climate Data Store according to the config file.
+"""
+parser = argparse.ArgumentParser()
+parser.add_argument('--var', nargs="+")
+parser.add_argument("--forecast_start", type=str, help="forecast start date")
+parser.add_argument("--forecast_end", type=str, help="forecast end date")
+parser.add_argument("--config", type=str, help="config file")
+args = parser.parse_args()
+
+
+args = parser.parse_args()
+forecast_start = args.forecast_start
+forecast_end = args.forecast_end
+config = args.config
+variable = args.var
+
+with open(config, 'r') as readfile:
+ config = json.load(readfile)
+max_lag = config["input_data"][variable[0]]["abs"]["max_lag"]
+
+forecast_start_year = pd.Timestamp(forecast_start).year
+forecast_start_month = pd.Timestamp(forecast_start).month
+
+forecast_end_year = pd.Timestamp(forecast_end).year
+forecast_end_month = pd.Timestamp(forecast_end).month
+
+area = [90, -180, 0, 180] # Latitude/longitude boundaries to download
+
+
+# Which years to download
+
+past_year = []
+if ((forecast_start_month - (6 + max_lag)) <= 0):
+ past_year = [forecast_start_year - 1]
+
+years = past_year + [i for i in range(forecast_start_year, forecast_end_year + 1)]
+
+months = []
+for i in range(0, 13):
+ if ((forecast_start_month - (6 + max_lag) + i < forecast_start_month) or (forecast_start_month - (6 + max_lag) + i < forecast_end_month)):
+ months = months + [(forecast_start_month - (6 + max_lag) + 12 + i) % 12 if (forecast_start_month - (6 + max_lag) + 12 + i) % 12 != 0 else 12]
+ else:
+ break
+print(years)
+print(months)
+
+# Near-real-time data contains ERA5T with 'expver' coord -- remove 'expver'
+# dim and concatenate into one array
+fix_near_real_time_era5_coords = True
+
+# Variable information
+################################################################################
+
+# To add more variables, go to the following dataset sites, fill in the download
+# form for the desired variable, and check the variable's CDI name by clicking
+# 'show API request':
+# - Surface vars: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=form
+# - Pressure level vars: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=overview
+variables = {
+ 'tas': {
+ 'cdi_name': '2m_temperature',
+ },
+ 'ta500': {
+ 'plevel': '500',
+ 'cdi_name': 'temperature',
+ },
+ 'tos': {
+ 'cdi_name': 'sea_surface_temperature',
+ },
+ # RSUS needs to be computed using net solar radiation and downwards radiation,
+ # and need to convert from J/m^2 to W/m^2 by dividing by the number of seconds
+ # in a day (60*60*25)
+ 'rsds': {
+ 'cdi_name': 'surface_solar_radiation_downwards',
+ },
+ 'rsus': {
+ 'cdi_name': 'surface_net_solar_radiation',
+ },
+ 'psl': {
+ 'cdi_name': 'mean_sea_level_pressure',
+ },
+ 'zg500': {
+ 'plevel': '500',
+ 'cdi_name': 'geopotential',
+ },
+ 'zg250': {
+ 'plevel': '250',
+ 'cdi_name': 'geopotential',
+ },
+ 'ua10': {
+ 'plevel': '10',
+ 'cdi_name': 'u_component_of_wind',
+ },
+ # Note: the surface wind variables are not regridded here; a separate script
+ # is used to rotate and regrid them.
+ 'uas': {
+ 'cdi_name': '10m_u_component_of_wind',
+ },
+ 'vas': {
+ 'cdi_name': '10m_v_component_of_wind',
+ },
+}
+var_dict = variables[variable[0]]
+var_names = []
+for var in variable:
+ var_names.append(variables[var]["cdi_name"])
+
+
+def retrieve_CDS_data(var_cdi_name, plevel=None):
+
+ cds_dict = {
+ 'product_type': 'monthly_averaged_reanalysis',
+ 'variable': var_cdi_name,
+ 'year': years,
+ 'month': months,
+ 'time': '00:00',
+ 'format': 'netcdf',
+ 'area': area
+ }
+
+ if plevel is not None:
+ dataset = 'reanalysis-era5-pressure-levels-monthly-means'
+ cds_dict['pressure_level'] = plevel
+ else:
+ dataset = 'reanalysis-era5-single-levels-monthly-means'
+
+ return "c.retrieve({}, \n {}, \n 'download.nc')".format(dataset, cds_dict)
+
+
+filename = '{}_request.txt'.format(variable[0])
+
+if 'plevel' not in var_dict.keys():
+ plevel = None
+else:
+ plevel = var_dict['plevel']
+
+
+with open(filename, 'w') as f:
+
+ f.write("import cdsapi\n\n")
+ f.write("c = cdsapi.Client()\n\n")
+ f.write(retrieve_CDS_data(var_names, plevel))
diff --git a/tools/icenet/forecast.py b/tools/icenet/forecast.py
new file mode 100755
index 0000000..c2697f5
--- /dev/null
+++ b/tools/icenet/forecast.py
@@ -0,0 +1,146 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import argparse
+import os
+import re
+import sys
+
+import config
+import numpy as np
+import pandas as pd
+import xarray as xr
+from tensorflow.keras.models import load_model
+from tqdm import tqdm
+from utils import IceNetDataLoader
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet'))
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--config", type=str, help="config file")
+parser.add_argument("--models", type=str, help="network models")
+parser.add_argument("--siconca", type=str, help="siconca netcdf file")
+parser.add_argument("--forecast_start", type=str, help="forecast start date")
+parser.add_argument("--forecast_end", type=str, help="forecast end date")
+args = parser.parse_args()
+
+# Load dataloader
+dataloader_ID = '2021_09_03_1300_icenet_demo'
+dataloader_config_fpath = args.config
+
+# Data loader
+# print("\nSetting up the data loader with config file: {}\n\n".format(dataloader_ID))
+dataloader = IceNetDataLoader(dataloader_config_fpath)
+print('\n\nDone.\n')
+
+# load networks
+network_regex = re.compile('^network_tempscaled_([0-9]*).h5$')
+
+network_fpaths = args.models.split(",")
+
+# ensemble_seeds = [36, 42, 53]
+ensemble_seeds = network_fpaths
+print(ensemble_seeds)
+
+networks = []
+for network_fpath in network_fpaths:
+ print('Loading model from {}... '.format(network_fpath), end='', flush=True)
+ networks.append(load_model(network_fpath, compile=False))
+ print('Done.')
+
+model = 'IceNet'
+
+forecast_start = pd.Timestamp(args.forecast_start)
+forecast_end = pd.Timestamp(args.forecast_end)
+
+n_forecast_months = dataloader.config['n_forecast_months']
+
+
+forecast_folder = os.path.join(config.forecast_data_folder, 'icenet', dataloader_ID, model)
+
+if not os.path.exists(forecast_folder):
+ os.makedirs(forecast_folder)
+
+# load ground truth
+print('Loading ground truth SIC... ', end='', flush=True)
+true_sic_fpath = args.siconca
+true_sic_da = xr.open_dataarray(true_sic_fpath)
+print('Done.')
+
+
+# set up forecast folder
+
+# define list of lead times
+leadtimes = np.arange(1, n_forecast_months + 1)
+
+# add ensemble to the list of models
+ensemble_seeds_and_mean = ensemble_seeds.copy()
+ensemble_seeds_and_mean.append('ensemble')
+
+all_target_dates = pd.date_range(
+ start=forecast_start,
+ end=forecast_end,
+ freq='MS'
+)
+
+all_start_dates = pd.date_range(
+ start=forecast_start - pd.DateOffset(months=n_forecast_months - 1),
+ end=forecast_end,
+ freq='MS'
+)
+
+shape = (len(all_target_dates),
+ *dataloader.config['raw_data_shape'],
+ n_forecast_months)
+
+coords = {
+ 'time': all_target_dates, # To be sliced to target dates
+ 'yc': true_sic_da.coords['yc'],
+ 'xc': true_sic_da.coords['xc'],
+ 'lon': true_sic_da.isel(time=0).coords['lon'],
+ 'lat': true_sic_da.isel(time=0).coords['lat'],
+ 'leadtime': leadtimes,
+ 'seed': ensemble_seeds_and_mean,
+ 'ice_class': ['no_ice', 'marginal_ice', 'full_ice']
+}
+
+# Probabilistic SIC class forecasts
+dims = ('seed', 'time', 'yc', 'xc', 'leadtime', 'ice_class')
+shape = (len(ensemble_seeds_and_mean), *shape, 3)
+print(dims)
+print(shape)
+model_forecast = xr.DataArray(
+ data=np.zeros(shape, dtype=np.float32),
+ coords=coords,
+ dims=dims
+)
+
+for start_date in tqdm(all_start_dates):
+
+ # Target forecast dates for the forecast beginning at this `start_date`
+ target_dates = pd.date_range(
+ start=start_date,
+ end=start_date + pd.DateOffset(months=n_forecast_months - 1),
+ freq='MS'
+ )
+
+ X, y, sample_weights = dataloader.data_generation([start_date])
+ mask = sample_weights > 0
+ pred = np.array([network.predict(X)[0] for network in networks])
+ pred *= mask # mask outside active grid cell region to zero
+ # concat ensemble mean to the set of network predictions
+ ensemble_mean_pred = pred.mean(axis=0, keepdims=True)
+ pred = np.concatenate([pred, ensemble_mean_pred], axis=0)
+
+ for i, (target_date, leadtime) in enumerate(zip(target_dates, leadtimes)):
+ if target_date in all_target_dates:
+ model_forecast.\
+ loc[:, target_date, :, :, leadtime] = pred[..., i]
+
+print('Saving forecast NetCDF for {}... '.format(model), end='', flush=True)
+
+forecast_fpath = os.path.join(forecast_folder, f'{model.lower()}_forecasts.nc'.format(model.lower()))
+model_forecast.to_netcdf(forecast_fpath) # export file as Net
+
+print('Done.')
diff --git a/tools/icenet/icenet_concatenate.xml b/tools/icenet/icenet_concatenate.xml
new file mode 100644
index 0000000..8041c9c
--- /dev/null
+++ b/tools/icenet/icenet_concatenate.xml
@@ -0,0 +1,47 @@
+
+ for concatenating yearly sea ice concentration netcdf files
+
+ python
+ xarray
+ netcdf4
+ h5netcdf
+ dask
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 10.5281/zenodo.5176573
+
+
+
diff --git a/tools/icenet/icenet_forecast.xml b/tools/icenet/icenet_forecast.xml
new file mode 100755
index 0000000..e709a51
--- /dev/null
+++ b/tools/icenet/icenet_forecast.xml
@@ -0,0 +1,444 @@
+
+ for forecasting sea ice concentration with IceNet
+
+ python
+ xarray
+ numpy
+ pandas
+ scipy
+ iris
+ netcdf4
+ imageio
+ matplotlib
+ tqdm
+ cdsapi
+ tensorflow
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 10.5281/zenodo.5176573
+
+
+
diff --git a/tools/icenet/icenet_preprocess.xml b/tools/icenet/icenet_preprocess.xml
new file mode 100755
index 0000000..1ff8fbc
--- /dev/null
+++ b/tools/icenet/icenet_preprocess.xml
@@ -0,0 +1,216 @@
+
+ for regridding and normalizing icenet data
+
+ python
+ xarray
+ numpy
+ pandas
+ scipy
+ iris
+ netcdf4
+ imageio
+ matplotlib
+ tqdm
+ cdsapi
+ tensorflow
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 10.5281/zenodo.5176573
+
+
+
diff --git a/tools/icenet/icenet_requests.xml b/tools/icenet/icenet_requests.xml
new file mode 100644
index 0000000..74baaaa
--- /dev/null
+++ b/tools/icenet/icenet_requests.xml
@@ -0,0 +1,59 @@
+
+ for creating request text files for downloading Icenet Data with the Copernicus Climate Data Store Tool
+
+ python
+ xarray
+ pandas
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 10.5281/zenodo.5176573
+
+
+
diff --git a/tools/icenet/icenet_visualize.xml b/tools/icenet/icenet_visualize.xml
new file mode 100755
index 0000000..dac371c
--- /dev/null
+++ b/tools/icenet/icenet_visualize.xml
@@ -0,0 +1,55 @@
+
+ for visualizing and analyzing the Icenet forecast
+
+ python
+ xarray
+ numpy
+ pandas
+ scipy
+ iris
+ netcdf4
+ imageio
+ matplotlib
+ tqdm
+ cdsapi
+ tensorflow
+ panel
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 10.5281/zenodo.5176573
+
+
+
diff --git a/tools/icenet/masks/.listing b/tools/icenet/masks/.listing
new file mode 100755
index 0000000..738cb2b
--- /dev/null
+++ b/tools/icenet/masks/.listing
@@ -0,0 +1,32 @@
+drwxr-xr-x 2 ftp ftp 0 May 09 2017 .
+drwxr-xr-x 2 ftp ftp 0 May 09 2017 ..
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901041200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901061200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901081200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901101200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901121200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901141200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901161200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901181200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901201200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901221200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901241200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901261200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901281200.nc
+-rwxr-xr-x 1 ftp ftp 9856120 May 09 2017 ice_conc_nh_ease2-250_cdr-v2p0_197901301200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901021200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901041200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901061200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901081200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901101200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901121200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901141200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901161200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901181200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901201200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901221200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901241200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901261200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901281200.nc
+-rwxr-xr-x 1 ftp ftp 9856141 Jun 08 2022 ice_conc_sh_ease2-250_cdr-v2p0_197901301200.nc
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diff --git a/tools/icenet/models.py b/tools/icenet/models.py
new file mode 100755
index 0000000..a3c59c8
--- /dev/null
+++ b/tools/icenet/models.py
@@ -0,0 +1,190 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import sys
+import os
+import config
+import numpy as np
+import pandas as pd
+import xarray as xr
+import tensorflow as tf
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Conv2D, BatchNormalization, UpSampling2D, \
+ concatenate, MaxPooling2D, Input
+from tensorflow.keras.optimizers import Adam
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+'''
+Defines the Python-based sea ice forecasting models, such as the IceNet architecture
+and the linear trend extrapolation model.
+'''
+
+# Custom layers:
+# --------------------------------------------------------------------
+
+
+@tf.keras.utils.register_keras_serializable()
+class TemperatureScale(tf.keras.layers.Layer):
+ '''
+ Implements the temperature scaling layer for probability calibration,
+ as introduced in Guo 2017 (http://proceedings.mlr.press/v70/guo17a.html).
+ '''
+ def __init__(self, **kwargs):
+ super(TemperatureScale, self).__init__()
+ self.temp = tf.Variable(initial_value=1.0, trainable=False,
+ dtype=tf.float32, name='temp')
+
+ def call(self, inputs):
+ ''' Divide the input logits by the T value. '''
+ return tf.divide(inputs, self.temp)
+
+ def get_config(self):
+ ''' For saving and loading networks with this custom layer. '''
+ return {'temp': self.temp.numpy()}
+
+
+# Network architectures:
+# --------------------------------------------------------------------
+
+def unet_batchnorm(input_shape, loss, weighted_metrics, learning_rate=1e-4, filter_size=3,
+ n_filters_factor=1, n_forecast_months=1, use_temp_scaling=False,
+ n_output_classes=3,
+ **kwargs):
+ inputs = Input(shape=input_shape)
+
+ conv1 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
+ conv1 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
+ bn1 = BatchNormalization(axis=-1)(conv1)
+ pool1 = MaxPooling2D(pool_size=(2, 2))(bn1)
+
+ conv2 = Conv2D(np.int(128 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
+ conv2 = Conv2D(np.int(128 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
+ bn2 = BatchNormalization(axis=-1)(conv2)
+ pool2 = MaxPooling2D(pool_size=(2, 2))(bn2)
+
+ conv3 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
+ conv3 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
+ bn3 = BatchNormalization(axis=-1)(conv3)
+ pool3 = MaxPooling2D(pool_size=(2, 2))(bn3)
+
+ conv4 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
+ conv4 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
+ bn4 = BatchNormalization(axis=-1)(conv4)
+ pool4 = MaxPooling2D(pool_size=(2, 2))(bn4)
+
+ conv5 = Conv2D(np.int(512 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
+ conv5 = Conv2D(np.int(512 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
+ bn5 = BatchNormalization(axis=-1)(conv5)
+
+ up6 = Conv2D(np.int(256 * n_filters_factor), 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2), interpolation='nearest')(bn5))
+ merge6 = concatenate([bn4, up6], axis=3)
+ conv6 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
+ conv6 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
+ bn6 = BatchNormalization(axis=-1)(conv6)
+
+ up7 = Conv2D(np.int(256 * n_filters_factor), 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2), interpolation='nearest')(bn6))
+ merge7 = concatenate([bn3, up7], axis=3)
+ conv7 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
+ conv7 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
+ bn7 = BatchNormalization(axis=-1)(conv7)
+
+ up8 = Conv2D(np.int(128 * n_filters_factor), 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2), interpolation='nearest')(bn7))
+ merge8 = concatenate([bn2, up8], axis=3)
+ conv8 = Conv2D(np.int(128 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
+ conv8 = Conv2D(np.int(128 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
+ bn8 = BatchNormalization(axis=-1)(conv8)
+
+ up9 = Conv2D(np.int(64 * n_filters_factor), 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2), interpolation='nearest')(bn8))
+ merge9 = concatenate([conv1, up9], axis=3)
+ conv9 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
+ conv9 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
+ conv9 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
+
+ final_layer_logits = [(Conv2D(n_output_classes, 1, activation='linear')(conv9)) for i in range(n_forecast_months)]
+ final_layer_logits = tf.stack(final_layer_logits, axis=-1)
+
+ if use_temp_scaling:
+ # Temperature scaling of the logits
+ final_layer_logits_scaled = TemperatureScale()(final_layer_logits)
+ final_layer = tf.nn.softmax(final_layer_logits_scaled, axis=-2)
+ else:
+ final_layer = tf.nn.softmax(final_layer_logits, axis=-2)
+
+ model = Model(inputs, final_layer)
+
+ model.compile(optimizer=Adam(lr=learning_rate), loss=loss, weighted_metrics=weighted_metrics)
+
+ return model
+
+
+# Benchmark models:
+# --------------------------------------------------------------------
+
+
+def linear_trend_forecast(forecast_month, n_linear_years='all', da=None, dataset='obs'):
+ '''
+ Returns a simple sea ice forecast based on a gridcell-wise linear extrapolation.
+
+ Parameters:
+ forecast_month (datetime.datetime): The month to forecast
+
+ n_linear_years (int or str): Number of past years to use for linear trend
+ extrapolation.
+
+ da (xr.DataArray): xarray data array to use instead of observational
+ data (used for setting up CMIP6 pre-training linear trend inputs in IceUNetDataPreProcessor).
+
+ dataset (str): 'obs' or 'cmip6'. If 'obs', missing observational SIC months
+ will be skipped
+
+ Returns:
+ output_map (np.ndarray): The output SIC map predicted
+ by fitting a least squares linear trend to the past n_linear_years
+ for the month being predicted.
+
+ sie (np.float): The predicted sea ice extend (SIE).
+ '''
+
+ if da is None:
+ with xr.open_dataset('data/obs/siconca_EASE.nc') as ds:
+ da = next(iter(ds.data_vars.values()))
+
+ valid_dates = [pd.Timestamp(date) for date in da.time.values]
+
+ input_dates = [forecast_month - pd.DateOffset(years=1 + lag) for lag in range(n_linear_years)]
+ input_dates
+
+ # Do not use missing months in the linear trend projection
+ input_dates = [date for date in input_dates if date not in config.missing_dates]
+
+ # Chop off input date from before data start
+ input_dates = [date for date in input_dates if date in valid_dates]
+
+ input_dates = sorted(input_dates)
+
+ # The actual number of past years used
+ actual_n_linear_years = len(input_dates)
+
+ da = da.sel(time=input_dates)
+
+ input_maps = np.array(da.data)
+
+ x = np.arange(actual_n_linear_years)
+ y = input_maps.reshape(actual_n_linear_years, -1)
+
+ # Fit the least squares linear coefficients
+ r = np.linalg.lstsq(np.c_[x, np.ones_like(x)], y, rcond=None)[0]
+
+ # y = mx + c
+ output_map = np.matmul(np.array([actual_n_linear_years, 1]), r).reshape(432, 432)
+
+ land_mask_path = os.path.join(config.mask_data_folder, config.land_mask_filename)
+ land_mask = np.load(land_mask_path)
+ output_map[land_mask] = 0.
+
+ output_map[output_map < 0] = 0.
+ output_map[output_map > 1] = 1.
+
+ sie = np.sum(output_map > 0.15) * 25**2
+
+ return output_map, sie
diff --git a/tools/icenet/norm_params.json b/tools/icenet/norm_params.json
new file mode 100755
index 0000000..b9f6fe8
--- /dev/null
+++ b/tools/icenet/norm_params.json
@@ -0,0 +1 @@
+{"ta500": {"anom": {"mean": -2.1603864274007837e-12, "std": 1.6591156546694987}}, "tos": {"anom": {"mean": 5.822156664378462e-13, "std": 0.52809598578201}}, "rsds": {"anom": {"mean": 1.711579045107998e-06, "std": 1015219.1048228218}}, "rsus": {"anom": {"mean": 9.95651960855671e-13, "std": 7.2490489258776405}}, "psl": {"anom": {"mean": 1.3657589659099027e-09, "std": 394.5617648238881}}, "zg500": {"anom": {"mean": 1.4951655222284315e-10, "std": 50.80324465199198}}, "zg250": {"anom": {"mean": 1.4348836011481582e-10, "std": 70.06606834576085}}, "ua10": {"abs": {"mean": 6.049679936289739, "std": 17.88357735045285}}, "uas": {"abs": {"mean": 0.7007453131053745, "std": 2.359575344121192}}, "vas": {"abs": {"mean": -0.09425203854142171, "std": 1.9121757437379892}}, "tas": {"anom": {"mean": -1.4865481246252265e-12, "std": 1.9800507660473554}}}
\ No newline at end of file
diff --git a/tools/icenet/prepare_era5_data.py b/tools/icenet/prepare_era5_data.py
new file mode 100755
index 0000000..ca4f314
--- /dev/null
+++ b/tools/icenet/prepare_era5_data.py
@@ -0,0 +1,294 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import config
+import xarray as xr
+import argparse
+import iris
+import time
+import warnings
+import numpy as np
+from utils import assignLatLonCoordSystem, fix_near_real_time_era5_func
+import sys
+import os
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+"""
+Script to download monthly-averaged ERA5 reanalysis variables from the Climate
+Data Store (CDS) and regrid them from latitude/longitude to the same EASE grid
+as the OSI-SAF sea ice data.
+
+Single-level (surface) variables:
+https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview
+
+Pressure-level variables:
+https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=overview
+
+The --var command line input controls which variable to download (using CMIP6
+variable naming convention:
+https://docs.google.com/spreadsheets/d/1UUtoz6Ofyjlpx5LdqhKcwHFz2SGoTQV2_yekHyMfL9Y/edit#gid=1221485271.
+The `variables` dict maps from CMIP6 variable names to the CDS naming scheme.
+
+See download_era5_data_in_parallel.sh to download and regrid multiple
+variables in parallel using this script.
+
+Files are saved to data/obs/ in _EASE.nc format.
+
+You need to have set up your ~/.cdsapirc file before running this - see the
+README.
+"""
+
+
+################################################################################
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input", type=str, nargs='+', help="input file")
+parser.add_argument("-v", "--var", type=str, help="variable from the input file")
+args = parser.parse_args()
+variable = args.var
+
+# User input
+################################################################################
+
+# Whether to skip variables that have already been downloaded or regridded
+overwrite = True
+
+do_download = False
+do_regrid = True
+gen_video = False
+
+area = [90, -180, 0, 180] # Latitude/longitude boundaries to download
+
+# Which years to download
+years = ['1979', '1980']
+
+months = ['01', '02']
+
+# Near-real-time data contains ERA5T with 'expver' coord -- remove 'expver'
+# dim and concatenate into one array
+fix_near_real_time_era5_coords = True
+
+# Variable information
+################################################################################
+
+# To add more variables, go to the following dataset sites, fill in the download
+# form for the desired variable, and check the variable's CDI name by clicking
+# 'show API request':
+# - Surface vars: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=form
+# - Pressure level vars: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=overview
+variables = {
+ 'tas': {
+ 'cdi_name': '2m_temperature',
+ },
+ 'ta500': {
+ 'plevel': '500',
+ 'cdi_name': 'temperature',
+ },
+ 'tos': {
+ 'cdi_name': 'sea_surface_temperature',
+ },
+ # RSUS needs to be computed using net solar radiation and downwards radiation,
+ # and need to convert from J/m^2 to W/m^2 by dividing by the number of seconds
+ # in a day (60*60*25)
+ 'rsds_and_rsus': {
+ 'rss_cdi_name': 'surface_net_solar_radiation',
+ 'rsds_cdi_name': 'surface_solar_radiation_downwards',
+ },
+ 'psl': {
+ 'cdi_name': 'mean_sea_level_pressure',
+ },
+ 'zg500': {
+ 'plevel': '500',
+ 'cdi_name': 'geopotential',
+ },
+ 'zg250': {
+ 'plevel': '250',
+ 'cdi_name': 'geopotential',
+ },
+ 'ua10': {
+ 'plevel': '10',
+ 'cdi_name': 'u_component_of_wind',
+ },
+ # Note: the surface wind variables are not regridded here; a separate script
+ # is used to rotate and regrid them.
+ 'uas': {
+ 'cdi_name': '10m_u_component_of_wind',
+ },
+ 'vas': {
+ 'cdi_name': '10m_v_component_of_wind',
+ },
+}
+
+var_dict = variables[variable]
+
+if not os.path.isdir(config.obs_data_folder):
+ os.makedirs(config.obs_data_folder)
+
+# Load SIC data for EASE grid
+################################################################################
+
+sic_day_fpath = os.path.join(config.obs_data_folder, 'ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc')
+
+if not os.path.exists(sic_day_fpath):
+ print("Downloading single daily SIC netCDF file for regridding ERA5 data to EASE grid...\n\n")
+
+ # Ignore "Missing CF-netCDF ancially data variable 'status_flag'" warning
+ warnings.simplefilter("ignore", UserWarning)
+
+ retrieve_sic_day_cmd = 'wget -m -nH --cut-dirs=6 -P {} ' \
+ 'ftp://osisaf.met.no/reprocessed/ice/conc/v2p0/1979/01/ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc'
+ os.system(retrieve_sic_day_cmd.format(config.obs_data_folder))
+
+ print('Done.')
+
+# Load a single SIC map to obtain the EASE grid for regridding ERA data
+sic_EASE_cube = iris.load_cube(sic_day_fpath, 'sea_ice_area_fraction')
+
+# Convert EASE coord units to metres for regridding
+sic_EASE_cube.coord('projection_x_coordinate').convert_units('meters')
+sic_EASE_cube.coord('projection_y_coordinate').convert_units('meters')
+
+land_mask = np.load(os.path.join(config.mask_data_folder, config.land_mask_filename))
+
+# Download and regridding functions
+################################################################################
+
+
+"""def retrieve_CDS_data(var_cdi_name, latlon_path, plevel=None):
+
+ print("\nDownloading data for {}...\n".format(var_cdi_name))
+ tic = time.time()
+
+ if os.path.exists(latlon_path) and overwrite:
+ print("Removing existing file: {}".format(latlon_path))
+ os.remove(latlon_path)
+ elif os.path.exists(latlon_path) and not overwrite:
+ print("Skipping download due to existing file: {}". format(latlon_path))
+ return 0
+
+ cds_dict = {
+ 'product_type': 'monthly_averaged_reanalysis',
+ 'variable': var_cdi_name,
+ 'year': years,
+ 'month': months,
+ 'time': '00:00',
+ 'format': 'netcdf',
+ 'area': area
+ }
+
+ if plevel is not None:
+ dataset = 'reanalysis-era5-pressure-levels-monthly-means'
+ cds_dict['pressure_level'] = plevel
+ else:
+ dataset = 'reanalysis-era5-single-levels-monthly-means'
+
+ cds.retrieve(dataset, cds_dict, latlon_path)
+
+ toc = time.time()
+ print("Done in {:.3f}s.".format(toc - tic))
+
+"""
+
+
+def regrid_var(variable, EASE_path, count=0):
+
+ # Load the monthly averaged ERA5 reanalysis data as iris cubes
+ print("\nRegridding and saving {} reanalysis data... ".format(variable), end='', flush=True)
+ tic = time.time()
+ if variable != 'rsds' and variable != 'rsus':
+ latlon_path = args.input[count]
+ else:
+ latlon_path = os.path.join(config.obs_data_folder, '{}_latlon.nc'.format(variable))
+
+ if os.path.exists(EASE_path) and overwrite:
+ print("Removing existing file: {}". format(EASE_path))
+ os.remove(EASE_path)
+ if os.path.exists(EASE_path) and not overwrite:
+ print("Skipping regrid due to existing file: {}". format(EASE_path))
+ return 0
+
+ if fix_near_real_time_era5_coords:
+ fix_near_real_time_era5_func(latlon_path)
+
+ cube = iris.load_cube(latlon_path)
+ cube = assignLatLonCoordSystem(cube)
+
+ # Regrid onto the EASE grid
+ cube_ease = cube.regrid(sic_EASE_cube, iris.analysis.Linear())
+
+ # Further processing needed
+ if variable in ['tos', 'zg500', 'zg250']:
+ if variable == 'tos':
+ # Overwrite maksed values with zeros
+ cube_ease.data[cube_ease.data > 500.] = 0.
+ cube_ease.data[cube_ease.data < 0.] = 0.
+
+ cube_ease.data[:, land_mask] = 0.
+
+ cube_ease.data = cube_ease.data.data # Remove mask from masked array
+ elif variable in ['zg500', 'zg250']:
+ # Convert from geopotential to geopotential height
+ cube_ease /= 9.80665
+
+ # Save the new cube
+ iris.save(cube_ease, EASE_path)
+
+ toc = time.time()
+ print("Done in {:.3f}s.".format(toc - tic))
+
+
+# Download and regrid
+################################################################################
+
+# cds = cdsapi.Client()
+
+if variable != 'rsds_and_rsus':
+
+ latlon_path = args.input[0]
+
+ if 'plevel' not in var_dict.keys():
+ plevel = None
+ else:
+ plevel = var_dict['plevel']
+
+ if do_regrid:
+ # Regrid to EASE
+ EASE_path = os.path.join(config.obs_data_folder, '{}_EASE.nc'.format(variable))
+ regrid_var(variable, EASE_path, 0)
+
+ # Delete lat-lon data
+ # os.remove(latlon_path)
+
+elif variable == 'rsds_and_rsus':
+
+ da_dict = {}
+ download_paths_dict = {}
+ for count, radiation_variable in enumerate(('rss', 'rsds')):
+
+ latlon_path = args.input[count]
+
+ download_paths_dict[radiation_variable] = latlon_path
+ da_dict[radiation_variable] = xr.open_dataarray(latlon_path)
+
+ # Compute upwelling solar, convert to W/m^2
+ rsus_da = (da_dict['rsds'] - da_dict['rss']) / (60 * 60 * 24)
+ rsus_da = rsus_da.rename('rsus')
+
+ rsds_da = da_dict['rsds'] / (60 * 60 * 24)
+
+ # Delete downloaded data
+ for path in download_paths_dict.values():
+ os.remove(path)
+
+ # Save new, processed data
+ rsds_da.to_netcdf(os.path.join(config.obs_data_folder, 'rsds_latlon.nc'))
+ rsus_da.to_netcdf(os.path.join(config.obs_data_folder, 'rsus_latlon.nc'))
+
+ for count, radiation_variable in enumerate(('rsus', 'rsds')):
+ # Regrid to EASE
+ EASE_path = os.path.join(config.obs_data_folder, '{}_EASE.nc'.format(radiation_variable))
+ regrid_var(radiation_variable, EASE_path, count)
+
+ # Delete lat-lon data
+ # latlon_path = args.input[count]
+ # os.remove(latlon_path)
diff --git a/tools/icenet/prepare_sic_data.py b/tools/icenet/prepare_sic_data.py
new file mode 100755
index 0000000..7e9c839
--- /dev/null
+++ b/tools/icenet/prepare_sic_data.py
@@ -0,0 +1,104 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import config
+from tqdm import tqdm
+import pandas as pd
+import xarray as xr
+import time
+import argparse
+import numpy as np
+from scipy import interpolate
+import os
+import sys
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet'))
+
+'''
+Downloads OSI-SAF SIC data from 1979-present using OpenDAP.
+The dataset comprises OSI-450 (1979-2015) and OSI-430-b (2016-ownards)
+Monthly averages are-computed on the server-side.
+This script can take about an hour to run.
+
+The query URLs were obtained from the following sites:
+ - OSI-450 (1979-2016): https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_v2p0_nh_agg.html
+ - OSI-430-b (2016-present): https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_crb_nh_agg.html
+'''
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input", type=str, help="input file")
+args = parser.parse_args()
+
+if not os.path.exists(config.obs_data_folder):
+ os.makedirs(config.obs_data_folder)
+
+print('Downloading OSI-450 monthly averages... ', end='', flush=True)
+da = xr.open_dataarray(args.input)
+# da = da.resample(time='1MS').mean()
+
+
+# Preprocess the data
+da /= 100. # Convert from SIC % to fraction
+
+mask_fpath_format = os.path.join(config.mask_data_folder,
+ config.active_grid_cell_file_format)
+
+# Load grid cell mask
+mask_dict = {
+ month: np.load(mask_fpath_format.format('{:02d}'.format(month))) for
+ month in np.arange(1, 12 + 1)
+}
+
+dates = [pd.Timestamp(date) for date in da.time.values]
+
+print('Preprocessing the SIC data:')
+tic = time.time()
+x = da['xc'].data
+y = da['yc'].data
+for date in tqdm(dates):
+
+ # Grab mask
+ mask = mask_dict[date.month]
+
+ # Set outside mask to zero
+ da.loc[date].data[~mask] = 0.
+
+ # Grab polar hole
+ skip_interp = False
+ if date <= config.polarhole1_final_date:
+ polarhole_mask = np.load(os.path.join(config.mask_data_folder, config.polarhole1_fname))
+ elif date <= config.polarhole2_final_date:
+ polarhole_mask = np.load(os.path.join(config.mask_data_folder, config.polarhole2_fname))
+ elif date <= config.polarhole3_final_date and config.use_polarhole3:
+ polarhole_mask = np.load(os.path.join(config.mask_data_folder, config.polarhole3_fname))
+ else:
+ skip_interp = True
+
+ # Interpolate polar hole
+ if not skip_interp:
+
+ xx, yy = np.meshgrid(np.arange(432), np.arange(432))
+
+ valid = ~polarhole_mask
+
+ x = xx[valid]
+ y = yy[valid]
+
+ x_interp = xx[polarhole_mask]
+ y_interp = yy[polarhole_mask]
+
+ values = da.loc[date].data[valid]
+
+ interp_vals = interpolate.griddata((x, y), values, (x_interp, y_interp), method='linear')
+ interpolated_array = da.loc[date].data.copy()
+ interpolated_array[polarhole_mask] = interp_vals
+ da.loc[date].data = interpolated_array
+
+dur = time.time() - tic
+print("Done in {}m:{:.0f}s.\n\n ".format(np.floor(dur / 60), dur % 60))
+
+fpath = os.path.join(config.obs_data_folder, 'siconca_EASE.nc')
+print(fpath)
+if os.path.exists(fpath):
+ os.remove(fpath)
+da.to_netcdf(fpath)
+print("worked")
diff --git a/tools/icenet/preproc_icenet_data.py b/tools/icenet/preproc_icenet_data.py
new file mode 100755
index 0000000..345fdbb
--- /dev/null
+++ b/tools/icenet/preproc_icenet_data.py
@@ -0,0 +1,93 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import os
+import sys
+import argparse
+from utils import IceNetDataPreProcessor
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+
+'''
+Use the IceNetDataPreProcessor class to normalise and save climate variables in
+NumPy format for training IceNet.
+
+Raw data is loaded from the data/obs/ and data/cmip6/ folders and the
+processed data is saved in data/network_datasets//.
+
+Normalisation parameters computed over the observational training data are
+stored in a JSON file at data/network_datasets//norm_params.json
+so that they are only computed once. Similarly, monthly climatology fields
+used for computing anomaly fields are saved next to the raw NetCDF files so that
+climatologies are only computed once for each variable.
+
+Note that producing the SIC linear trend forecast fields over all the climate
+simulations can take on the order of an hour to compute.
+'''
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input", type=str, help="config file")
+args = parser.parse_args()
+
+# Path to the dataloader configuration JSON file
+dataloader_config_fpath = args.input
+
+preproc_obs_data = True
+
+# If True, normalisation parameters must have been computed for each variable
+# by running this with `preproc_obs_data` equal to True
+preproc_transfer_data = False # changed to false
+
+# False: Normalise data to have mean=0 and standard deviation=1 or min=-1 and max=+1.
+# True: Normalise data to have min=-1 and max=+1.
+minmax = False
+
+# If 'anom' is True, compute and process anomaly from the climatology over
+# the training years. If 'abs' is True, process the absolute data.
+preproc_vars = {
+ 'siconca': {'anom': False, 'abs': True, 'linear_trend': True},
+ 'tas': {'anom': True, 'abs': False},
+ 'ta500': {'anom': True, 'abs': False},
+ 'tos': {'anom': True, 'abs': False},
+ 'rsds': {'anom': True, 'abs': False},
+ 'rsus': {'anom': True, 'abs': False},
+ 'psl': {'anom': True, 'abs': False},
+ 'zg500': {'anom': True, 'abs': False},
+ 'zg250': {'anom': True, 'abs': False},
+ 'ua10': {'anom': False, 'abs': True},
+ 'uas': {'anom': False, 'abs': True},
+ 'vas': {'anom': False, 'abs': True},
+ 'land': {'metadata': True, 'include': True},
+ 'circmonth': {'metadata': True, 'include': True}
+}
+
+n_linear_years = 35 # Number of past years to used in the linear trend projections
+
+verbose_level = 2
+
+cmip_transfer_data = {
+ 'EC-Earth3': (
+ 'r2i1p1f1',
+ 'r7i1p1f1',
+ 'r10i1p1f1',
+ 'r12i1p1f1',
+ 'r14i1p1f1',
+ ),
+ 'MRI-ESM2-0': (
+ 'r1i1p1f1',
+ 'r2i1p1f1',
+ 'r3i1p1f1',
+ 'r4i1p1f1',
+ 'r5i1p1f1',
+ ),
+}
+
+dpp = IceNetDataPreProcessor(
+ dataloader_config_fpath=dataloader_config_fpath,
+ preproc_vars=preproc_vars,
+ n_linear_years=n_linear_years,
+ minmax=minmax,
+ verbose_level=verbose_level,
+ preproc_obs_data=preproc_obs_data,
+ preproc_transfer_data=preproc_transfer_data,
+ cmip_transfer_data=cmip_transfer_data
+)
diff --git a/tools/icenet/rotate_wind_data.py b/tools/icenet/rotate_wind_data.py
new file mode 100644
index 0000000..9f9fa9c
--- /dev/null
+++ b/tools/icenet/rotate_wind_data.py
@@ -0,0 +1,135 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import config
+import iris
+import xarray as xr
+import time
+import warnings
+import numpy as np
+import utils
+import argparse
+import sys
+import os
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+"""
+Script to rotate ERA5 and CMIP6 wind vector data to the EASE grid. Replaces the
+uas_EASE and vas_EASE files with their rotated counterparts.
+
+The rotate_wind_data_in_parallel.sh bash scipt runs this script in parallel for
+the ERA5 and all climate simulation data in parallel. Note this can have heavy
+CPU demand!
+"""
+
+################################################################################
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--cmip6', default=False, help='Rotate CMIP6 wind rather than ERA5',
+ action='store_true')
+parser.add_argument('--source_id', default='EC-Earth3', type=str)
+parser.add_argument('--member_id', default='r2i1p1f1', type=str)
+commandline_args = parser.parse_args()
+
+cmip6 = commandline_args.cmip6
+source_id = commandline_args.source_id
+member_id = commandline_args.member_id
+
+overwrite = True
+gen_video = False
+
+# Check wind magnitude before and after is the same
+verify_wind_magnitude = True
+
+################################################################################
+
+if not cmip6:
+ wind_data_folder = config.obs_data_folder
+ fname_suffix = '_EASE.nc'
+elif cmip6:
+ wind_data_folder = os.path.join(config.cmip6_data_folder, source_id, member_id)
+ fname_suffix = '_EASE_cmpr.nc'
+
+# Load SIC data for EASE grid
+################################################################################
+
+sic_day_fpath = os.path.join(config.obs_data_folder, 'ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc')
+
+if not os.path.exists(sic_day_fpath):
+ print("Downloading single daily SIC netCDF file for regridding ERA5 data to EASE grid...\n\n")
+
+ # Ignore "Missing CF-netCDF ancially data variable 'status_flag'" warning
+ warnings.simplefilter("ignore", UserWarning)
+
+ retrieve_sic_day_cmd = 'wget -m -nH --cut-dirs=6 -P {} ' \
+ 'ftp://osisaf.met.no/reprocessed/ice/conc/v2p0/1979/01/ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc'
+ os.system(retrieve_sic_day_cmd.format(config.obs_data_folder))
+
+ print('Done.')
+
+# Load a single SIC map to obtain the EASE grid for regridding ERA data
+sic_EASE_cube = iris.load_cube(sic_day_fpath, 'sea_ice_area_fraction')
+
+# Convert EASE coord units to metres for regridding
+sic_EASE_cube.coord('projection_x_coordinate').convert_units('meters')
+sic_EASE_cube.coord('projection_y_coordinate').convert_units('meters')
+
+land_mask = np.load(os.path.join(config.mask_data_folder, config.land_mask_filename))
+
+# get the gridcell angles
+angles = utils.gridcell_angles_from_dim_coords(sic_EASE_cube)
+
+# invert the angles
+utils.invert_gridcell_angles(angles)
+
+# Rotate, regrid, and save
+################################################################################
+
+tic = time.time()
+
+print(f'\nRotating wind data in {wind_data_folder}')
+wind_cubes = {}
+for var in ['uas', 'vas']:
+ EASE_path = os.path.join(wind_data_folder, f'{var}{fname_suffix}')
+ wind_cubes[var] = iris.load_cube(EASE_path)
+
+# rotate the winds using the angles
+wind_cubes_r = {}
+wind_cubes_r['uas'], wind_cubes_r['vas'] = utils.rotate_grid_vectors(
+ wind_cubes['uas'], wind_cubes['vas'], angles)
+
+# save the new cube
+for var, cube_ease_r in wind_cubes_r.items():
+ EASE_path = os.path.join(wind_data_folder, f'{var}{fname_suffix}')
+
+ if os.path.exists(EASE_path) and overwrite:
+ print("Removing existing file: {}". format(EASE_path))
+ os.remove(EASE_path)
+ elif os.path.exists(EASE_path) and not overwrite:
+ print("Skipping due to existing file: {}". format(EASE_path))
+ sys.exit()
+
+ iris.save(cube_ease_r, EASE_path)
+
+if gen_video:
+ for var in ['uas', 'vas']:
+ print(f'generating video for {var}')
+ EASE_path = os.path.join(wind_data_folder, f'{var}{fname_suffix}')
+
+ video_folder = os.path.join('videos', 'wind')
+ if not os.path.exists(video_folder):
+ os.makedirs(video_folder)
+ fname = '{}_{}.mp4'.format(wind_data_folder.replace('/', '_'), var)
+ video_path = os.path.join(video_folder, fname)
+
+ utils.xarray_to_video(
+ da=next(iter(xr.open_dataset(EASE_path).data_vars.values())),
+ video_path=video_path,
+ fps=6,
+ mask=land_mask,
+ figsize=7,
+ dpi=100,
+ )
+
+toc = time.time()
+print("Done in {:.3f}s.".format(toc - tic))
diff --git a/tools/icenet/utils.py b/tools/icenet/utils.py
new file mode 100755
index 0000000..6787625
--- /dev/null
+++ b/tools/icenet/utils.py
@@ -0,0 +1,1955 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import os
+import sys
+import numpy as np
+import tensorflow as tf
+from models import linear_trend_forecast
+import config
+import itertools
+import requests
+import json
+import time
+import re
+import xarray as xr
+import pandas as pd
+from dateutil.relativedelta import relativedelta
+import iris
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+import imageio
+from tqdm import tqdm
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+
+
+###############################################################################
+# DATA PROCESSING & LOADING
+###############################################################################
+
+
+class IceNetDataPreProcessor(object):
+ """
+ Normalises IceNet input data and saves the normalised monthly averages
+ as .npy files. If preprocessing climate model data for transfer learning,
+ the observational normalisation is repeated for the climate model data in order
+ to preserve the mapping from raw values to normalised values.
+
+ Data is stored in the following form with observations separated from climate
+ model transfer learning data:
+ - data/network_datasets//obs/tas/2006_04.npy
+ - data/network_datasets//transfer/MRI-ESM2-0/r1i1p1f1/tas/2056_09.npy
+
+ Normalisation parameters computed over the observational training data are
+ stored in a JSON file at data/network_datasets//norm_params.json
+ so that they are only computed once. Similarly, monthly climatology fields
+ used for computing anomaly fields are saved next to the raw NetCDF files so that
+ climatologies are only computed once for each variable.
+ """
+
+ def __init__(self, dataloader_config_fpath, preproc_vars,
+ n_linear_years, minmax, verbose_level,
+ preproc_obs_data=True,
+ preproc_transfer_data=False, cmip_transfer_data={}):
+ """
+ Parameters:
+
+ dataloader_config_fpath (str): Path to the data loader configuration
+ settings JSON file, defining IceNet's input-output data configuration.
+ This also defines the dataset name, used as the folder name to
+ store the preprocessed data within data/network_datasets/.
+
+ preproc_vars (dict): Which variables to preprocess. Example:
+
+ preproc_vars = {
+ 'siconca': {'anom': True, 'abs': True},
+ 'tas': {'anom': True, 'abs': False},
+ 'tos': {'anom': True, 'abs': False},
+ 'rsds': {'anom': True, 'abs': False},
+ 'rsus': {'anom': True, 'abs': False},
+ 'psl': {'anom': False, 'abs': True},
+ 'zg500': {'anom': False, 'abs': True},
+ 'zg250': {'anom': False, 'abs': True},
+ 'ua10': {'anom': False, 'abs': True},
+ 'uas': {'anom': False, 'abs': True},
+ 'vas': {'anom': False, 'abs': True},
+ 'sfcWind': {'anom': False, 'abs': True},
+ 'land': {'metadata': True, 'include': True},
+ 'circmonth': {'metadata': True, 'include': True}
+ }
+
+ n_linear_years (int): Number of past years to used in the linear trend
+ projections.
+
+ minmax (bool): Whether to use min-max normalisation to (-1, 1) or normalise
+ the mean and standard deviation to 0 and 1.
+
+ verbose_level (int): Controls how much to print. 0: Print nothing.
+ 1: Print key set-up stages. 2: Print debugging info.
+
+ preproc_obs_data (bool): Whether to preprocess observational data
+ (default True).
+
+ preproc_transfer_data (bool): Whether to also preprocess CMIP6 data for each variable
+ (default False).
+
+ cmip_transfer_data (dict): Which CMIP6 models & model runs to
+ preprocess for transfer learning. Example:
+
+ cmip_transfer_data = {
+ 'MRI-ESM2-0': ('r1i1p1f1', 'r2i1p1f1', 'r3i1p1f1',
+ 'r4i1p1f1', 'r5i1p1f1')
+ }
+
+ """
+
+ with open(dataloader_config_fpath, 'r') as readfile:
+ self.config = json.load(readfile)
+
+ self.preproc_vars = preproc_vars
+ self.n_linear_years = n_linear_years
+ self.minmax = minmax
+ self.verbose_level = verbose_level
+ self.preproc_obs_data = preproc_obs_data
+ self.preproc_transfer_data = preproc_transfer_data
+ self.cmip_transfer_data = cmip_transfer_data
+
+ self.load_or_instantiate_norm_params_dict()
+ self.set_obs_train_dates()
+ self.set_up_folder_hierarchy()
+
+ if self.verbose_level >= 1:
+ print("Loading and normalising the raw input maps.\n")
+ tic = time.time()
+
+ self.preproc_and_save_icenet_data()
+
+ if self.verbose_level >= 1:
+ print("\nPreprocessing completed in {:.0f}s.\n".format(time.time() - tic))
+
+ def load_or_instantiate_norm_params_dict(self):
+
+ # Path to JSON file storing normalisation parameters for each variable
+ self.norm_params_fpath = os.path.join(
+ config.network_dataset_folder, self.config['dataset_name'], 'norm_params.json')
+
+ if not os.path.exists(self.norm_params_fpath):
+ self.norm_params = {}
+
+ else:
+ with open(self.norm_params_fpath, 'r') as readfile:
+ self.norm_params = json.load(readfile)
+
+ def set_obs_train_dates(self):
+
+ forecast_start_date_ends = self.config['sample_IDs']['obs_train_dates']
+
+ if forecast_start_date_ends is not None:
+
+ # Convert to Pandas Timestamps
+ forecast_start_date_ends = [
+ pd.Timestamp(date).to_pydatetime() for date in forecast_start_date_ends
+ ]
+
+ self.obs_train_dates = list(pd.date_range(
+ forecast_start_date_ends[0],
+ forecast_start_date_ends[1],
+ freq='MS',
+ closed='right',
+ ))
+
+ def set_up_folder_hierarchy(self):
+
+ """
+ Initialise the folders to store the datasets.
+ """
+
+ if self.verbose_level >= 1:
+ print('Setting up the folder hierarchy for {}... '.format(self.config['dataset_name']),
+ end='', flush=True)
+
+ # Parent folder for this dataset
+ self.dataset_path = os.path.join(config.data_folder, 'network_datasets', self.config['dataset_name'])
+
+ # Dictionary data structure to store folder paths
+ self.paths = {}
+
+ # Set up the folder hierarchy
+ self.paths['obs'] = {}
+
+ for varname, vardict in self.preproc_vars.items():
+
+ if 'metadata' not in vardict.keys():
+ self.paths['obs'][varname] = {}
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format] is True:
+ path = os.path.join(self.dataset_path, 'obs',
+ varname, data_format)
+
+ self.paths['obs'][varname][data_format] = path
+
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ self.paths['transfer'] = {}
+
+ for model_name, member_ids in self.cmip_transfer_data.items():
+ self.paths['transfer'][model_name] = {}
+ for member_id in member_ids:
+ self.paths['transfer'][model_name][member_id] = {}
+
+ for varname, vardict in self.preproc_vars.items():
+
+ if 'metadata' not in vardict.keys():
+ self.paths['transfer'][model_name][member_id][varname] = {}
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format] is True:
+ path = os.path.join(self.dataset_path, 'transfer',
+ model_name, member_id,
+ varname, data_format)
+
+ self.paths['transfer'][model_name][member_id][varname][data_format] = path
+
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ for varname, vardict in self.preproc_vars.items():
+ if 'metadata' in vardict.keys():
+
+ if vardict['include'] is True:
+ path = os.path.join(self.dataset_path, 'meta')
+
+ self.paths['meta'] = path
+
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ if self.verbose_level >= 1:
+ print('Done.')
+
+ @staticmethod
+ def standardise_cmip6_time_coord(da):
+
+ """
+ Convert the cmip6 xarray time dimension to use day=1, hour=0 convention
+ used in the rest of the project.
+ """
+
+ standardised_dates = []
+ for datetime64 in da.time.values:
+ date = pd.Timestamp(datetime64, unit='s')
+ date = date.replace(day=1, hour=0)
+ standardised_dates.append(date)
+ da = da.assign_coords({'time': standardised_dates})
+
+ return da
+
+ @staticmethod
+ def mean_and_std(list, verbose_level=2):
+
+ # Must use float64s to be JSON serialisable
+ mean = np.nanmean(list, dtype=np.float64)
+ std = np.nanstd(list, dtype=np.float64)
+
+ return mean, std
+
+ def normalise_array_using_all_training_months(self, da, minmax=False,
+ mean=None, std=None,
+ min=None, max=None):
+
+ """
+ Using the *training* months only, compute the mean and
+ standard deviation of the input raw satellite DataArray (`da`)
+ and return a normalised version. If minmax=True,
+ instead normalise to lie between min and max of the elements of `array`.
+
+ If min, max, mean, or std are given values other than None,
+ those values are used rather than being computed from the training months.
+
+ Returns:
+ new_da (xarray.DataArray): Normalised array.
+
+ mean, std (float): Pre-computed mean and standard deviation for the
+ normalisation.
+
+ min, max (float): Pre-computed min and max for the normalisation.
+ """
+
+ if (min is not None and max is not None) or (mean is not None and std is not None):
+ # Function has been passed precomputed normalisation parameters
+ pass
+ else:
+ # Function will be computing new normalisation parameters
+ training_samples = da.sel(time=self.obs_train_dates).data
+ training_samples = training_samples.ravel()
+
+ if not minmax:
+ if mean is None and std is None:
+ # Compute mean and std
+ mean, std = IceNetDataPreProcessor.mean_and_std(
+ training_samples, self.verbose_level)
+ elif mean is not None and std is None:
+ # Compute std only
+ _, std = IceNetDataPreProcessor.mean_and_std(
+ training_samples, self.verbose_level)
+ elif mean is None and std is not None:
+ # Compute mean only
+ mean, _ = IceNetDataPreProcessor.mean_and_std(
+ training_samples, self.verbose_level)
+
+ new_da = (da - mean) / std
+
+ elif minmax:
+ if min is None:
+ # Compute min
+ min = np.nanmin(training_samples).astype(np.float64)
+ if max is None:
+ # Compute max
+ max = np.nanmax(training_samples).astype(np.float64)
+
+ new_da = (da - min) / (max - min)
+
+ if minmax:
+ return new_da, min, max
+ elif not minmax:
+ return new_da, mean, std
+
+ def save_xarray_in_monthly_averages(self, da, dataset_type, varname, data_format,
+ model_name=None, member_id=None):
+
+ """
+ Saves an xarray DataArray as monthly averaged .npy files using the
+ self.paths data structure.
+
+ Parameters:
+ da (xarray.DataArray): The DataArray to save.
+
+ dataset_type (str): Either 'obs' or 'transfer' (for CMIP6 data) - the type
+ of dataset being saved.
+
+ varname (str): Variable name being saved.
+
+ data_format (str): Either 'abs' or 'anom' - the format of the data
+ being saved.
+ """
+
+ if self.verbose_level >= 2:
+ print('Saving {} {} monthly averages... '.format(data_format, varname), end='', flush=True)
+
+ # Allow for datasets without a time dimension (a single time slice)
+ dates = da.time.values
+ if hasattr(dates, '__iter__'):
+ pass # Dataset has 'time' dimension; dates already iterable
+ else:
+ dates = [dates] # Convert single time value to iterable
+ da = da.expand_dims({'time': dates})
+
+ for date in dates:
+ slice = da.sel(time=date).data
+ date = pd.Timestamp(date)
+ year_str = '{:04d}'.format(date.year)
+ month_str = '{:02d}'.format(date.month)
+ fname = '{}_{}.npy'.format(year_str, month_str)
+
+ if dataset_type == 'obs':
+ np.save(os.path.join(self.paths[dataset_type][varname][data_format], fname),
+ slice)
+
+ if dataset_type == 'transfer':
+ np.save(os.path.join(self.paths[dataset_type][model_name][member_id][varname][data_format], fname),
+ slice)
+
+ if self.verbose_level >= 2:
+ print('Done.')
+
+ def build_linear_trend_da(self, input_da, dataset):
+
+ """
+ Construct a DataArray `linea_trend_da` containing the linear trend SIC
+ forecasts based on the input DataArray `input_da`.
+
+ `linear_trend_da` will be saved in monthly averages using
+ the `save_xarray_in_monthly_averages` method.
+
+ Parameters:
+ `input_da` (xarray.DataArray): Input DataArray to produce linear SIC
+ forecasts for.
+
+ `dataset` (str): 'obs' or 'cmip6' (dictates whether to skip missing
+ observational months in the linear trend extrapolation)
+
+ Returns:
+ `linear_trend_da` (xarray.DataArray): DataArray whose time slices
+ correspond to the linear trend SIC projection for that month.
+ """
+
+ linear_trend_da = input_da.copy(data=np.zeros(input_da.shape, dtype=np.float32))
+
+ # No prediction possible for the first year of data
+ forecast_dates = input_da.time.values[12:]
+
+ # Convert from datetime64 to pd.Timestamp
+ forecast_dates = [pd.Timestamp(date) for date in forecast_dates]
+
+ # Add on the future year
+ last_year = forecast_dates[-12:]
+ forecast_dates.extend([date + pd.DateOffset(years=1) for date in last_year])
+
+ linear_trend_da = linear_trend_da.assign_coords({'time': forecast_dates})
+
+ for forecast_date in forecast_dates:
+ linear_trend_da.loc[dict(time=forecast_date)] = \
+ linear_trend_forecast(forecast_date, self.n_linear_years, da=input_da, dataset=dataset)[0]
+
+ return linear_trend_da
+
+ def check_if_params_precomputed(self, varname, data_format):
+ ''' Searches self.norm_params for normalisation parameters
+ for a given variable name and data format. '''
+
+ if varname == 'siconca':
+ # No normalisation for SIC
+ return True
+
+ # Grab existing parameters if stored in norm_params JSON file
+ precomputed_params_exists = False
+ if varname in self.norm_params.keys():
+ if data_format in self.norm_params[varname].keys():
+ params = self.norm_params[varname][data_format]
+ if self.minmax:
+ if 'min' in params.keys() and 'max' in params.keys():
+ precomputed_params_exists = True
+ elif not self.minmax:
+ if 'mean' in params.keys() and 'std' in params.keys():
+ precomputed_params_exists = True
+
+ return precomputed_params_exists
+
+ def save_variable(self, varname, data_format, dates=None):
+
+ """
+ Save a normalised 3-dimensional satellite/reanalysis dataset as monthly
+ averages (either the absolute values or the monthly anomalies
+ computed with xarray).
+
+ This method assumes there is only one variable stored in the NetCDF files.
+
+ Parameters:
+ varname (str): Name of the variable to load & save
+
+ data_format (str): 'abs' for absolute values, or 'anom' to compute the
+ anomalies, or 'linear_trend' for SIC linear trend projections.
+
+ dates (list of dates): Months to use to compute the monthly
+ climatologies (defaults to the months used for training).
+ """
+
+ if data_format == 'anom':
+ if dates is None:
+ dates = self.obs_train_dates
+
+ ########################################################################
+ # Observational variable
+ ########################################################################
+
+ if self.preproc_obs_data:
+ if self.verbose_level >= 2:
+ print("Preprocessing {} data for {}... ".format(data_format, varname), end='', flush=True)
+ tic = time.time()
+
+ fpath = os.path.join(config.obs_data_folder, '{}_EASE.nc'.format(varname))
+ with xr.open_dataset(fpath) as ds:
+ da = next(iter(ds.data_vars.values()))
+
+ if data_format == 'anom':
+
+ # Check if climatology already computed
+ train_start = self.obs_train_dates[0].strftime('%Y')
+ train_end = self.obs_train_dates[-1].strftime('%Y')
+
+ climatology_fpath = os.path.join(
+ config.obs_data_folder,
+ '{}_climatology_{}_{}.nc'.format(varname, train_start, train_end))
+
+ if os.path.exists(climatology_fpath):
+ with xr.open_dataset(climatology_fpath) as ds:
+ climatology = next(iter(ds.data_vars.values()))
+ else:
+ climatology = da.sel(time=dates). \
+ groupby("time.month", restore_coord_dims=True).mean("time")
+ climatology.to_netcdf(climatology_fpath)
+
+ da = da.groupby("time.month", restore_coord_dims=True) - climatology
+
+ elif data_format == 'linear_trend':
+ da = self.build_linear_trend_da(da, dataset='obs')
+
+ # Realise the array
+ da.data = np.asarray(da.data, dtype=np.float32)
+
+ # Normalise the array
+ if varname == 'siconca':
+ # Don't normalise SIC - already betw 0 and 1
+ mean, std = None, None
+ min, max = None, None
+
+ elif varname != 'siconca':
+ precomputed_params_exists = self.check_if_params_precomputed(varname, data_format)
+
+ if precomputed_params_exists:
+ if self.minmax:
+ min = self.norm_params[varname][data_format]['min']
+ max = self.norm_params[varname][data_format]['max']
+ if self.verbose_level >= 2:
+ print("Using precomputed min/max: {}/{}... ".format(min, max),
+ end='', flush=True)
+ elif not self.minmax:
+ mean = self.norm_params[varname][data_format]['mean']
+ std = self.norm_params[varname][data_format]['std']
+ if self.verbose_level >= 2:
+ print("Using precomputed mean/std: {}/{}... ".format(mean, std),
+ end='', flush=True)
+ elif not precomputed_params_exists:
+ mean, std = None, None
+ min, max = None, None
+ self.norm_params[varname] = {}
+ self.norm_params[varname][data_format] = {}
+
+ if self.minmax:
+ da, min, max = self.normalise_array_using_all_training_months(
+ da, self.minmax, min=min, max=max)
+ if not precomputed_params_exists:
+ if self.verbose_level >= 2:
+ print("Newly computed min/max: {}/{}... ".format(min, max),
+ end='', flush=True)
+ self.norm_params[varname][data_format]['min'] = min
+ self.norm_params[varname][data_format]['max'] = max
+ elif not self.minmax:
+ da, mean, std = self.normalise_array_using_all_training_months(
+ da, self.minmax, mean=mean, std=std)
+ if not precomputed_params_exists:
+ if self.verbose_level >= 2:
+ print("Newly computed mean/std: {}/{}... ".format(mean, std),
+ end='', flush=True)
+ self.norm_params[varname][data_format]['mean'] = mean
+ self.norm_params[varname][data_format]['std'] = std
+
+ da.data[np.isnan(da.data)] = 0. # Convert any NaNs to zeros
+
+ self.save_xarray_in_monthly_averages(da, 'obs', varname, data_format)
+
+ if self.verbose_level >= 2:
+ print("Done in {:.0f}s.\n".format(time.time() - tic))
+
+ ########################################################################
+ # Transfer variable
+ ########################################################################
+
+ if self.preproc_transfer_data:
+ if self.verbose_level >= 2:
+ print("Preprocessing CMIP6 {} data for {}... ".format(data_format, varname), end='', flush=True)
+ tic = time.time()
+
+ if not self.check_if_params_precomputed(varname, data_format):
+ raise ValueError('Normalisation parameters must be computed '
+ 'from observational data before preprocessing '
+ 'CMIP6 data.')
+
+ elif varname != 'siconca' and self.minmax:
+ min = self.norm_params[varname][data_format]['min']
+ max = self.norm_params[varname][data_format]['max']
+ if self.verbose_level >= 2:
+ print("Using precomputed min/max: {}/{}... ".format(min, max),
+ end='', flush=True)
+
+ elif varname != 'siconca' and not self.minmax:
+ mean = self.norm_params[varname][data_format]['mean']
+ std = self.norm_params[varname][data_format]['std']
+ if self.verbose_level >= 2:
+ print("Using precomputed mean/std: {}/{}... ".format(mean, std),
+ end='', flush=True)
+
+ for model_name, member_ids in self.cmip_transfer_data.items():
+ print('{}: '.format(model_name), end='', flush=True)
+
+ for member_id in member_ids:
+ print('{}, '.format(member_id), end='', flush=True)
+
+ fname = '{}_EASE_cmpr.nc'.format(varname)
+ fpath = os.path.join(config.cmip6_data_folder, model_name, member_id, fname)
+
+ with xr.open_dataset(fpath) as ds:
+ da = next(iter(ds.data_vars.values()))
+
+ # Convert to my month convention of day=1 and time=00:00
+ da = IceNetDataPreProcessor.standardise_cmip6_time_coord(da)
+
+ # Realise the array
+ da.data = np.asarray(da.data, dtype=np.float32)
+
+ if data_format == 'anom':
+
+ climatology = da.sel(time=dates). \
+ groupby("time.month", restore_coord_dims=True).mean("time")
+ da = da.groupby("time.month", restore_coord_dims=True) - climatology
+
+ elif data_format == 'linear_trend':
+ da = self.build_linear_trend_da(da, dataset='cmip6')
+
+ # Normalise the array
+ if varname != 'siconca':
+ if self.minmax:
+ da, _, _ = self.normalise_array_using_all_training_months(
+ da, self.minmax, min=min, max=max)
+ elif not self.minmax:
+ da, _, _ = self.normalise_array_using_all_training_months(
+ da, self.minmax, mean=mean, std=std)
+
+ self.save_xarray_in_monthly_averages(da, 'transfer', varname, data_format,
+ model_name, member_id)
+
+ if self.verbose_level >= 2:
+ print("Done in {:.0f}s.\n".format(time.time() - tic))
+
+ def preproc_and_save_icenet_data(self):
+
+ '''
+ Loop through each variable, preprocessing and saving.
+ '''
+
+ for varname, vardict in self.preproc_vars.items():
+
+ if 'metadata' not in vardict.keys():
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format] is True:
+
+ self.save_variable(varname, data_format)
+
+ elif 'metadata' in vardict.keys():
+
+ if vardict['include']:
+ if varname == 'land':
+ if self.verbose_level >= 2:
+ print("Setting up the land map: ", end='', flush=True)
+
+ land_mask = np.load(os.path.join(config.mask_data_folder, config.land_mask_filename))
+ land_map = np.ones(self.config['raw_data_shape'], np.float32)
+ land_map[~land_mask] = -1.
+
+ np.save(os.path.join(self.paths['meta'], 'land.npy'), land_map)
+
+ print('\n')
+
+ elif varname == 'circmonth':
+ if self.verbose_level >= 2:
+ print("Computing circular month values... ", end='', flush=True)
+ tic = time.time()
+
+ for month in np.arange(1, 13):
+ cos_month = np.cos(2 * np.pi * month / 12, dtype='float32')
+ sin_month = np.sin(2 * np.pi * month / 12, dtype='float32')
+
+ np.save(os.path.join(self.paths['meta'], 'cos_month_{:02d}.npy'.format(month)), cos_month)
+ np.save(os.path.join(self.paths['meta'], 'sin_month_{:02d}.npy'.format(month)), sin_month)
+
+ if self.verbose_level >= 2:
+ print("Done in {:.0f}s.\n".format(time.time() - tic))
+
+ with open(self.norm_params_fpath, 'w') as outfile:
+ json.dump(self.norm_params, outfile)
+
+
+class IceNetDataLoader(tf.keras.utils.Sequence):
+ """
+ Custom data loader class for generating batches of input-output tensors for
+ training IceNet. Inherits from keras.utils.Sequence, which ensures each the
+ network trains once on each sample per epoch. Must implement a __len__
+ method that returns the number of batches and a __getitem__ method that
+ returns a batch of data. The on_epoch_end method is called after each
+ epoch.
+ See: https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence
+
+ """
+
+ def __init__(self, dataloader_config_fpath, seed=None):
+
+ '''
+ Params:
+ dataloader_config_fpath (str): Path to the data loader configuration
+ settings JSON file, defining IceNet's input-output data configuration.
+
+ seed (int): Random seed used for shuffling the training samples before
+ each epoch.
+ '''
+
+ with open(dataloader_config_fpath, 'r') as readfile:
+ self.config = json.load(readfile)
+
+ if seed is None:
+ self.set_seed(self.config['default_seed'])
+ else:
+ self.set_seed(seed)
+
+ self.do_transfer_learning = False
+
+ self.set_obs_forecast_IDs(dataset='train')
+ self.set_transfer_forecast_IDs()
+ self.all_forecast_IDs = self.obs_forecast_IDs
+ self.remove_missing_dates()
+ self.set_variable_path_formats()
+ self.set_number_of_input_channels_for_each_input_variable()
+ self.load_polarholes()
+ self.determine_tot_num_channels()
+ self.on_epoch_end()
+
+ if self.config['verbose_level'] >= 1:
+ print("Setup complete.\n")
+
+ def set_obs_forecast_IDs(self, dataset='train'):
+ """
+ Build up a list of forecast initialisation dates for the train, val, or
+ test sets based on the configuration JSON file start & end points for
+ each dataset.
+ """
+
+ forecast_start_date_ends = self.config['sample_IDs']['obs_{}_dates'.format(dataset)]
+
+ if forecast_start_date_ends is not None:
+
+ # Convert to Pandas Timestamps
+ forecast_start_date_ends = [
+ pd.Timestamp(date).to_pydatetime() for date in forecast_start_date_ends
+ ]
+
+ self.obs_forecast_IDs = list(pd.date_range(
+ forecast_start_date_ends[0],
+ forecast_start_date_ends[1],
+ freq='MS',
+ closed='right',
+ ))
+
+ def set_transfer_forecast_IDs(self):
+
+ '''
+ Use self.cmip6_transfer_train_dict to set up a list array of
+ 3-tuples of the form:
+ (cmip6_model_name, member_id, forecast_start_date)
+
+ This list is used as IDs into the transfer data hierarchy
+ to train on all cmip6 models and and their runs simultaneously.
+ '''
+
+ self.transfer_forecast_IDs = []
+ for cmip6_model_name, member_id_dict in self.config['cmip6_run_dict'].items():
+ for member_id, (start_date, end_date) in member_id_dict.items():
+
+ member_id_dates = list(pd.date_range(
+ start_date,
+ end_date,
+ freq='MS',
+ closed='right',
+ ))
+
+ self.transfer_forecast_IDs.extend(
+ itertools.product([cmip6_model_name], [member_id], member_id_dates)
+ )
+
+ def set_variable_path_formats(self):
+
+ """
+ Initialise the paths to the .npy files of each variable based on
+ `self.config['input_data']`.
+ """
+
+ if self.config['verbose_level'] >= 1:
+ print('Setting up the variable paths for {}... '.format(self.config['dataset_name']),
+ end='', flush=True)
+
+ # Parent folder for this dataset
+ self.dataset_path = os.path.join(config.network_dataset_folder, self.config['dataset_name'])
+
+ # Dictionary data structure to store image variable paths
+ self.variable_paths = {}
+
+ for varname, vardict in self.config['input_data'].items():
+
+ if 'metadata' not in vardict.keys():
+ self.variable_paths[varname] = {}
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format]['include'] is True:
+
+ if not self.do_transfer_learning:
+ path = os.path.join(
+ self.dataset_path, 'obs',
+ varname, data_format, '{:04d}_{:02d}.npy'
+ )
+ elif self.do_transfer_learning:
+ path = os.path.join(
+ self.dataset_path, 'transfer', '{}', '{}',
+ varname, data_format, '{:04d}_{:02d}.npy'
+ )
+
+ self.variable_paths[varname][data_format] = path
+
+ elif 'metadata' in vardict.keys():
+
+ if vardict['include'] is True:
+
+ if varname == 'land':
+ path = os.path.join(self.dataset_path, 'meta', 'land.npy')
+ self.variable_paths['land'] = path
+
+ elif varname == 'circmonth':
+ path = os.path.join(self.dataset_path, 'meta',
+ '{}_month_{:02d}.npy')
+ self.variable_paths['circmonth'] = path
+
+ if self.config['verbose_level'] >= 1:
+ print('Done.')
+
+ def set_seed(self, seed):
+ """
+ Set the seed used by the random generator (used to randomly shuffle
+ the ordering of training samples after each epoch).
+ """
+ if self.config['verbose_level'] >= 1:
+ print("Setting the data generator's random seed to {}".format(seed))
+ self.rng = np.random.default_rng(seed)
+
+ def determine_variable_names(self):
+ """
+ Set up a list of strings for the names of each input variable (in the
+ correct order) by looping over the `input_data` dictionary.
+ """
+ variable_names = []
+
+ for varname, vardict in self.config['input_data'].items():
+ # Input variables that span time
+ if 'metadata' not in vardict.keys():
+ for data_format in vardict.keys():
+ if vardict[data_format]['include']:
+ if data_format != 'linear_trend':
+ for lag in np.arange(1, vardict[data_format]['max_lag'] + 1):
+ variable_names.append(varname + '_{}_{}'.format(data_format, lag))
+ elif data_format == 'linear_trend':
+ for leadtime in np.arange(1, self.config['n_forecast_months'] + 1):
+ variable_names.append(varname + '_{}_{}'.format(data_format, leadtime))
+
+ # Metadata input variables that don't span time
+ elif 'metadata' in vardict.keys() and vardict['include']:
+ if varname == 'land':
+ variable_names.append(varname)
+
+ elif varname == 'circmonth':
+ variable_names.append('cos(month)')
+ variable_names.append('sin(month)')
+
+ return variable_names
+
+ def set_number_of_input_channels_for_each_input_variable(self):
+ """
+ Build up the dict `self.num_input_channels_dict` to store the number of input
+ channels spanned by each input variable.
+ """
+
+ if self.config['verbose_level'] >= 1:
+ print("Setting the number of input months for each input variable.")
+
+ self.num_input_channels_dict = {}
+
+ for varname, vardict in self.config['input_data'].items():
+ if 'metadata' not in vardict.keys():
+ # Variables that span time
+ for data_format in vardict.keys():
+ if vardict[data_format]['include']:
+ varname_format = varname + '_{}'.format(data_format)
+ if data_format != 'linear_trend':
+ self.num_input_channels_dict[varname_format] = vardict[data_format]['max_lag']
+ elif data_format == 'linear_trend':
+ self.num_input_channels_dict[varname_format] = self.config['n_forecast_months']
+
+ # Metadata input variables that don't span time
+ elif 'metadata' in vardict.keys() and vardict['include']:
+ if varname == 'land':
+ self.num_input_channels_dict[varname] = 1
+
+ if varname == 'circmonth':
+ self.num_input_channels_dict[varname] = 2
+
+ def determine_tot_num_channels(self):
+ """
+ Determine the number of channels for the input 3D volumes.
+ """
+
+ self.tot_num_channels = 0
+ for varname, num_channels in self.num_input_channels_dict.items():
+ self.tot_num_channels += num_channels
+
+ def all_sic_input_dates_from_forecast_start_date(self, forecast_start_date):
+ """
+ Return a list of all the SIC dates used as input for a particular forecast
+ date, based on the "max_lag" options of self.config['input_data'].
+ """
+
+ # Find all SIC lags
+ max_lags = []
+ if self.config['input_data']['siconca']['abs']['include']:
+ max_lags.append(self.config['input_data']['siconca']['abs']['max_lag'])
+ if self.config['input_data']['siconca']['anom']['include']:
+ max_lags.append(self.config['input_data']['siconca']['anom']['max_lag'])
+ max_lag = np.max(max_lags)
+
+ input_dates = [
+ forecast_start_date - pd.DateOffset(months=int(lag)) for lag in np.arange(1, max_lag + 1)
+ ]
+
+ return input_dates
+
+ def check_for_missing_date_dependence(self, forecast_start_date):
+ """
+ Check a forecast ID and return a bool for whether any of the input SIC maps
+ are missing. Used to remove forecast IDs that depend on missing SIC data.
+
+ Note: If one of the _forecast_ dates are missing but not _input_ dates,
+ the sample weight matrix for that date will be all zeroes so that the
+ samples for that date do not appear in the loss function.
+ """
+ contains_missing_date = False
+
+ # Check SIC input dates
+ input_dates = self.all_sic_input_dates_from_forecast_start_date(forecast_start_date)
+
+ for input_date in input_dates:
+ if any([input_date == missing_date for missing_date in config.missing_dates]):
+ contains_missing_date = True
+ break
+
+ return contains_missing_date
+
+ def remove_missing_dates(self):
+
+ '''
+ Remove dates from self.obs_forecast_IDs that depend on a missing
+ observation of SIC.
+ '''
+
+ if self.config['verbose_level'] >= 2:
+ print('Checking forecast start dates for missing SIC dates... ', end='', flush=True)
+
+ new_obs_forecast_IDs = []
+ for forecast_start_date in self.obs_forecast_IDs:
+ if self.check_for_missing_date_dependence(forecast_start_date):
+ if self.config['verbose_level'] >= 3:
+ print('Removing {}, '.format(
+ forecast_start_date.strftime('%Y_%m_%d')), end='', flush=True)
+
+ else:
+ new_obs_forecast_IDs.append(forecast_start_date)
+
+ self.obs_forecast_IDs = new_obs_forecast_IDs
+
+ def load_polarholes(self):
+ """
+ Loads each of the polar holes.
+ """
+
+ if self.config['verbose_level'] >= 1:
+ tic = time.time()
+ print("Loading and augmenting the polar holes... ", end='', flush=True)
+
+ polarhole_path = os.path.join(config.mask_data_folder, config.polarhole1_fname)
+ self.polarhole1_mask = np.load(polarhole_path)
+
+ polarhole_path = os.path.join(config.mask_data_folder, config.polarhole2_fname)
+ self.polarhole2_mask = np.load(polarhole_path)
+
+ if config.use_polarhole3:
+ polarhole_path = os.path.join(config.mask_data_folder, config.polarhole3_fname)
+ self.polarhole3_mask = np.load(polarhole_path)
+
+ self.nopolarhole_mask = np.full((432, 432), False)
+
+ if self.config['verbose_level'] >= 1:
+ print("Done in {:.0f}s.\n".format(time.time() - tic))
+
+ def determine_polar_hole_mask(self, forecast_start_date):
+ """
+ Determine which polar hole mask to use (if any) by finding the oldest SIC
+ input month based on the current output month. The polar hole active for
+ the oldest input month is used (because the polar hole size decreases
+ monotonically over time, and we wish to use the largest polar hole for
+ the input data).
+
+ Parameters:
+ forecast_start_date (pd.Timestamp): Timepoint for the forecast initialialisation.
+
+ Returns:
+ polarhole_mask: Mask array with NaNs on polar hole grid cells and 1s
+ elsewhere.
+ """
+
+ oldest_input_date = min(self.all_sic_input_dates_from_forecast_start_date(forecast_start_date))
+
+ if oldest_input_date <= config.polarhole1_final_date:
+ polarhole_mask = self.polarhole1_mask
+ if self.config['verbose_level'] >= 3:
+ print("Forecast start date: {}, polar hole: {}".format(
+ forecast_start_date.strftime("%Y_%m"), 1))
+
+ elif oldest_input_date <= config.polarhole2_final_date:
+ polarhole_mask = self.polarhole2_mask
+ if self.config['verbose_level'] >= 3:
+ print("Forecast start date: {}, polar hole: {}".format(
+ forecast_start_date.strftime("%Y_%m"), 2))
+
+ else:
+ polarhole_mask = self.nopolarhole_mask
+ if self.config['verbose_level'] >= 3:
+ print("Forecast start date: {}, polar hole: {}".format(
+ forecast_start_date.strftime("%Y_%m"), "none"))
+
+ return polarhole_mask
+
+ def determine_active_grid_cell_mask(self, forecast_date):
+ """
+ Determine which active grid cell mask to use (a boolean array with
+ True on active cells and False on inactive cells). The cells with 'True'
+ are where predictions are to be made with IceNet. The active grid cell
+ mask for a particular month is determined by the sum of the land cells,
+ the ocean cells (for that month), and the missing polar hole.
+
+ The mask is used for removing 'inactive' cells (such as land or polar
+ hole cells) from the loss function in self.data_generation.
+ """
+
+ output_month_str = '{:02d}'.format(forecast_date.month)
+ output_active_grid_cell_mask_fname = config.active_grid_cell_file_format. \
+ format(output_month_str)
+ output_active_grid_cell_mask_path = os.path.join(
+ config.mask_data_folder, output_active_grid_cell_mask_fname)
+ output_active_grid_cell_mask = np.load(output_active_grid_cell_mask_path)
+
+ # Only use the polar hole mask if predicting observational data
+ if not self.do_transfer_learning:
+ polarhole_mask = self.determine_polar_hole_mask(forecast_date)
+
+ # Add the polar hole mask to that land/ocean mask for the current month
+ output_active_grid_cell_mask[polarhole_mask] = False
+
+ return output_active_grid_cell_mask
+
+ def turn_on_transfer_learning(self):
+
+ '''
+ Converts the data loader to use CMIP6 pre-training data
+ for transfer learning.
+ '''
+
+ self.do_transfer_learning = True
+ self.all_forecast_IDs = self.transfer_forecast_IDs
+ self.on_epoch_end() # Shuffle transfer training indexes
+ self.set_variable_path_formats()
+
+ def turn_off_transfer_learning(self):
+
+ '''
+ Converts the data loader back to using ERA5/OSI-SAF observational
+ training data.
+ '''
+
+ self.do_transfer_learning = False
+ self.all_forecast_IDs = self.obs_forecast_IDs
+ self.on_epoch_end() # Shuffle transfer training indexes
+ self.set_variable_path_formats()
+
+ def convert_to_validation_data_loader(self):
+
+ """
+ Resets the `all_forecast_IDs` array to correspond to the validation
+ months defined by the data loader configuration file.
+ """
+
+ self.set_obs_forecast_IDs(dataset='val')
+ self.remove_missing_dates()
+ self.all_forecast_IDs = self.obs_forecast_IDs
+
+ def convert_to_test_data_loader(self):
+
+ """
+ As above but for the testing months.
+ """
+
+ self.set_obs_forecast_IDs(dataset='test')
+ self.remove_missing_dates()
+ self.all_forecast_IDs = self.obs_forecast_IDs
+
+ def data_generation(self, forecast_IDs):
+ """
+ Generate input-output data for IceNet for a given forecast ID.
+
+ Parameters:
+ forecast_IDs (list):
+ If self.do_transfer_learning is False, a list of pd.Timestamp objects
+ corresponding to the forecast initialisation dates (first month
+ being forecast) for the batch of X-y data to load.
+
+ If self.do_transfer_learning is True, a list of tuples
+ of the form (cmip6_model_name, member_id, forecast_start_date).
+
+ Returns:
+ X (ndarray): Batch of input 3D volumes.
+
+ y (ndarray): Batch of ground truth output SIC class maps
+
+ sample_weight (ndarray): Batch of pixelwise weights for weighting the
+ loss function (masking outside the active grid cell region and
+ up-weighting summer months).
+ """
+
+ # Allow non-list input for single forecasts
+ forecast_IDs = pd.Timestamp(forecast_IDs) if isinstance(forecast_IDs, str) else forecast_IDs
+ forecast_IDs = [forecast_IDs] if not isinstance(forecast_IDs, list) else forecast_IDs
+
+ current_batch_size = len(forecast_IDs)
+
+ if self.do_transfer_learning:
+ cmip6_model_names = [forecast_ID[0] for forecast_ID in forecast_IDs]
+ cmip6_member_ids = [forecast_ID[1] for forecast_ID in forecast_IDs]
+ forecast_start_dates = [forecast_ID[2] for forecast_ID in forecast_IDs]
+ else:
+ forecast_start_dates = forecast_IDs
+
+ ########################################################################
+ # OUTPUT LABELS
+ ########################################################################
+
+ # Build up the set of N_samps output SIC time-series
+ # (each n_forecast_months long in the time dimension)
+
+ # To become array of shape (N_samps, *raw_data_shape, n_forecast_months)
+ batch_sic_list = []
+
+ # True = forecasts months corresponding to no data
+ missing_month_dict = {}
+
+ for sample_idx, forecast_date in enumerate(forecast_start_dates):
+
+ # To become array of shape (*raw_data_shape, n_forecast_months)
+ sample_sic_list = []
+
+ # List of forecast indexes with missing data
+ missing_month_dict[sample_idx] = []
+
+ for forecast_leadtime_idx in range(self.config['n_forecast_months']):
+
+ forecast_date = forecast_start_dates[sample_idx] + pd.DateOffset(months=forecast_leadtime_idx)
+
+ if self.do_transfer_learning:
+ sample_sic_list.append(
+ np.load(self.variable_paths['siconca']['abs'].format(
+ cmip6_model_names[sample_idx], cmip6_member_ids[sample_idx],
+ forecast_date.year, forecast_date.month))
+ )
+
+ elif not self.do_transfer_learning:
+ if any([forecast_date == missing_date for missing_date in config.missing_dates]):
+ # Output file does not exist
+ sample_sic_list.append(np.zeros(self.config['raw_data_shape']))
+
+ else:
+ fpath = self.variable_paths['siconca']['abs'].format(
+ forecast_date.year, forecast_date.month)
+ if os.path.exists(fpath):
+ sample_sic_list.append(np.load(fpath))
+ else:
+ # Ground truth data doesn't exist: fill with NaNs
+ sample_sic_list.append(
+ np.full(self.config['raw_data_shape'], np.nan, dtype=np.float32))
+
+ batch_sic_list.append(np.stack(sample_sic_list, axis=2))
+
+ batch_sic = np.stack(batch_sic_list, axis=0)
+
+ no_ice_gridcells = batch_sic <= 0.15
+ ice_gridcells = batch_sic >= 0.80
+ marginal_ice_gridcells = ~((no_ice_gridcells) | (ice_gridcells))
+
+ # Categorical representation with channel dimension for class probs
+ y = np.zeros((
+ current_batch_size,
+ *self.config['raw_data_shape'],
+ self.config['n_forecast_months'],
+ 3
+ ), dtype=np.float32)
+
+ y[no_ice_gridcells, 0] = 1
+ y[marginal_ice_gridcells, 1] = 1
+ y[ice_gridcells, 2] = 1
+
+ # Move lead time to final axis
+ y = np.moveaxis(y, source=3, destination=4)
+
+ # Missing months
+ for sample_idx, forecast_leadtime_idx_list in missing_month_dict.items():
+ if len(forecast_leadtime_idx_list) > 0:
+ y[sample_idx, :, :, :, forecast_leadtime_idx_list] = 0
+
+ ########################################################################
+ # PIXELWISE LOSS FUNCTION WEIGHTING
+ ########################################################################
+
+ sample_weight = np.zeros((
+ current_batch_size,
+ *self.config['raw_data_shape'],
+ 1, # Broadcastable class dimension
+ self.config['n_forecast_months']
+ ), dtype=np.float32)
+ for sample_idx, forecast_date in enumerate(forecast_start_dates):
+
+ for forecast_leadtime_idx in range(self.config['n_forecast_months']):
+
+ forecast_date = forecast_start_dates[sample_idx] + pd.DateOffset(months=forecast_leadtime_idx)
+
+ if any([forecast_date == missing_date for missing_date in config.missing_dates]):
+ # Leave sample weighting as all-zeros
+ pass
+
+ else:
+ # Zero loss outside of 'active grid cells'
+ sample_weight_ij = self.determine_active_grid_cell_mask(forecast_date)
+ sample_weight_ij = sample_weight_ij.astype(np.float32)
+
+ # Scale the loss for each month s.t. March is
+ # scaled by 1 and Sept is scaled by 1.77
+ if self.config['loss_weight_months']:
+ sample_weight_ij *= 33928. / np.sum(sample_weight_ij)
+
+ sample_weight[sample_idx, :, :, 0, forecast_leadtime_idx] = \
+ sample_weight_ij
+
+ ########################################################################
+ # INPUT FEATURES
+ ########################################################################
+
+ # Batch tensor
+ X = np.zeros((
+ current_batch_size,
+ *self.config['raw_data_shape'],
+ self.tot_num_channels
+ ), dtype=np.float32)
+
+ # Build up the batch of inputs
+ for sample_idx, forecast_start_date in enumerate(forecast_start_dates):
+
+ present_date = forecast_start_date - relativedelta(months=1)
+
+ # Initialise variable indexes used to fill the input tensor `X`
+ variable_idx1 = 0
+ variable_idx2 = 0
+
+ for varname, vardict in self.config['input_data'].items():
+
+ if 'metadata' not in vardict.keys():
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format]['include']:
+
+ varname_format = '{}_{}'.format(varname, data_format)
+
+ if data_format != 'linear_trend':
+ lbs = range(vardict[data_format]['max_lag'])
+ input_months = [present_date - relativedelta(months=lb) for lb in lbs]
+ elif data_format == 'linear_trend':
+ input_months = [present_date + relativedelta(months=forecast_leadtime)
+ for forecast_leadtime in np.arange(1, self.config['n_forecast_months'] + 1)]
+
+ variable_idx2 += self.num_input_channels_dict[varname_format]
+
+ if not self.do_transfer_learning:
+ X[sample_idx, :, :, variable_idx1:variable_idx2] = \
+ np.stack([np.load(self.variable_paths[varname][data_format].format(
+ date.year, date.month))
+ for date in input_months], axis=-1)
+ elif self.do_transfer_learning:
+ cmip6_model_name = cmip6_model_names[sample_idx]
+ cmip6_member_id = cmip6_member_ids[sample_idx]
+
+ X[sample_idx, :, :, variable_idx1:variable_idx2] = \
+ np.stack([np.load(self.variable_paths[varname][data_format].format(
+ cmip6_model_name, cmip6_member_id, date.year, date.month))
+ for date in input_months], axis=-1)
+
+ variable_idx1 += self.num_input_channels_dict[varname_format]
+
+ elif 'metadata' in vardict.keys() and vardict['include']:
+
+ variable_idx2 += self.num_input_channels_dict[varname]
+
+ if varname == 'land':
+ X[sample_idx, :, :, variable_idx1] = np.load(self.variable_paths['land'])
+
+ elif varname == 'circmonth':
+ X[sample_idx, :, :, variable_idx1] = \
+ np.load(self.variable_paths['circmonth'].format('cos', forecast_start_date.month))
+ X[sample_idx, :, :, variable_idx1 + 1] = \
+ np.load(self.variable_paths['circmonth'].format('sin', forecast_start_date.month))
+
+ variable_idx1 += self.num_input_channels_dict[varname]
+
+ return X, y, sample_weight
+
+ def __getitem__(self, batch_idx):
+ '''
+ Generate one batch of data of size `batch_size` at batch index `batch_idx`
+ into the set of batches in the epoch.
+ '''
+
+ batch_start = batch_idx * self.config['batch_size']
+ batch_end = np.min([(batch_idx + 1) * self.config['batch_size'], len(self.all_forecast_IDs)])
+
+ sample_idxs = np.arange(batch_start, batch_end)
+ batch_IDs = [self.all_forecast_IDs[sample_idx] for sample_idx in sample_idxs]
+
+ return self.data_generation(batch_IDs)
+
+ def __len__(self):
+ ''' Returns the number of batches per training epoch. '''
+ return int(np.ceil(len(self.all_forecast_IDs) / self.config['batch_size']))
+
+ def on_epoch_end(self):
+ """ Randomly shuffles training samples after each epoch. """
+
+ if self.config['verbose_level'] >= 2:
+ print("on_epoch_end called")
+
+ # Randomly shuffle the forecast IDs in-place
+ self.rng.shuffle(self.all_forecast_IDs)
+
+
+# MISC FUNCTIONS
+################################################################################
+
+
+def create_results_dataset_index(model_compute_list, leadtimes,
+ all_target_dates, icenet_ID,
+ icenet_seeds):
+
+ '''
+ Returns a pandas.MultiIndex object of results dataset indexes for a
+ given list of models to compute metrics for. For IceNet, the 'Ensemble
+ member' column delineates the performance of each IceNet ensemble
+ member (identified by the integer random seed value it was trained
+ with) and the ensemble mean models ('ensemble' or 'ensemble_tempscaled').
+ '''
+
+ multi_index = pd.MultiIndex.from_product(
+ [model_compute_list, leadtimes, all_target_dates])
+
+ idxs = []
+ for row in multi_index:
+ model = row[0]
+ row = [[item] for item in row]
+ if model == icenet_ID:
+ idxs.extend(list(itertools.product(*row, icenet_seeds)))
+ else:
+ idxs.extend(list(itertools.product(*row, ['NA'])))
+
+ multi_index = pd.MultiIndex.from_tuples(
+ idxs, names=['Model', 'Leadtime', 'Forecast date', 'Ensemble member']).\
+ reorder_levels(['Model', 'Ensemble member', 'Leadtime', 'Forecast date'])
+
+ return multi_index
+
+
+def make_varname_verbose(varname, leadtime, fc_month_idx):
+
+ '''
+ Takes IceNet short variable name (e.g. siconca_abs_3) and converts it to a
+ long name for a given forecast calendar month and lead time (e.g.
+ 'Feb SIC').
+
+ Inputs:
+ varname: Short variable name.
+ leadtime: Lead time of the forecast.
+ fc_month_index: Mod-12 calendar month index for the month being forecast
+ (e.g. 8 for September)
+
+ Returns:
+ verbose_varname: Long variable name.
+ '''
+
+ month_names = np.array(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec'])
+
+ varname_regex = re.compile('^(.*)_(abs|anom|linear_trend)_([0-9]+)$')
+
+ var_lookup_table = {
+ 'siconca': 'SIC',
+ 'tas': '2m air temperature',
+ 'ta500': '500 hPa air temperature',
+ 'tos': 'sea surface temperature',
+ 'rsds': 'downwelling solar radiation',
+ 'rsus': 'upwelling solar radiation',
+ 'psl': 'sea level pressure',
+ 'zg500': '500 hPa geopotential height',
+ 'zg250': '250 hPa geopotential height',
+ 'ua10': '10 hPa zonal wind speed',
+ 'uas': 'x-direction wind',
+ 'vas': 'y-direction wind'
+ }
+
+ initialisation_month_idx = (fc_month_idx - leadtime) % 12
+
+ varname_match = varname_regex.match(varname)
+
+ field = varname_match[1]
+ data_format = varname_match[2]
+ lead_or_lag = int(varname_match[3])
+
+ verbose_varname = ''
+
+ month_suffix = ' '
+ month_prefix = ''
+ if data_format != 'linear_trend':
+ # Read back from initialisation month to get input lag month
+ lag = lead_or_lag # In no of months
+ input_month_name = month_names[(initialisation_month_idx - lag + 1) % 12]
+
+ if (initialisation_month_idx - lag + 1) // 12 == -1:
+ # Previous calendar year
+ month_prefix = 'Previous '
+
+ elif data_format == 'linear_trend':
+ # Read forward from initialisation month to get linear trend forecast month
+ lead = lead_or_lag # In no of months
+ input_month_name = month_names[(initialisation_month_idx + lead) % 12]
+
+ if (initialisation_month_idx + lead) // 12 == 1:
+ # Next calendar year
+ month_prefix = 'Next '
+
+ # Month the input corresponds to
+ verbose_varname += month_prefix + input_month_name + month_suffix
+
+ # verbose variable name
+ if data_format != 'linear_trend':
+ verbose_varname += var_lookup_table[field]
+ if data_format == 'anom':
+ verbose_varname += ' anomaly'
+ elif data_format == 'linear_trend':
+ verbose_varname += 'linear trend SIC forecast'
+
+ return verbose_varname
+
+
+def make_varname_verbose_any_leadtime(varname):
+
+ ''' As above, but agnostic to what the target month or lead time is. E.g.
+ "SIC (1)" for sea ice concentration at a lag of 1 month. '''
+
+ varname_regex = re.compile('^(.*)_(abs|anom|linear_trend)_([0-9]+)$')
+
+ var_lookup_table = {
+ 'siconca': 'SIC',
+ 'tas': '2m air temperature',
+ 'ta500': '500 hPa air temperature',
+ 'tos': 'sea surface temperature',
+ 'rsds': 'downwelling solar radiation',
+ 'rsus': 'upwelling solar radiation',
+ 'psl': 'sea level pressure',
+ 'zg500': '500 hPa geopotential height',
+ 'zg250': '250 hPa geopotential height',
+ 'ua10': '10 hPa zonal wind speed',
+ 'uas': 'x-direction wind',
+ 'vas': 'y-direction wind',
+ 'land': 'land mask',
+ 'cos(month)': 'cos(init month)',
+ 'sin(month)': 'sin(init month)',
+ }
+
+ exception_vars = ['cos(month)', 'sin(month)', 'land']
+
+ if varname in exception_vars:
+ return var_lookup_table[varname]
+ else:
+ varname_match = varname_regex.match(varname)
+
+ field = varname_match[1]
+ data_format = varname_match[2]
+ lead_or_lag = int(varname_match[3])
+
+ # verbose variable name
+ if data_format != 'linear_trend':
+ verbose_varname = var_lookup_table[field]
+ if data_format == 'anom':
+ verbose_varname += ' anomaly'
+ elif data_format == 'linear_trend':
+ verbose_varname = 'Linear trend SIC forecast'
+
+ verbose_varname += ' ({:.0f})'.format(lead_or_lag)
+
+ return verbose_varname
+
+
+################################################################################
+# FUNCTIONS
+################################################################################
+
+
+def assignLatLonCoordSystem(cube):
+ ''' Assign coordinate system to iris cube to allow regridding. '''
+
+ cube.coord('latitude').coord_system = iris.coord_systems.GeogCS(6367470.0)
+ cube.coord('longitude').coord_system = iris.coord_systems.GeogCS(6367470.0)
+
+ return cube
+
+
+def fix_near_real_time_era5_func(latlon_path):
+
+ '''
+ Near-real-time ERA5 data is classed as a different dataset called 'ERA5T'.
+ This results in a spurious 'expver' dimension. This method detects
+ whether that dim is present and removes it, concatenating into one array
+ '''
+
+ ds = xr.open_dataarray(latlon_path)
+
+ if len(ds.data.shape) == 4:
+ print('Fixing spurious ERA5 "expver dimension for {}.'.format(latlon_path))
+
+ arr = xr.open_dataarray(latlon_path).data
+ arr = ds.data
+ # Expver 1 (ERA5)
+ era5_months = ~np.isnan(arr[:, 0, :, :]).all(axis=(1, 2))
+
+ # Expver 2 (ERA5T - near real time)
+ era5t_months = ~np.isnan(arr[:, 1, :, :]).all(axis=(1, 2))
+
+ ds = xr.concat((ds[era5_months, 0, :], ds[era5t_months, 1, :]), dim='time')
+
+ ds = ds.reset_coords('expver', drop=True)
+
+ os.remove(latlon_path)
+ ds.load().to_netcdf(latlon_path)
+
+
+###############################################################################
+# LEARNING RATE SCHEDULER
+###############################################################################
+
+
+def make_exp_decay_lr_schedule(rate, start_epoch=1, end_epoch=np.inf, verbose=False):
+
+ ''' Returns an exponential learning rate function that multiplies by
+ exp(-rate) each epoch after `start_epoch`. '''
+
+ def lr_scheduler_exp_decay(epoch, lr):
+ ''' Learning rate scheduler for fine tuning.
+ Exponential decrease after start_epoch until end_epoch. '''
+
+ if epoch >= start_epoch and epoch < end_epoch:
+ lr = lr * np.math.exp(-rate)
+
+ if verbose:
+ print('\nSetting learning rate to: {}\n'.format(lr))
+
+ return lr
+
+ return lr_scheduler_exp_decay
+
+
+###############################################################################
+# REGRIDDING VECTOR DATA
+###############################################################################
+
+
+def rotate_grid_vectors(u_cube, v_cube, angles):
+ """
+ Author: Tony Phillips (BAS)
+
+ Wrapper for :func:`~iris.analysis.cartography.rotate_grid_vectors`
+ that can rotate multiple masked spatial fields in one go by iterating
+ over the horizontal spatial axes in slices
+ """
+ # lists to hold slices of rotated vectors
+ u_r_all = iris.cube.CubeList()
+ v_r_all = iris.cube.CubeList()
+
+ # get the X and Y dimension coordinates for each source cube
+ u_xy_coords = [u_cube.coord(axis='x', dim_coords=True),
+ u_cube.coord(axis='y', dim_coords=True)]
+ v_xy_coords = [v_cube.coord(axis='x', dim_coords=True),
+ v_cube.coord(axis='y', dim_coords=True)]
+
+ # iterate over X, Y slices of the source cubes, rotating each in turn
+ for u, v in zip(u_cube.slices(u_xy_coords, ordered=False),
+ v_cube.slices(v_xy_coords, ordered=False)):
+ u_r, v_r = iris.analysis.cartography.rotate_grid_vectors(u, v, angles)
+ u_r_all.append(u_r)
+ v_r_all.append(v_r)
+
+ # return the slices, merged back together into a pair of cubes
+ return (u_r_all.merge_cube(), v_r_all.merge_cube())
+
+
+def gridcell_angles_from_dim_coords(cube):
+ """
+ Author: Tony Phillips (BAS)
+
+ Wrapper for :func:`~iris.analysis.cartography.gridcell_angles`
+ that derives the 2D X and Y lon/lat coordinates from 1D X and Y
+ coordinates identifiable as 'x' and 'y' axes
+
+ The provided cube must have a coordinate system so that its
+ X and Y coordinate bounds (which are derived if necessary)
+ can be converted to lons and lats
+ """
+
+ # get the X and Y dimension coordinates for the cube
+ x_coord = cube.coord(axis='x', dim_coords=True)
+ y_coord = cube.coord(axis='y', dim_coords=True)
+
+ # add bounds if necessary
+ if not x_coord.has_bounds():
+ x_coord = x_coord.copy()
+ x_coord.guess_bounds()
+ if not y_coord.has_bounds():
+ y_coord = y_coord.copy()
+ y_coord.guess_bounds()
+
+ # get the grid cell bounds
+ x_bounds = x_coord.bounds
+ y_bounds = y_coord.bounds
+ nx = x_bounds.shape[0]
+ ny = y_bounds.shape[0]
+
+ # make arrays to hold the ordered X and Y bound coordinates
+ x = np.zeros((ny, nx, 4))
+ y = np.zeros((ny, nx, 4))
+
+ # iterate over the bounds (in order BL, BR, TL, TR), mesh them and
+ # put them into the X and Y bound coordinates (in order BL, BR, TR, TL)
+ c = [0, 1, 3, 2]
+ cind = 0
+ for yi in [0, 1]:
+ for xi in [0, 1]:
+ xy = np.meshgrid(x_bounds[:, xi], y_bounds[:, yi])
+ x[:, :, c[cind]] = xy[0]
+ y[:, :, c[cind]] = xy[1]
+ cind += 1
+
+ # convert the X and Y coordinates to longitudes and latitudes
+ source_crs = cube.coord_system().as_cartopy_crs()
+ target_crs = ccrs.PlateCarree()
+ pts = target_crs.transform_points(source_crs, x.flatten(), y.flatten())
+ lons = pts[:, 0].reshape(x.shape)
+ lats = pts[:, 1].reshape(x.shape)
+
+ # get the angles
+ angles = iris.analysis.cartography.gridcell_angles(lons, lats)
+
+ # add the X and Y dimension coordinates from the cube to the angles cube
+ angles.add_dim_coord(y_coord, 0)
+ angles.add_dim_coord(x_coord, 1)
+
+ # if the cube's X dimension preceeds its Y dimension
+ # transpose the angles to match
+ if cube.coord_dims(x_coord)[0] < cube.coord_dims(y_coord)[0]:
+ angles.transpose()
+
+ return angles
+
+
+def invert_gridcell_angles(angles):
+ """
+ Author: Tony Phillips (BAS)
+
+ Negate a cube of gridcell angles in place, transforming
+ gridcell_angle_from_true_east <--> true_east_from_gridcell_angle
+ """
+ angles.data *= -1
+
+ names = ['true_east_from_gridcell_angle', 'gridcell_angle_from_true_east']
+ name = angles.name()
+ if name in names:
+ angles.rename(names[1 - names.index(name)])
+
+
+###############################################################################
+# CMIP6
+###############################################################################
+
+
+# Below taken from https://hub.binder.pangeo.io/user/pangeo-data-pan--cmip6-examples-ro965nih/lab
+def esgf_search(server="https://esgf-node.llnl.gov/esg-search/search",
+ files_type="OPENDAP", local_node=False, latest=True, project="CMIP6",
+ verbose1=False, verbose2=False, format="application%2Fsolr%2Bjson",
+ use_csrf=False, **search):
+ client = requests.session()
+ payload = search
+ payload["project"] = project
+ payload["type"] = "File"
+ if latest:
+ payload["latest"] = "true"
+ if local_node:
+ payload["distrib"] = "false"
+ if use_csrf:
+ client.get(server)
+ if 'csrftoken' in client.cookies:
+ # Django 1.6 and up
+ csrftoken = client.cookies['csrftoken']
+ else:
+ # older versions
+ csrftoken = client.cookies['csrf']
+ payload["csrfmiddlewaretoken"] = csrftoken
+
+ payload["format"] = format
+
+ offset = 0
+ numFound = 10000
+ all_files = []
+ files_type = files_type.upper()
+ while offset < numFound:
+ payload["offset"] = offset
+ url_keys = []
+ for k in payload:
+ url_keys += ["{}={}".format(k, payload[k])]
+
+ url = "{}/?{}".format(server, "&".join(url_keys))
+ if verbose1:
+ print(url)
+ r = client.get(url)
+ r.raise_for_status()
+ resp = r.json()["response"]
+ numFound = int(resp["numFound"])
+ resp = resp["docs"]
+ offset += len(resp)
+ for d in resp:
+ if verbose2:
+ for k in d:
+ print("{}: {}".format(k, d[k]))
+ url = d["url"]
+ for f in d["url"]:
+ sp = f.split("|")
+ if sp[-1] == files_type:
+ all_files.append(sp[0].split(".html")[0])
+ return sorted(all_files)
+
+
+def regrid_cmip6(cmip6_cube, grid_cube, verbose=False):
+
+ if verbose:
+ tic = time.time()
+ print("regridding... ", end='', flush=True)
+
+ cs = grid_cube.coord_system().ellipsoid
+
+ for coord in ['longitude', 'latitude']:
+ cmip6_cube.coord(coord).coord_system = cs
+
+ cmip6_ease = cmip6_cube.regrid(grid_cube, iris.analysis.Linear())
+
+ if verbose:
+ dur = time.time() - tic
+ print("done in {}m:{:.0f}s... ".format(np.floor(dur / 60), dur % 60), end='', flush=True)
+
+ return cmip6_ease
+
+
+def save_cmip6(cmip6_ease, fpath, compress=True, verbose=False):
+ tic = time.time()
+
+ if compress:
+ if verbose:
+ print('compressing & saving... ', end='', flush=True)
+ iris.fileformats.netcdf.save(cmip6_ease, fpath, complevel=7, zlib=True)
+ else:
+ if verbose:
+ print('saving uncompressed... ', end='', flush=True)
+ iris.save(cmip6_ease, fpath)
+
+ if verbose:
+ dur = time.time() - tic
+ print("done in {}m:{:.0f}s... ".format(np.floor(dur / 60), dur % 60), end='', flush=True)
+
+
+###############################################################################
+# PLOTTING
+###############################################################################
+
+
+def compute_heatmap(results_df, model, seed='NA', metric='Binary accuracy'):
+ '''
+ Returns a binary accuracy heatmap of lead time vs. calendar month
+ for a given model.
+ '''
+
+ month_names = np.array(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
+ 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec'])
+
+ # Mean over calendar month
+ mean_df = results_df.loc[model, seed].reset_index().\
+ groupby(['Calendar month', 'Leadtime']).mean()
+
+ # Pivot
+ heatmap_df = mean_df.reset_index().\
+ pivot('Calendar month', 'Leadtime', metric).reindex(month_names)
+
+ return heatmap_df
+
+
+def arr_to_ice_edge_arr(arr, thresh, land_mask, region_mask):
+
+ '''
+ Compute a boolean mask with True over ice edge contour grid cells using
+ matplotlib.pyplot.contour and an input threshold to define the ice edge
+ (e.g. 0.15 for the 15% SIC ice edge or 0.5 for SIP forecasts). The contour
+ along the coastline is removed using the region mask.
+ '''
+
+ X, Y = np.meshgrid(np.arange(arr.shape[0]), np.arange(arr.shape[1]))
+ X = X.T
+ Y = Y.T
+
+ cs = plt.contour(X, Y, arr, [thresh], alpha=0) # Do not plot on any axes
+ x = []
+ y = []
+ for p in cs.collections[0].get_paths():
+ x_i, y_i = p.vertices.T
+ x.extend(np.round(x_i))
+ y.extend(np.round(y_i))
+ x = np.array(x, int)
+ y = np.array(y, int)
+ ice_edge_arr = np.zeros(arr.shape, dtype=bool)
+ ice_edge_arr[x, y] = True
+ # Mask out ice edge contour that hugs the coastline
+ ice_edge_arr[land_mask] = False
+ ice_edge_arr[region_mask == 13] = False
+
+ return ice_edge_arr
+
+
+def arr_to_ice_edge_rgba_arr(arr, thresh, land_mask, region_mask, rgb):
+
+ ice_edge_arr = arr_to_ice_edge_arr(arr, thresh, land_mask, region_mask)
+
+ # Contour pixels -> alpha=1, alpha=0 elsewhere
+ ice_edge_rgba_arr = np.zeros((*arr.shape, 4))
+ ice_edge_rgba_arr[:, :, 3] = ice_edge_arr
+ ice_edge_rgba_arr[:, :, :3] = rgb
+
+ return ice_edge_rgba_arr
+
+
+###############################################################################
+# VIDEOS
+###############################################################################
+
+
+def xarray_to_video(da, video_path, fps, mask=None, mask_type='contour', clim=None,
+ crop=None, data_type='abs', video_dates=None, cmap='viridis',
+ figsize=15, dpi=300):
+
+ '''
+ Generate video of an xarray.DataArray. Optionally input a list of
+ `video_dates` to show, otherwise the full set of time coordiantes
+ of the dataset is used.
+
+ Parameters:
+ da (xr.DataArray): Dataset to create video of.
+
+ video_path (str): Path to save the video to.
+
+ fps (int): Frames per second of the video.
+
+ mask (np.ndarray): Boolean mask with True over masked elements to overlay
+ as a contour or filled contour. Defaults to None (no mask plotting).
+
+ mask_type (str): 'contour' or 'contourf' dictating whether the mask is overlaid
+ as a contour line or a filled contour.
+
+ data_type (str): 'abs' or 'anom' describing whether the data is in absolute
+ or anomaly format. If anomaly, the colorbar is centred on 0.
+
+ video_dates (list): List of Pandas Timestamps or datetime.datetime objects
+ to plot video from the dataset.
+
+ crop (list): [(a, b), (c, d)] to crop the video from a:b and c:d
+
+ clim (list): Colormap limits. Default is None, in which case the min and max values
+ of the array are used.
+
+ cmap (str): Matplotlib colormap.
+
+ figsize (int or float): Figure size in inches.
+
+ dpi (int): Figure DPI.
+ '''
+
+ if clim is not None:
+ min = clim[0]
+ max = clim[1]
+ elif clim is None:
+ max = da.max().values
+ min = da.min().values
+
+ if data_type == 'anom':
+ if np.abs(max) > np.abs(min):
+ min = -max
+ elif np.abs(min) > np.abs(max):
+ max = -min
+
+ def make_frame(date):
+ fig, ax = plt.subplots(figsize=(figsize, figsize))
+ fig.set_dpi(dpi)
+ im = ax.imshow(da.sel(time=date), cmap=cmap, clim=(min, max))
+ if mask is not None:
+ if mask_type == 'contour':
+ ax.contour(mask, levels=[.5, 1], colors='k')
+ elif mask_type == 'contourf':
+ ax.contourf(mask, levels=[.5, 1], colors='k')
+ ax.axes.xaxis.set_visible(False)
+ ax.axes.yaxis.set_visible(False)
+
+ ax.set_title('{:04d}/{:02d}/{:02d}'.format(date.year, date.month, date.day), fontsize=figsize * 4)
+
+ divider = make_axes_locatable(ax)
+ cax = divider.append_axes('right', size='5%', pad=0.05)
+ plt.colorbar(im, cax)
+
+ # TEMP crop to image
+ # fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
+
+ fig.canvas.draw()
+ image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')
+ image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+
+ plt.close()
+ return image
+
+ if video_dates is None:
+ video_dates = [pd.Timestamp(date).to_pydatetime() for date in da.time.values]
+
+ if crop is not None:
+ a = crop[0][0]
+ b = crop[0][1]
+ c = crop[1][0]
+ d = crop[1][1]
+ da = da.isel(xc=np.arange(a, b), yc=np.arange(c, d))
+ if mask is not None:
+ mask = mask[a:b, c:d]
+
+ imageio.mimsave(video_path,
+ [make_frame(date) for date in tqdm(video_dates)],
+ fps=fps)
diff --git a/tools/icenet/visualize.py b/tools/icenet/visualize.py
new file mode 100755
index 0000000..0da3496
--- /dev/null
+++ b/tools/icenet/visualize.py
@@ -0,0 +1,290 @@
+import os
+import sys
+import argparse
+import utils
+import pandas as pd
+import xarray as xr
+import numpy as np
+import config
+import calendar
+import panel as pn
+import matplotlib.pyplot as plt
+import matplotlib.offsetbox as ob
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet'))
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--siconca", type=str, help="siconca netcdf file")
+parser.add_argument("--forecast", type=str, help="forecast netcdf file")
+parser.add_argument("--forecast_start", type=str, help="forecast start date")
+parser.add_argument("--forecast_end", type=str, help="forecast end date")
+parser.add_argument("--old_results", type=str, help="forecast end date")
+args = parser.parse_args()
+
+
+model = 'IceNet'
+
+dataloader_ID = '2021_09_03_1300_icenet_demo'
+forecast_folder = os.path.join(config.forecast_data_folder, 'icenet', dataloader_ID, model)
+
+if not os.path.exists(forecast_folder):
+ os.makedirs(forecast_folder)
+
+
+# define list of lead times
+leadtimes = np.arange(1, 7)
+
+forecast_start = pd.Timestamp(args.forecast_start)
+forecast_end = pd.Timestamp(args.forecast_end)
+
+all_target_dates = pd.date_range(
+ start=forecast_start,
+ end=forecast_end,
+ freq='MS'
+)
+
+metric_compute_list = ['Binary accuracy', 'SIE error']
+
+forecast_fpath = args.forecast
+
+chunks = {'seed': 1}
+icenet_forecast_da = xr.open_dataarray(forecast_fpath, chunks=chunks)
+icenet_seeds = icenet_forecast_da.seed.values
+
+mask_fpath_format = os.path.join(config.mask_data_folder, 'active_grid_cell_mask_{}.npy')
+
+month_mask_da = xr.DataArray(np.array(
+ [np.load(mask_fpath_format.format('{:02d}'.format(month))) for
+ month in np.arange(1, 12 + 1)],
+))
+
+now = pd.Timestamp.now()
+new_results_df_fname = now.strftime('%Y_%m_%d_%H%M%S_forecast_results.csv')
+new_results_df_fpath = os.path.join(config.forecast_results_folder, new_results_df_fname)
+
+print('New results will be saved to {}\n\n'.format(new_results_df_fpath))
+
+
+old_results_df_fpath = args.old_results
+print('\n\nLoading previous results dataset from {}'.format(old_results_df_fpath))
+
+# Load previous results, do not interpret 'NA' as NaN
+results_df = pd.read_csv(old_results_df_fpath, keep_default_na=False, comment='#')
+
+# Remove existing IceNet results
+results_df = results_df[~results_df['Model'].str.startswith('IceNet')]
+
+# Drop spurious index column if present
+results_df = results_df.drop('Unnamed: 0', axis=1, errors='ignore')
+results_df['Forecast date'] = [pd.Timestamp(date) for date in results_df['Forecast date']]
+
+results_df = results_df.set_index(['Model', 'Ensemble member', 'Leadtime', 'Forecast date'])
+
+# Add new models to the dataframe
+multi_index = utils.create_results_dataset_index([model], leadtimes, all_target_dates, model, icenet_seeds)
+results_df = results_df.append(pd.DataFrame(index=multi_index)).sort_index()
+
+icenet_sip_da = icenet_forecast_da.sel(ice_class=['marginal_ice', 'full_ice']).sum('ice_class')
+
+true_sic_fpath = args.siconca
+true_sic_da = xr.open_dataarray(true_sic_fpath, chunks={})
+true_sic_da = true_sic_da.load()
+true_sic_da = true_sic_da.sel(time=all_target_dates)
+
+if 'Binary accuracy' in metric_compute_list:
+ binary_true_da = true_sic_da > 0.15
+
+print(all_target_dates)
+months = [pd.Timestamp(date).month - 1 for date in all_target_dates]
+mask_da = xr.DataArray(
+ [month_mask_da[month] for month in months],
+ dims=('time', 'yc', 'xc'),
+ coords={
+ 'time': true_sic_da.time.values,
+ 'yc': true_sic_da.yc.values,
+ 'xc': true_sic_da.xc.values,
+ }
+)
+
+print('Analysing forecasts: \n\n')
+
+print('Computing metrics:')
+print(metric_compute_list)
+
+binary_forecast_da = icenet_sip_da > 0.5
+
+compute_ds = xr.Dataset()
+for metric in metric_compute_list:
+
+ if metric == 'Binary accuracy':
+ binary_correct_da = (binary_forecast_da == binary_true_da).astype(np.float32)
+ binary_correct_weighted_da = binary_correct_da.weighted(mask_da)
+
+ # Mean percentage of correct classifications over the active
+ # grid cell area
+ ds_binacc = (binary_correct_weighted_da.mean(dim=['yc', 'xc']) * 100)
+ compute_ds[metric] = ds_binacc
+
+ elif metric == 'SIE error':
+ binary_forecast_weighted_da = binary_forecast_da.astype(int).weighted(mask_da)
+ binary_true_weighted_da = binary_true_da.astype(int).weighted(mask_da)
+
+ ds_sie_error = (
+ binary_forecast_weighted_da.sum(['xc', 'yc']) - binary_true_weighted_da.sum(['xc', 'yc'])
+ ) * 25**2
+
+ compute_ds[metric] = ds_sie_error
+
+print('Writing to results dataset...')
+for compute_da in iter(compute_ds.data_vars.values()):
+ metric = compute_da.name
+
+ compute_df_index = results_df.loc[
+ pd.IndexSlice[model, :, leadtimes, all_target_dates], metric].\
+ droplevel(0).index
+
+ # Ensure indexes are aligned for assigning to results_df
+ compute_df = compute_da.to_dataframe().reset_index().\
+ set_index(['seed', 'leadtime', 'time']).\
+ reindex(index=compute_df_index)
+
+ results_df.loc[pd.IndexSlice[model, :, leadtimes, all_target_dates], metric] = \
+ compute_df.values
+
+print('\nCheckpointing results dataset... ', end='', flush=True)
+results_df.to_csv(new_results_df_fpath)
+print('Done.')
+
+
+settings_lineplots = dict(padding=0.1, height=400, width=700, fontsize={'title': '120%', 'labels': '120%', 'ticks': '100%'})
+
+# Reset index to preprocess results dataset
+results_df = results_df.reset_index()
+
+results_df['Forecast date'] = pd.to_datetime(results_df['Forecast date'])
+
+month_names = np.array(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
+ 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec'])
+forecast_month_names = month_names[results_df['Forecast date'].dt.month.values - 1]
+results_df['Calendar month'] = forecast_month_names
+
+results_df = results_df.set_index(['Model', 'Ensemble member', 'Leadtime', 'Forecast date'])
+
+# subset target period
+results_df = results_df.loc(axis=0)[pd.IndexSlice[:, :, :, slice(forecast_start, forecast_end)]]
+
+results_df = results_df.sort_index()
+
+# set target years
+year = pd.Timestamp(all_target_dates[0]).year
+
+# set sliders
+month_name = [f'{calendar.month_name[m]} {year}' for m in list(range(1, 13))]
+
+month_slider = pn.widgets.DiscreteSlider(name="Month", options=month_name[months[0]:months[len(months) - 1] + 1], value=month_name[months[0]], width=200)
+
+lead_slider = pn.widgets.IntSlider(name="Lead time (months)", start=1, end=4, step=1, value=1, direction='rtl', width=200)
+
+# set boundaries
+mask = np.load(os.path.join(config.mask_data_folder,
+ 'active_grid_cell_mask_{}.npy'.format('03')))
+
+min_0 = np.min(np.argwhere(mask)[:, 0])
+max_0 = np.max(np.argwhere(mask)[:, 0])
+mid_0 = np.mean((min_0, max_0)).astype(int)
+min_1 = np.min(np.argwhere(mask)[:, 1])
+max_1 = np.max(np.argwhere(mask)[:, 1])
+mid_1 = np.mean((min_1, max_1)).astype(int)
+max_diff = np.max([mid_0 - min_0, mid_1 - min_1])
+max_diff *= .85 # Zoom in
+max_diff = int(max_diff)
+top = mid_0 - max_diff + 10
+bot = mid_0 + max_diff + 10
+left = mid_1 - max_diff
+right = mid_1 + max_diff
+
+# land and region masks
+land_mask = np.load(os.path.join(config.mask_data_folder, 'land_mask.npy'))
+region_mask = np.load(os.path.join(config.mask_data_folder, 'region_mask.npy'))
+
+# define coastline and land layers
+arr = region_mask == 13
+coastline_rgba_arr = np.zeros((*arr.shape, 4))
+coastline_rgba_arr[:, :, 3] = arr # alpha channel
+coastline_rgba_arr[:, :, :3] = .5 # black coastline
+land_mask_rgba_arr = np.zeros((*arr.shape, 4))
+land_mask_rgba_arr[:, :, 3] = land_mask # alpha channel
+land_mask_rgba_arr[:, :, :3] = .5 # gray land
+
+# line colours
+pred_ice_edge_rgb = 'green'
+true_ice_edge_rgb = 'black'
+
+
+# define plot function
+@pn.depends(month_slider.param.value, lead_slider.param.value)
+def plot_forecast(month, leadtime):
+ print(month)
+ print(months)
+ print(leadtime)
+ print(forecast_month_names)
+ tdate = pd.Timestamp(year, month_name.index(month) + 1, 1)
+
+ fig0 = plt.Figure(figsize=(8, 8))
+ ax0 = fig0.subplots()
+ # FigureCanvas(fig0) not needed for mpl >= 3.1
+
+ ax0.imshow(coastline_rgba_arr[top:bot, left:right, :], zorder=20)
+ ax0.imshow(land_mask_rgba_arr[top:bot, left:right, :], zorder=1)
+
+ icenet_sip = icenet_sip_da.sel(time=tdate, leadtime=leadtime, seed='ensemble').data
+ ax0.contour(
+ icenet_sip[top:bot, left:right],
+ levels=[0.5],
+ colors=[pred_ice_edge_rgb],
+ zorder=1,
+ linewidths=1.5,
+ )
+
+ groundtruth_sic = true_sic_da.sel(time=tdate)
+ gt_img = (groundtruth_sic > 0.15).data
+
+ ax0.contour(
+ gt_img[top:bot, left:right],
+ levels=[0.5],
+ colors=[true_ice_edge_rgb],
+ zorder=1,
+ linewidths=1.5
+ )
+ ax0.tick_params(which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
+
+ proxy = [plt.Line2D([0], [1], color=true_ice_edge_rgb),
+ plt.Line2D([0], [1], color=pred_ice_edge_rgb)]
+
+ ax0.legend(proxy, ['Observed', 'Predicted'], loc='upper left', fontsize=11)
+
+ ax0.set_title(f'Date = {month} & Lead time = {leadtime} months')
+
+ acc = results_df.loc['IceNet', 'ensemble', leadtime, tdate]['Binary accuracy']
+ sie_err = results_df.loc['IceNet', 'ensemble', leadtime, tdate]['SIE error']
+
+ Afont = {
+ 'backgroundcolor': 'lightgray',
+ 'color': 'black',
+ 'weight': 'normal',
+ 'size': 11, }
+
+ t = ob.AnchoredText('Binary acc: {:.1f}% \nSIE error: {:+.3f} mil km$^2$'.format(acc, sie_err / 1e6), prop=Afont, loc='lower right', pad=0.5, borderpad=0.4, frameon=False)
+ t = ax0.add_artist(t)
+ t.zorder = 21
+
+ return pn.pane.Matplotlib(fig0, tight=True, dpi=150)
+
+
+plot_ie = pn.Row(
+ plot_forecast,
+ pn.Column(pn.Spacer(height=5), month_slider, pn.Spacer(height=15), lead_slider, background='#f0f0f0', sizing_mode="fixed"),
+ width_policy='max', height_policy='max',
+)
+
+plot_ie.save('forecast_visualization.html', embed=True)
diff --git a/tools/preprocess/.shed.yml b/tools/preprocess/.shed.yml
new file mode 100644
index 0000000..aa999e3
--- /dev/null
+++ b/tools/preprocess/.shed.yml
@@ -0,0 +1,10 @@
+name: preprocess
+owner: climate
+description: Preprocess Icenet Data (preprocess)
+homepage_url: https://zenodo.org/record/5176573
+long_description: |
+ This tool is a wrapper to preprocess icenet data (netcdf files).
+remote_repository_url: https://github.com/NordicESMhub/galaxy-tools/tree/master/tools/preprocess
+type: unrestricted
+categories:
+- Climate Analysis
\ No newline at end of file
diff --git a/tools/preprocess/2021_09_03_1300_icenet_demo.json b/tools/preprocess/2021_09_03_1300_icenet_demo.json
new file mode 100644
index 0000000..9f09208
--- /dev/null
+++ b/tools/preprocess/2021_09_03_1300_icenet_demo.json
@@ -0,0 +1,207 @@
+{
+ "dataloader_name": "icenet_demo",
+ "dataset_name": "dataset1",
+ "input_data": {
+ "siconca": {
+ "abs": {
+ "include": true,
+ "max_lag": 12
+ },
+ "anom": {
+ "include": false,
+ "max_lag": 3
+ },
+ "linear_trend": {
+ "include": true
+ }
+ },
+ "tas": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "ta500": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "tos": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "rsds": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "rsus": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "psl": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "zg500": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "zg250": {
+ "abs": {
+ "include": false,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": true,
+ "max_lag": 3
+ }
+ },
+ "ua10": {
+ "abs": {
+ "include": true,
+ "max_lag": 3
+ },
+ "anom": {
+ "include": false,
+ "max_lag": 3
+ }
+ },
+ "uas": {
+ "abs": {
+ "include": true,
+ "max_lag": 1
+ },
+ "anom": {
+ "include": false,
+ "max_lag": 1
+ }
+ },
+ "vas": {
+ "abs": {
+ "include": true,
+ "max_lag": 1
+ },
+ "anom": {
+ "include": false,
+ "max_lag": 1
+ }
+ },
+ "land": {
+ "metadata": true,
+ "include": true
+ },
+ "circmonth": {
+ "metadata": true,
+ "include": true
+ }
+ },
+ "batch_size": 2,
+ "shuffle": true,
+ "n_forecast_months": 6,
+ "sample_IDs": {
+ "obs_train_dates": [
+ "1980-1-1",
+ "1980-2-1"
+ ],
+ "obs_val_dates": [
+ "2012-1-1",
+ "2017-6-1"
+ ],
+ "obs_test_dates": [
+ "2018-1-1",
+ "2019-6-1"
+ ]
+ },
+ "cmip6_run_dict": {
+ "EC-Earth3": {
+ "r2i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r7i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r10i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r12i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r14i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ]
+ },
+ "MRI-ESM2-0": {
+ "r1i1p1f1": [
+ "1851-1-1",
+ "2099-6-1"
+ ],
+ "r2i1p1f1": [
+ "1851-1-1",
+ "2029-6-1"
+ ],
+ "r3i1p1f1": [
+ "1851-1-1",
+ "2029-6-1"
+ ],
+ "r4i1p1f1": [
+ "1851-1-1",
+ "2029-6-1"
+ ],
+ "r5i1p1f1": [
+ "1851-1-1",
+ "2029-6-1"
+ ]
+ }
+ },
+ "raw_data_shape": [
+ 432,
+ 432
+ ],
+ "default_seed": 42,
+ "loss_weight_months": true,
+ "verbose_level": 0
+}
diff --git a/tools/preprocess/config.py b/tools/preprocess/config.py
new file mode 100644
index 0000000..bd2c791
--- /dev/null
+++ b/tools/preprocess/config.py
@@ -0,0 +1,88 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+
+import os
+import pandas as pd
+
+'''
+Defines globals used throughout the codebase.
+'''
+
+###############################################################################
+# Folder structure naming system
+###############################################################################
+
+data_folder = 'data'
+obs_data_folder = os.path.join(data_folder, 'obs')
+cmip6_data_folder = os.path.join(data_folder, 'cmip6')
+mask_data_folder = os.path.join(data_folder, 'masks')
+forecast_data_folder = os.path.join(data_folder, 'forecasts')
+network_dataset_folder = os.path.join(data_folder, 'network_datasets')
+
+dataloader_config_folder = 'dataloader_configs'
+
+networks_folder = 'trained_networks'
+
+results_folder = 'results'
+forecast_results_folder = os.path.join(results_folder, 'forecast_results')
+permute_and_predict_results_folder = os.path.join(results_folder, 'permute_and_predict_results')
+uncertainty_results_folder = os.path.join(results_folder, 'uncertainty_results')
+
+figure_folder = 'figures'
+
+video_folder = 'videos'
+
+active_grid_cell_file_format = 'active_grid_cell_mask_{}.npy'
+land_mask_filename = 'land_mask.npy'
+region_mask_filename = 'region_mask.npy'
+
+###############################################################################
+# Polar hole/missing months
+###############################################################################
+
+# Pre-defined polar hole radii (in number of 25km x 25km grid cells)
+# The polar hole radii were determined from Sections 2.1, 2.2, and 2.3 of
+# http://osisaf.met.no/docs/osisaf_cdop3_ss2_pum_sea-ice-conc-climate-data-record_v2p0.pdf
+polarhole1_radius = 28
+polarhole2_radius = 11
+polarhole3_radius = 3
+
+# Whether or not to mask out the 3rd polar hole mask from
+# Nov 2005 to Dec 2015 with a radius of only 3 grid cells. Including it creates
+# some complications when analysing performance on a validation set that
+# overlaps with the 3rd polar hole period.
+use_polarhole3 = False
+
+polarhole1_fname = 'polarhole1_mask.npy'
+polarhole2_fname = 'polarhole2_mask.npy'
+polarhole3_fname = 'polarhole3_mask.npy'
+
+# Final month that each of the polar holes apply
+# NOTE: 1st of the month chosen arbitrarily throughout as always working wit
+# monthly averages
+polarhole1_final_date = pd.Timestamp('1987-06-01') # 1987 June
+polarhole2_final_date = pd.Timestamp('2005-10-01') # 2005 Oct
+polarhole3_final_date = pd.Timestamp('2015-12-01') # 2015 Dec
+missing_dates = [pd.Timestamp('1986-4-1'), pd.Timestamp('1986-5-1'), pd.Timestamp('1986-6-1'), pd.Timestamp('1987-12-1')]
+
+###############################################################################
+# Weights and biases config (https://docs.wandb.ai/guides/track/advanced/environment-variables)
+###############################################################################
+
+# Get API key from https://wandb.ai/authorize
+WANDB_API_KEY = 'YOUR-KEY-HERE'
+# Absolute path to store wandb generated files (folder must exist)
+# Note: user must have write access
+WANDB_DIR = '/path/to/wandb/dir'
+# Absolute path to wandb config dir (
+WANDB_CONFIG_DIR = '/path/to/wandb/config/dir'
+WANDB_CACHE_DIR = '/path/to/wandb/cache/dir'
+
+###############################################################################
+# ECMWF details
+###############################################################################
+
+ECMWF_API_KEY = 'YOUR-KEY-HERE'
+ECMWF_API_EMAIL = 'YOUR-KEY-HERE'
diff --git a/tools/preprocess/gen_masks.py b/tools/preprocess/gen_masks.py
new file mode 100644
index 0000000..bcb9cba
--- /dev/null
+++ b/tools/preprocess/gen_masks.py
@@ -0,0 +1,304 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+
+import sys
+import os
+import numpy as np
+import xarray as xr
+import shutil
+import config
+import iris
+import warnings
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+
+'''
+Obtains masks for land, the polar holes, OSI-SAF monthly maximum ice extent (the 'active
+grid cell region'), and the Arctic regions & coastline. Figures of the
+masks are saved in the figures/ folder.
+
+The polar hole radii were determined from Sections 2.1, 2.2, and 2.3 of
+http://osisaf.met.no/docs/osisaf_cdop3_ss2_pum_sea-ice-conc-climate-data-record_v2p0.pdf
+
+Region mask: https://nsidc.org/data/polar-stereo/tools_masks.html#region_masks
+'''
+
+###############################################################################
+
+save_active_grid_cell_masks = True # Save the monthly land-lake-ocean masks for active gridcells
+save_land_mask = True # Save the land mask (constant across months)
+save_arctic_region_mask = True # Save Arctic region mask from NSIDC, with coastline cells
+save_polarhole_masks = True # Save the polarhole masks
+save_figures = False # Figures of the max extent/region masks
+
+temp_ice_data_folder = os.path.join(config.mask_data_folder, 'temp')
+
+if not os.path.isdir(config.mask_data_folder):
+ os.makedirs(config.mask_data_folder)
+
+retrieve_cmd_template_osi450 = 'wget --quiet -m -nH --cut-dirs=4 -P ' + temp_ice_data_folder + \
+ ' ftp://osisaf.met.no/reprocessed/ice/conc/v2p0/{:04d}/{:02d}/' + '{}'
+filename_template_osi450 = 'ice_conc_nh_ease2-250_cdr-v2p0_{:04d}{:02d}021200.nc'
+
+# Generate the land-lake-sea mask using the second day from each month of
+# the year 2000 (chosen arbitrarily as the mask is fixed within a calendar month)
+###############################################################################
+
+print("Generating active grid cell region & and masks\n")
+
+year = 2000
+
+if save_figures:
+ fig_folder = os.path.join(config.figure_folder, 'max_extent_masks')
+ if not os.path.exists(fig_folder):
+ os.makedirs(fig_folder)
+
+# Active grid cell masks and land mask
+###############################################################################
+
+for month in range(1, 13):
+
+ # Download the data if not already downloaded
+ filename_osi450 = filename_template_osi450.format(year, month)
+ os.system(retrieve_cmd_template_osi450.format(year, month, filename_osi450))
+
+ year_str = '{:04d}'.format(year)
+ month_str = '{:02d}'.format(month)
+ month_path = os.path.join(temp_ice_data_folder, year_str, month_str)
+
+ # More than just '.listing' was downloaded
+ if len(os.listdir(month_path)) > 1:
+ day_path = os.path.join(month_path, filename_osi450)
+
+ with xr.open_dataset(day_path) as ds:
+ status_flag = ds['status_flag']
+ status_flag = np.array(status_flag.data).astype(np.uint8)
+ status_flag = status_flag.reshape(432, 432)
+
+ # See status flag definition in Table 4 of OSI-SAF documention:
+ # http://osisaf.met.no/docs/osisaf_cdop3_ss2_pum_sea-ice-conc-climate-data-record_v2p0.pdf
+ # Note 'Bit Nr' corresponds to leftmost bit in the byte (e.g. Bit Nr 0
+ # is index 7)
+ binary = np.unpackbits(status_flag, axis=1).reshape(432, 432, 8)
+
+ # Mask out: land, lake, and 'outside max climatology' (i.e. open sea)
+ max_extent_mask = np.sum(binary[:, :, [7, 6, 0]], axis=2).reshape(432, 432) >= 1
+ max_extent_mask = ~max_extent_mask # False outside of max extent
+ max_extent_mask[325:386, 317:380] = False # Remove Caspian and Black seas
+
+ if save_active_grid_cell_masks:
+ mask_filename = config.active_grid_cell_file_format.format(month_str)
+ mask_path = os.path.join(config.mask_data_folder, mask_filename)
+ np.save(mask_path, max_extent_mask)
+
+ if save_land_mask and month == 1:
+ land_mask = np.sum(binary[:, :, [7, 6]], axis=2).reshape(432, 432) >= 1
+ land_mask_path = os.path.join(config.mask_data_folder, config.land_mask_filename)
+ np.save(land_mask_path, land_mask)
+
+ if save_figures:
+ fig, ax = plt.subplots(figsize=(10, 10))
+ ax.imshow(max_extent_mask, cmap='Blues_r')
+ ax.contour(land_mask, colors='k', linewidths=0.3)
+ plt.savefig(os.path.join(fig_folder, month_str + '.png'))
+ plt.close()
+
+ # Delete the downloaded daily data
+ shutil.rmtree(temp_ice_data_folder)
+
+# Arctic region mask
+###############################################################################
+
+if save_arctic_region_mask:
+ # Authors: Tony Phillips (BAS), Tom Andersson (BAS)
+
+ print("Generating NSIDC Arctic sea region array\n")
+
+ # Download the Arctic region mask
+ retrieve_arctic_region_mask_cmd = 'wget --quiet -m -nH --cut-dirs=6 -P {} ' \
+ 'ftp://sidads.colorado.edu/pub/DATASETS/seaice/polar-stereo/tools/region_n.msk'
+ os.system(retrieve_arctic_region_mask_cmd.format(os.path.join(config.mask_data_folder)))
+
+ # Download SIC data for regridding
+ sic_day_fpath = os.path.join(config.mask_data_folder, 'ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc')
+
+ if not os.path.exists(sic_day_fpath):
+
+ # Ignore "Missing CF-netCDF ancially data variable 'status_flag'" warning
+ warnings.simplefilter("ignore", UserWarning)
+
+ retrieve_sic_day_cmd = 'wget --quiet -m -nH --cut-dirs=6 -P {} ' \
+ 'ftp://osisaf.met.no/reprocessed/ice/conc/v2p0/1979/01/ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc'
+ os.system(retrieve_sic_day_cmd.format(config.mask_data_folder))
+
+ sic_EASE_cube = iris.load_cube(sic_day_fpath, 'sea_ice_area_fraction')
+ sic_EASE_cube.coord('projection_x_coordinate').convert_units('meters')
+ sic_EASE_cube.coord('projection_y_coordinate').convert_units('meters')
+
+ # set ellipsoid, coordinate system, grid metadata and polar stereo coordinates for
+ # NSIDC 25km polar stereographic sea ice data
+
+ # ellipsoid -- see http://nsidc.org/data/polar-stereo/ps_grids.html
+ a = 6378273.0
+ e = 0.081816153
+ b = a * ((1.0 - e * e) ** 0.5)
+ ellipsoid = iris.coord_systems.GeogCS(semi_major_axis=a, semi_minor_axis=b)
+
+ # coordinate system -- see ftp://sidads.colorado.edu/pub/tools/mapx/nsidc_maps/Nps.mpp
+ nps_cs = iris.coord_systems.Stereographic(90, -45,
+ false_easting=0.0, false_northing=0.0,
+ true_scale_lat=70,
+ ellipsoid=ellipsoid)
+
+ # grid definition -- see ftp://sidads.colorado.edu/pub/tools/mapx/nsidc_maps/N3B.gpd
+ grid_length = 25000 # in m
+ nx = 304
+ ny = 448
+ cx = 153.5
+ cy = 233.5
+
+ # derive X and Y coordinates of pixel centres -- Y reversed so it starts at the bottom-left
+ x = np.linspace(-cx, (nx - 1) - cx, num=nx) * grid_length
+ y = np.linspace(cy - (ny - 1), cy, num=ny) * grid_length
+
+ # read region data
+ region_file_path = os.path.join(config.mask_data_folder, 'region_n.msk')
+ region_data = np.fromfile(region_file_path, dtype='b', offset=300)
+
+ # reshape and flip the data in the Y-direction
+ region_data = region_data.reshape((ny, nx))[::-1]
+
+ # Shift up by 1 so that class 0 means 'undefined'
+ region_data += 1
+
+ # convert to a cube
+ x_coord = iris.coords.DimCoord(x, 'projection_x_coordinate', units='m', coord_system=nps_cs)
+ y_coord = iris.coords.DimCoord(y, 'projection_y_coordinate', units='m', coord_system=nps_cs)
+
+ regions = iris.cube.Cube(region_data, dim_coords_and_dims=[(y_coord, 0), (x_coord, 1)])
+
+ regions_ease = regions.regrid(sic_EASE_cube, iris.analysis.Nearest(extrapolation_mode='mask'))
+
+ # Save the mask as a Numpy array and remove the temporary files
+ arctic_region_mask = regions_ease.data.data
+
+ # Extract the coastline properly using numpy
+ land_mask_path = os.path.join(config.mask_data_folder, config.land_mask_filename)
+ land_mask = np.load(land_mask_path)
+
+ # Masked elements over sea
+ sea_mask = np.ma.masked_array(np.full((432, 432), 0.))
+ sea_mask[~land_mask] = np.ma.masked
+
+ coast_arrays = {}
+ for direction in ('horiz', 'vertic'):
+
+ # C-style indexing for horizontal raveling; F-style for vertical raveling
+ if direction == 'horiz':
+ order = 'C' # Scan columns fastest
+ elif direction == 'vertic':
+ order = 'F' # Scan rows fastest
+
+ # Tuples with starts and ends indexes of masked element chunks
+ slice_ends = np.ma.clump_masked(sea_mask.ravel(order=order))
+
+ coast_idxs = []
+ coast_idxs.extend([s.start for s in slice_ends])
+ coast_idxs.extend([s.stop - 1 for s in slice_ends])
+
+ coastline_i = np.array(np.full((432, 432), False), order=order)
+ coastline_i.ravel(order=order)[coast_idxs] = True
+ coast_arrays[direction] = coastline_i
+
+ coastline = coast_arrays['horiz'] + coast_arrays['vertic']
+
+ # Remove artefacts along edge of the grid
+ coastline[:, 0] = coastline[0, :] = coastline[:, -1] = coastline[-1, :] = False
+
+ # Paste over the land mask
+ arctic_region_mask[land_mask] = 12
+ # Paste over new coastline grid cells
+ arctic_region_mask[coastline] = 13
+
+ # Make 'Undefined' cells outside of region equal to 'Open sea'
+ arctic_region_mask[arctic_region_mask == 0] = 2
+
+ # Save the Arctic mask array
+ np.save(os.path.join(config.mask_data_folder, config.region_mask_filename), arctic_region_mask)
+
+ # Make pan-Arctic region plot
+ land_map = np.zeros((432, 432))
+ land_map[land_mask] = 1.
+
+ if save_figures:
+ fig_folder = os.path.join(config.figure_folder, 'regions')
+ if not os.path.exists(fig_folder):
+ os.makedirs(fig_folder)
+
+ fig, axes = plt.subplots(figsize=(40, 20), nrows=1, ncols=2)
+ im = axes[0].imshow(regions_ease.data.data, cmap=plt.get_cmap('tab20c', 14), clim=(-0.5, 13.5))
+ axes[0].contour(land_map, levels=[0.5], colors='k', linewidths=0.5)
+ divider = make_axes_locatable(axes[0])
+ cax = divider.append_axes('right', size='5%', pad=0.05)
+ region_names = ['Undefined', 'Lakes', 'Open sea', 'Sea of Okhotsk and Japan',
+ 'Bering Sea', 'Hudson Bay', 'Baffin Bay - Davis Strait - Labrador Sea',
+ 'Greenland Sea', 'Barents and Kara Seas', 'Arctic Ocean',
+ 'Canadian Archipelago', 'Gulf of St. Lawrence', 'Land', 'Coast']
+ formatter = mpl.ticker.FuncFormatter(lambda val, loc: region_names[loc])
+ plt.colorbar(im, cax, ticks=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], format=formatter)
+ axes[0].set_title('Arctic region segmentation', fontsize=20)
+ getattr(mpl.cm, 'Blues_r').set_bad(color='black')
+ axes[1].imshow(sic_EASE_cube.data[0, :], cmap='Blues_r')
+ axes[1].contour(land_map, levels=[0.5], colors='k', linewidths=0.5)
+ axes[1].contour(regions_ease.data.data, levels=np.arange(0, 13, 1), colors='r', linewidths=2)
+ axes[1].set_title('SIC day map with Arctic region segmentation', fontsize=20)
+ plt.savefig(os.path.join(fig_folder, 'all_regions.pdf'))
+ plt.savefig(os.path.join(fig_folder, 'all_regions.png'))
+ plt.close()
+
+ for i, region_str in enumerate(region_names):
+ fig, ax = plt.subplots(figsize=(10, 10))
+
+ ax.contour(land_map, levels=[0.5], colors='k', linewidths=0.5)
+ ax.imshow(sic_EASE_cube.data[0, :], cmap='Blues_r')
+ ax.contour(regions_ease.data.data == i, levels=np.arange(0, 13, 1), colors='r', linewidths=2)
+
+ plt.savefig(os.path.join(fig_folder, region_str + '.pdf'))
+ plt.savefig(os.path.join(fig_folder, region_str + '.png'))
+ plt.close()
+
+ os.remove(sic_day_fpath)
+ os.remove(region_file_path)
+
+# Polar hole masks
+###############################################################################
+
+if save_polarhole_masks:
+
+ print("Generating polar hole masks\n")
+
+ # Generate the polar hole masks
+ x = np.tile(np.arange(0, 432).reshape(432, 1), (1, 432)).astype(np.float32) - 215.5
+ y = np.tile(np.arange(0, 432).reshape(1, 432), (432, 1)).astype(np.float32) - 215.5
+ squaresum = np.square(x) + np.square(y)
+
+ # Jan 1979 - June 1987
+ polarhole1 = np.full((432, 432), False)
+ polarhole1[squaresum < config.polarhole1_radius**2] = True
+ np.save(os.path.join(config.mask_data_folder, config.polarhole1_fname), polarhole1)
+
+ # July 1987 - Oct 2005
+ polarhole2 = np.full((432, 432), False)
+ polarhole2[squaresum < config.polarhole2_radius**2] = True
+ np.save(os.path.join(config.mask_data_folder, config.polarhole2_fname), polarhole2)
+
+ # Nov 2005 - Dec 2015
+ polarhole3 = np.full((432, 432), False)
+ polarhole3[squaresum < config.polarhole3_radius**2] = True
+ np.save(os.path.join(config.mask_data_folder, config.polarhole3_fname), polarhole3)
+
+print("\nDone.")
diff --git a/tools/preprocess/models.py b/tools/preprocess/models.py
new file mode 100644
index 0000000..e3d1abf
--- /dev/null
+++ b/tools/preprocess/models.py
@@ -0,0 +1,191 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import config
+import numpy as np
+import pandas as pd
+import xarray as xr
+import tensorflow as tf
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Conv2D, BatchNormalization, UpSampling2D, \
+ concatenate, MaxPooling2D, Input
+from tensorflow.keras.optimizers import Adam
+import sys
+import os
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+
+'''
+Defines the Python-based sea ice forecasting models, such as the IceNet architecture
+and the linear trend extrapolation model.
+'''
+
+# Custom layers:
+# --------------------------------------------------------------------
+
+
+@tf.keras.utils.register_keras_serializable()
+class TemperatureScale(tf.keras.layers.Layer):
+ '''
+ Implements the temperature scaling layer for probability calibration,
+ as introduced in Guo 2017 (http://proceedings.mlr.press/v70/guo17a.html).
+ '''
+ def __init__(self, **kwargs):
+ super(TemperatureScale, self).__init__()
+ self.temp = tf.Variable(initial_value=1.0, trainable=False,
+ dtype=tf.float32, name='temp')
+
+ def call(self, inputs):
+ ''' Divide the input logits by the T value. '''
+ return tf.divide(inputs, self.temp)
+
+ def get_config(self):
+ ''' For saving and loading networks with this custom layer. '''
+ return {'temp': self.temp.numpy()}
+
+
+# Network architectures:
+# --------------------------------------------------------------------
+
+def unet_batchnorm(input_shape, loss, weighted_metrics, learning_rate=1e-4, filter_size=3,
+ n_filters_factor=1, n_forecast_months=1, use_temp_scaling=False,
+ n_output_classes=3,
+ **kwargs):
+ inputs = Input(shape=input_shape)
+
+ conv1 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
+ conv1 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
+ bn1 = BatchNormalization(axis=-1)(conv1)
+ pool1 = MaxPooling2D(pool_size=(2, 2))(bn1)
+
+ conv2 = Conv2D(np.int(128 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
+ conv2 = Conv2D(np.int(128 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
+ bn2 = BatchNormalization(axis=-1)(conv2)
+ pool2 = MaxPooling2D(pool_size=(2, 2))(bn2)
+
+ conv3 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
+ conv3 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
+ bn3 = BatchNormalization(axis=-1)(conv3)
+ pool3 = MaxPooling2D(pool_size=(2, 2))(bn3)
+
+ conv4 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
+ conv4 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
+ bn4 = BatchNormalization(axis=-1)(conv4)
+ pool4 = MaxPooling2D(pool_size=(2, 2))(bn4)
+
+ conv5 = Conv2D(np.int(512 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
+ conv5 = Conv2D(np.int(512 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
+ bn5 = BatchNormalization(axis=-1)(conv5)
+
+ up6 = Conv2D(np.int(256 * n_filters_factor), 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2), interpolation='nearest')(bn5))
+ merge6 = concatenate([bn4, up6], axis=3)
+ conv6 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
+ conv6 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
+ bn6 = BatchNormalization(axis=-1)(conv6)
+
+ up7 = Conv2D(np.int(256 * n_filters_factor), 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2), interpolation='nearest')(bn6))
+ merge7 = concatenate([bn3, up7], axis=3)
+ conv7 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
+ conv7 = Conv2D(np.int(256 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
+ bn7 = BatchNormalization(axis=-1)(conv7)
+
+ up8 = Conv2D(np.int(128 * n_filters_factor), 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2), interpolation='nearest')(bn7))
+ merge8 = concatenate([bn2, up8], axis=3)
+ conv8 = Conv2D(np.int(128 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
+ conv8 = Conv2D(np.int(128 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
+ bn8 = BatchNormalization(axis=-1)(conv8)
+
+ up9 = Conv2D(np.int(64 * n_filters_factor), 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2), interpolation='nearest')(bn8))
+ merge9 = concatenate([conv1, up9], axis=3)
+ conv9 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
+ conv9 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
+ conv9 = Conv2D(np.int(64 * n_filters_factor), filter_size, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
+
+ final_layer_logits = [(Conv2D(n_output_classes, 1, activation='linear')(conv9)) for i in range(n_forecast_months)]
+ final_layer_logits = tf.stack(final_layer_logits, axis=-1)
+
+ if use_temp_scaling:
+ # Temperature scaling of the logits
+ final_layer_logits_scaled = TemperatureScale()(final_layer_logits)
+ final_layer = tf.nn.softmax(final_layer_logits_scaled, axis=-2)
+ else:
+ final_layer = tf.nn.softmax(final_layer_logits, axis=-2)
+
+ model = Model(inputs, final_layer)
+
+ model.compile(optimizer=Adam(lr=learning_rate), loss=loss, weighted_metrics=weighted_metrics)
+
+ return model
+
+
+# Benchmark models:
+# --------------------------------------------------------------------
+
+
+def linear_trend_forecast(forecast_month, n_linear_years='all', da=None, dataset='obs'):
+ '''
+ Returns a simple sea ice forecast based on a gridcell-wise linear extrapolation.
+
+ Parameters:
+ forecast_month (datetime.datetime): The month to forecast
+
+ n_linear_years (int or str): Number of past years to use for linear trend
+ extrapolation.
+
+ da (xr.DataArray): xarray data array to use instead of observational
+ data (used for setting up CMIP6 pre-training linear trend inputs in IceUNetDataPreProcessor).
+
+ dataset (str): 'obs' or 'cmip6'. If 'obs', missing observational SIC months
+ will be skipped
+
+ Returns:
+ output_map (np.ndarray): The output SIC map predicted
+ by fitting a least squares linear trend to the past n_linear_years
+ for the month being predicted.
+
+ sie (np.float): The predicted sea ice extend (SIE).
+ '''
+
+ if da is None:
+ with xr.open_dataset('data/obs/siconca_EASE.nc') as ds:
+ da = next(iter(ds.data_vars.values()))
+
+ valid_dates = [pd.Timestamp(date) for date in da.time.values]
+
+ input_dates = [forecast_month - pd.DateOffset(years=1 + lag) for lag in range(n_linear_years)]
+ input_dates
+
+ # Do not use missing months in the linear trend projection
+ input_dates = [date for date in input_dates if date not in config.missing_dates]
+
+ # Chop off input date from before data start
+ input_dates = [date for date in input_dates if date in valid_dates]
+
+ input_dates = sorted(input_dates)
+
+ # The actual number of past years used
+ actual_n_linear_years = len(input_dates)
+
+ da = da.sel(time=input_dates)
+
+ input_maps = np.array(da.data)
+
+ x = np.arange(actual_n_linear_years)
+ y = input_maps.reshape(actual_n_linear_years, -1)
+
+ # Fit the least squares linear coefficients
+ r = np.linalg.lstsq(np.c_[x, np.ones_like(x)], y, rcond=None)[0]
+
+ # y = mx + c
+ output_map = np.matmul(np.array([actual_n_linear_years, 1]), r).reshape(432, 432)
+
+ land_mask_path = os.path.join(config.mask_data_folder, config.land_mask_filename)
+ land_mask = np.load(land_mask_path)
+ output_map[land_mask] = 0.
+
+ output_map[output_map < 0] = 0.
+ output_map[output_map > 1] = 1.
+
+ sie = np.sum(output_map > 0.15) * 25**2
+
+ return output_map, sie
diff --git a/tools/preprocess/norm_params.json b/tools/preprocess/norm_params.json
new file mode 100644
index 0000000..b9f6fe8
--- /dev/null
+++ b/tools/preprocess/norm_params.json
@@ -0,0 +1 @@
+{"ta500": {"anom": {"mean": -2.1603864274007837e-12, "std": 1.6591156546694987}}, "tos": {"anom": {"mean": 5.822156664378462e-13, "std": 0.52809598578201}}, "rsds": {"anom": {"mean": 1.711579045107998e-06, "std": 1015219.1048228218}}, "rsus": {"anom": {"mean": 9.95651960855671e-13, "std": 7.2490489258776405}}, "psl": {"anom": {"mean": 1.3657589659099027e-09, "std": 394.5617648238881}}, "zg500": {"anom": {"mean": 1.4951655222284315e-10, "std": 50.80324465199198}}, "zg250": {"anom": {"mean": 1.4348836011481582e-10, "std": 70.06606834576085}}, "ua10": {"abs": {"mean": 6.049679936289739, "std": 17.88357735045285}}, "uas": {"abs": {"mean": 0.7007453131053745, "std": 2.359575344121192}}, "vas": {"abs": {"mean": -0.09425203854142171, "std": 1.9121757437379892}}, "tas": {"anom": {"mean": -1.4865481246252265e-12, "std": 1.9800507660473554}}}
\ No newline at end of file
diff --git a/tools/preprocess/prepare_era5_data.py b/tools/preprocess/prepare_era5_data.py
new file mode 100644
index 0000000..ca4f314
--- /dev/null
+++ b/tools/preprocess/prepare_era5_data.py
@@ -0,0 +1,294 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import config
+import xarray as xr
+import argparse
+import iris
+import time
+import warnings
+import numpy as np
+from utils import assignLatLonCoordSystem, fix_near_real_time_era5_func
+import sys
+import os
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+"""
+Script to download monthly-averaged ERA5 reanalysis variables from the Climate
+Data Store (CDS) and regrid them from latitude/longitude to the same EASE grid
+as the OSI-SAF sea ice data.
+
+Single-level (surface) variables:
+https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview
+
+Pressure-level variables:
+https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=overview
+
+The --var command line input controls which variable to download (using CMIP6
+variable naming convention:
+https://docs.google.com/spreadsheets/d/1UUtoz6Ofyjlpx5LdqhKcwHFz2SGoTQV2_yekHyMfL9Y/edit#gid=1221485271.
+The `variables` dict maps from CMIP6 variable names to the CDS naming scheme.
+
+See download_era5_data_in_parallel.sh to download and regrid multiple
+variables in parallel using this script.
+
+Files are saved to data/obs/ in _EASE.nc format.
+
+You need to have set up your ~/.cdsapirc file before running this - see the
+README.
+"""
+
+
+################################################################################
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input", type=str, nargs='+', help="input file")
+parser.add_argument("-v", "--var", type=str, help="variable from the input file")
+args = parser.parse_args()
+variable = args.var
+
+# User input
+################################################################################
+
+# Whether to skip variables that have already been downloaded or regridded
+overwrite = True
+
+do_download = False
+do_regrid = True
+gen_video = False
+
+area = [90, -180, 0, 180] # Latitude/longitude boundaries to download
+
+# Which years to download
+years = ['1979', '1980']
+
+months = ['01', '02']
+
+# Near-real-time data contains ERA5T with 'expver' coord -- remove 'expver'
+# dim and concatenate into one array
+fix_near_real_time_era5_coords = True
+
+# Variable information
+################################################################################
+
+# To add more variables, go to the following dataset sites, fill in the download
+# form for the desired variable, and check the variable's CDI name by clicking
+# 'show API request':
+# - Surface vars: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=form
+# - Pressure level vars: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=overview
+variables = {
+ 'tas': {
+ 'cdi_name': '2m_temperature',
+ },
+ 'ta500': {
+ 'plevel': '500',
+ 'cdi_name': 'temperature',
+ },
+ 'tos': {
+ 'cdi_name': 'sea_surface_temperature',
+ },
+ # RSUS needs to be computed using net solar radiation and downwards radiation,
+ # and need to convert from J/m^2 to W/m^2 by dividing by the number of seconds
+ # in a day (60*60*25)
+ 'rsds_and_rsus': {
+ 'rss_cdi_name': 'surface_net_solar_radiation',
+ 'rsds_cdi_name': 'surface_solar_radiation_downwards',
+ },
+ 'psl': {
+ 'cdi_name': 'mean_sea_level_pressure',
+ },
+ 'zg500': {
+ 'plevel': '500',
+ 'cdi_name': 'geopotential',
+ },
+ 'zg250': {
+ 'plevel': '250',
+ 'cdi_name': 'geopotential',
+ },
+ 'ua10': {
+ 'plevel': '10',
+ 'cdi_name': 'u_component_of_wind',
+ },
+ # Note: the surface wind variables are not regridded here; a separate script
+ # is used to rotate and regrid them.
+ 'uas': {
+ 'cdi_name': '10m_u_component_of_wind',
+ },
+ 'vas': {
+ 'cdi_name': '10m_v_component_of_wind',
+ },
+}
+
+var_dict = variables[variable]
+
+if not os.path.isdir(config.obs_data_folder):
+ os.makedirs(config.obs_data_folder)
+
+# Load SIC data for EASE grid
+################################################################################
+
+sic_day_fpath = os.path.join(config.obs_data_folder, 'ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc')
+
+if not os.path.exists(sic_day_fpath):
+ print("Downloading single daily SIC netCDF file for regridding ERA5 data to EASE grid...\n\n")
+
+ # Ignore "Missing CF-netCDF ancially data variable 'status_flag'" warning
+ warnings.simplefilter("ignore", UserWarning)
+
+ retrieve_sic_day_cmd = 'wget -m -nH --cut-dirs=6 -P {} ' \
+ 'ftp://osisaf.met.no/reprocessed/ice/conc/v2p0/1979/01/ice_conc_nh_ease2-250_cdr-v2p0_197901021200.nc'
+ os.system(retrieve_sic_day_cmd.format(config.obs_data_folder))
+
+ print('Done.')
+
+# Load a single SIC map to obtain the EASE grid for regridding ERA data
+sic_EASE_cube = iris.load_cube(sic_day_fpath, 'sea_ice_area_fraction')
+
+# Convert EASE coord units to metres for regridding
+sic_EASE_cube.coord('projection_x_coordinate').convert_units('meters')
+sic_EASE_cube.coord('projection_y_coordinate').convert_units('meters')
+
+land_mask = np.load(os.path.join(config.mask_data_folder, config.land_mask_filename))
+
+# Download and regridding functions
+################################################################################
+
+
+"""def retrieve_CDS_data(var_cdi_name, latlon_path, plevel=None):
+
+ print("\nDownloading data for {}...\n".format(var_cdi_name))
+ tic = time.time()
+
+ if os.path.exists(latlon_path) and overwrite:
+ print("Removing existing file: {}".format(latlon_path))
+ os.remove(latlon_path)
+ elif os.path.exists(latlon_path) and not overwrite:
+ print("Skipping download due to existing file: {}". format(latlon_path))
+ return 0
+
+ cds_dict = {
+ 'product_type': 'monthly_averaged_reanalysis',
+ 'variable': var_cdi_name,
+ 'year': years,
+ 'month': months,
+ 'time': '00:00',
+ 'format': 'netcdf',
+ 'area': area
+ }
+
+ if plevel is not None:
+ dataset = 'reanalysis-era5-pressure-levels-monthly-means'
+ cds_dict['pressure_level'] = plevel
+ else:
+ dataset = 'reanalysis-era5-single-levels-monthly-means'
+
+ cds.retrieve(dataset, cds_dict, latlon_path)
+
+ toc = time.time()
+ print("Done in {:.3f}s.".format(toc - tic))
+
+"""
+
+
+def regrid_var(variable, EASE_path, count=0):
+
+ # Load the monthly averaged ERA5 reanalysis data as iris cubes
+ print("\nRegridding and saving {} reanalysis data... ".format(variable), end='', flush=True)
+ tic = time.time()
+ if variable != 'rsds' and variable != 'rsus':
+ latlon_path = args.input[count]
+ else:
+ latlon_path = os.path.join(config.obs_data_folder, '{}_latlon.nc'.format(variable))
+
+ if os.path.exists(EASE_path) and overwrite:
+ print("Removing existing file: {}". format(EASE_path))
+ os.remove(EASE_path)
+ if os.path.exists(EASE_path) and not overwrite:
+ print("Skipping regrid due to existing file: {}". format(EASE_path))
+ return 0
+
+ if fix_near_real_time_era5_coords:
+ fix_near_real_time_era5_func(latlon_path)
+
+ cube = iris.load_cube(latlon_path)
+ cube = assignLatLonCoordSystem(cube)
+
+ # Regrid onto the EASE grid
+ cube_ease = cube.regrid(sic_EASE_cube, iris.analysis.Linear())
+
+ # Further processing needed
+ if variable in ['tos', 'zg500', 'zg250']:
+ if variable == 'tos':
+ # Overwrite maksed values with zeros
+ cube_ease.data[cube_ease.data > 500.] = 0.
+ cube_ease.data[cube_ease.data < 0.] = 0.
+
+ cube_ease.data[:, land_mask] = 0.
+
+ cube_ease.data = cube_ease.data.data # Remove mask from masked array
+ elif variable in ['zg500', 'zg250']:
+ # Convert from geopotential to geopotential height
+ cube_ease /= 9.80665
+
+ # Save the new cube
+ iris.save(cube_ease, EASE_path)
+
+ toc = time.time()
+ print("Done in {:.3f}s.".format(toc - tic))
+
+
+# Download and regrid
+################################################################################
+
+# cds = cdsapi.Client()
+
+if variable != 'rsds_and_rsus':
+
+ latlon_path = args.input[0]
+
+ if 'plevel' not in var_dict.keys():
+ plevel = None
+ else:
+ plevel = var_dict['plevel']
+
+ if do_regrid:
+ # Regrid to EASE
+ EASE_path = os.path.join(config.obs_data_folder, '{}_EASE.nc'.format(variable))
+ regrid_var(variable, EASE_path, 0)
+
+ # Delete lat-lon data
+ # os.remove(latlon_path)
+
+elif variable == 'rsds_and_rsus':
+
+ da_dict = {}
+ download_paths_dict = {}
+ for count, radiation_variable in enumerate(('rss', 'rsds')):
+
+ latlon_path = args.input[count]
+
+ download_paths_dict[radiation_variable] = latlon_path
+ da_dict[radiation_variable] = xr.open_dataarray(latlon_path)
+
+ # Compute upwelling solar, convert to W/m^2
+ rsus_da = (da_dict['rsds'] - da_dict['rss']) / (60 * 60 * 24)
+ rsus_da = rsus_da.rename('rsus')
+
+ rsds_da = da_dict['rsds'] / (60 * 60 * 24)
+
+ # Delete downloaded data
+ for path in download_paths_dict.values():
+ os.remove(path)
+
+ # Save new, processed data
+ rsds_da.to_netcdf(os.path.join(config.obs_data_folder, 'rsds_latlon.nc'))
+ rsus_da.to_netcdf(os.path.join(config.obs_data_folder, 'rsus_latlon.nc'))
+
+ for count, radiation_variable in enumerate(('rsus', 'rsds')):
+ # Regrid to EASE
+ EASE_path = os.path.join(config.obs_data_folder, '{}_EASE.nc'.format(radiation_variable))
+ regrid_var(radiation_variable, EASE_path, count)
+
+ # Delete lat-lon data
+ # latlon_path = args.input[count]
+ # os.remove(latlon_path)
diff --git a/tools/preprocess/prepare_sic_data.py b/tools/preprocess/prepare_sic_data.py
new file mode 100644
index 0000000..7e9c839
--- /dev/null
+++ b/tools/preprocess/prepare_sic_data.py
@@ -0,0 +1,104 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import config
+from tqdm import tqdm
+import pandas as pd
+import xarray as xr
+import time
+import argparse
+import numpy as np
+from scipy import interpolate
+import os
+import sys
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet'))
+
+'''
+Downloads OSI-SAF SIC data from 1979-present using OpenDAP.
+The dataset comprises OSI-450 (1979-2015) and OSI-430-b (2016-ownards)
+Monthly averages are-computed on the server-side.
+This script can take about an hour to run.
+
+The query URLs were obtained from the following sites:
+ - OSI-450 (1979-2016): https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_v2p0_nh_agg.html
+ - OSI-430-b (2016-present): https://thredds.met.no/thredds/dodsC/osisaf/met.no/reprocessed/ice/conc_crb_nh_agg.html
+'''
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input", type=str, help="input file")
+args = parser.parse_args()
+
+if not os.path.exists(config.obs_data_folder):
+ os.makedirs(config.obs_data_folder)
+
+print('Downloading OSI-450 monthly averages... ', end='', flush=True)
+da = xr.open_dataarray(args.input)
+# da = da.resample(time='1MS').mean()
+
+
+# Preprocess the data
+da /= 100. # Convert from SIC % to fraction
+
+mask_fpath_format = os.path.join(config.mask_data_folder,
+ config.active_grid_cell_file_format)
+
+# Load grid cell mask
+mask_dict = {
+ month: np.load(mask_fpath_format.format('{:02d}'.format(month))) for
+ month in np.arange(1, 12 + 1)
+}
+
+dates = [pd.Timestamp(date) for date in da.time.values]
+
+print('Preprocessing the SIC data:')
+tic = time.time()
+x = da['xc'].data
+y = da['yc'].data
+for date in tqdm(dates):
+
+ # Grab mask
+ mask = mask_dict[date.month]
+
+ # Set outside mask to zero
+ da.loc[date].data[~mask] = 0.
+
+ # Grab polar hole
+ skip_interp = False
+ if date <= config.polarhole1_final_date:
+ polarhole_mask = np.load(os.path.join(config.mask_data_folder, config.polarhole1_fname))
+ elif date <= config.polarhole2_final_date:
+ polarhole_mask = np.load(os.path.join(config.mask_data_folder, config.polarhole2_fname))
+ elif date <= config.polarhole3_final_date and config.use_polarhole3:
+ polarhole_mask = np.load(os.path.join(config.mask_data_folder, config.polarhole3_fname))
+ else:
+ skip_interp = True
+
+ # Interpolate polar hole
+ if not skip_interp:
+
+ xx, yy = np.meshgrid(np.arange(432), np.arange(432))
+
+ valid = ~polarhole_mask
+
+ x = xx[valid]
+ y = yy[valid]
+
+ x_interp = xx[polarhole_mask]
+ y_interp = yy[polarhole_mask]
+
+ values = da.loc[date].data[valid]
+
+ interp_vals = interpolate.griddata((x, y), values, (x_interp, y_interp), method='linear')
+ interpolated_array = da.loc[date].data.copy()
+ interpolated_array[polarhole_mask] = interp_vals
+ da.loc[date].data = interpolated_array
+
+dur = time.time() - tic
+print("Done in {}m:{:.0f}s.\n\n ".format(np.floor(dur / 60), dur % 60))
+
+fpath = os.path.join(config.obs_data_folder, 'siconca_EASE.nc')
+print(fpath)
+if os.path.exists(fpath):
+ os.remove(fpath)
+da.to_netcdf(fpath)
+print("worked")
diff --git a/tools/preprocess/preproc_icenet_data.py b/tools/preprocess/preproc_icenet_data.py
new file mode 100644
index 0000000..1fd8224
--- /dev/null
+++ b/tools/preprocess/preproc_icenet_data.py
@@ -0,0 +1,93 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import os
+import sys
+import argparse
+from utils import IceNetDataPreProcessor
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+
+'''
+Use the IceNetDataPreProcessor class to normalise and save climate variables in
+NumPy format for training IceNet.
+
+Raw data is loaded from the data/obs/ and data/cmip6/ folders and the
+processed data is saved in data/network_datasets//.
+
+Normalisation parameters computed over the observational training data are
+stored in a JSON file at data/network_datasets//norm_params.json
+so that they are only computed once. Similarly, monthly climatology fields
+used for computing anomaly fields are saved next to the raw NetCDF files so that
+climatologies are only computed once for each variable.
+
+Note that producing the SIC linear trend forecast fields over all the climate
+simulations can take on the order of an hour to compute.
+'''
+parser = argparse.ArgumentParser()
+parser.add_argument("-i", "--input", type=str, help="config file")
+args = parser.parse_args()
+
+# Path to the dataloader configuration JSON file
+dataloader_config_fpath = args.input
+
+preproc_obs_data = True
+
+# If True, normalisation parameters must have been computed for each variable
+# by running this with `preproc_obs_data` equal to True
+preproc_transfer_data = False # changed to false
+
+# False: Normalise data to have mean=0 and standard deviation=1 or min=-1 and max=+1.
+# True: Normalise data to have min=-1 and max=+1.
+minmax = False
+
+# If 'anom' is True, compute and process anomaly from the climatology over
+# the training years. If 'abs' is True, process the absolute data.
+preproc_vars = {
+ 'siconca': {'anom': False, 'abs': True, 'linear_trend': False}, # changed linear trend to false
+ 'tas': {'anom': True, 'abs': False},
+ 'ta500': {'anom': True, 'abs': False},
+ 'tos': {'anom': True, 'abs': False},
+ 'rsds': {'anom': True, 'abs': False},
+ 'rsus': {'anom': True, 'abs': False},
+ 'psl': {'anom': True, 'abs': False},
+ 'zg500': {'anom': True, 'abs': False},
+ 'zg250': {'anom': True, 'abs': False},
+ 'ua10': {'anom': False, 'abs': True},
+ 'uas': {'anom': False, 'abs': True},
+ 'vas': {'anom': False, 'abs': True},
+ 'land': {'metadata': True, 'include': True},
+ 'circmonth': {'metadata': True, 'include': True}
+}
+
+n_linear_years = 35 # Number of past years to used in the linear trend projections
+
+verbose_level = 2
+
+cmip_transfer_data = {
+ 'EC-Earth3': (
+ 'r2i1p1f1',
+ 'r7i1p1f1',
+ 'r10i1p1f1',
+ 'r12i1p1f1',
+ 'r14i1p1f1',
+ ),
+ 'MRI-ESM2-0': (
+ 'r1i1p1f1',
+ 'r2i1p1f1',
+ 'r3i1p1f1',
+ 'r4i1p1f1',
+ 'r5i1p1f1',
+ ),
+}
+
+dpp = IceNetDataPreProcessor(
+ dataloader_config_fpath=dataloader_config_fpath,
+ preproc_vars=preproc_vars,
+ n_linear_years=n_linear_years,
+ minmax=minmax,
+ verbose_level=verbose_level,
+ preproc_obs_data=preproc_obs_data,
+ preproc_transfer_data=preproc_transfer_data,
+ cmip_transfer_data=cmip_transfer_data
+)
diff --git a/tools/preprocess/preprocess.xml b/tools/preprocess/preprocess.xml
new file mode 100644
index 0000000..3d0b658
--- /dev/null
+++ b/tools/preprocess/preprocess.xml
@@ -0,0 +1,215 @@
+
+ for regridding and normalizing icenet data
+
+ python
+ xarray
+ numpy
+ pandas
+ scipy
+ iris
+ netcdf4
+ imageio
+ matplotlib
+ tqdm
+ cdsapi
+ tensorflow
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 10.5281/zenodo.5176573
+
+
+
diff --git a/tools/preprocess/utils.py b/tools/preprocess/utils.py
new file mode 100644
index 0000000..30b6c8f
--- /dev/null
+++ b/tools/preprocess/utils.py
@@ -0,0 +1,1954 @@
+"""
+Code taken from https://github.com/tom-andersson/icenet-paper and slightly adjusted
+to fit the galaxy interface.
+"""
+import os
+import sys
+import numpy as np
+import tensorflow as tf
+from models import linear_trend_forecast
+import config
+import itertools
+import requests
+import json
+import time
+import re
+import xarray as xr
+import pandas as pd
+from dateutil.relativedelta import relativedelta
+import iris
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+import imageio
+from tqdm import tqdm
+sys.path.insert(0, os.path.join(os.getcwd(), 'icenet')) # if using jupyter kernel
+
+###############################################################################
+# DATA PROCESSING & LOADING
+###############################################################################
+
+
+class IceNetDataPreProcessor(object):
+ """
+ Normalises IceNet input data and saves the normalised monthly averages
+ as .npy files. If preprocessing climate model data for transfer learning,
+ the observational normalisation is repeated for the climate model data in order
+ to preserve the mapping from raw values to normalised values.
+
+ Data is stored in the following form with observations separated from climate
+ model transfer learning data:
+ - data/network_datasets//obs/tas/2006_04.npy
+ - data/network_datasets//transfer/MRI-ESM2-0/r1i1p1f1/tas/2056_09.npy
+
+ Normalisation parameters computed over the observational training data are
+ stored in a JSON file at data/network_datasets//norm_params.json
+ so that they are only computed once. Similarly, monthly climatology fields
+ used for computing anomaly fields are saved next to the raw NetCDF files so that
+ climatologies are only computed once for each variable.
+ """
+
+ def __init__(self, dataloader_config_fpath, preproc_vars,
+ n_linear_years, minmax, verbose_level,
+ preproc_obs_data=True,
+ preproc_transfer_data=False, cmip_transfer_data={}):
+ """
+ Parameters:
+
+ dataloader_config_fpath (str): Path to the data loader configuration
+ settings JSON file, defining IceNet's input-output data configuration.
+ This also defines the dataset name, used as the folder name to
+ store the preprocessed data within data/network_datasets/.
+
+ preproc_vars (dict): Which variables to preprocess. Example:
+
+ preproc_vars = {
+ 'siconca': {'anom': True, 'abs': True},
+ 'tas': {'anom': True, 'abs': False},
+ 'tos': {'anom': True, 'abs': False},
+ 'rsds': {'anom': True, 'abs': False},
+ 'rsus': {'anom': True, 'abs': False},
+ 'psl': {'anom': False, 'abs': True},
+ 'zg500': {'anom': False, 'abs': True},
+ 'zg250': {'anom': False, 'abs': True},
+ 'ua10': {'anom': False, 'abs': True},
+ 'uas': {'anom': False, 'abs': True},
+ 'vas': {'anom': False, 'abs': True},
+ 'sfcWind': {'anom': False, 'abs': True},
+ 'land': {'metadata': True, 'include': True},
+ 'circmonth': {'metadata': True, 'include': True}
+ }
+
+ n_linear_years (int): Number of past years to used in the linear trend
+ projections.
+
+ minmax (bool): Whether to use min-max normalisation to (-1, 1) or normalise
+ the mean and standard deviation to 0 and 1.
+
+ verbose_level (int): Controls how much to print. 0: Print nothing.
+ 1: Print key set-up stages. 2: Print debugging info.
+
+ preproc_obs_data (bool): Whether to preprocess observational data
+ (default True).
+
+ preproc_transfer_data (bool): Whether to also preprocess CMIP6 data for each variable
+ (default False).
+
+ cmip_transfer_data (dict): Which CMIP6 models & model runs to
+ preprocess for transfer learning. Example:
+
+ cmip_transfer_data = {
+ 'MRI-ESM2-0': ('r1i1p1f1', 'r2i1p1f1', 'r3i1p1f1',
+ 'r4i1p1f1', 'r5i1p1f1')
+ }
+
+ """
+
+ with open(dataloader_config_fpath, 'r') as readfile:
+ self.config = json.load(readfile)
+
+ self.preproc_vars = preproc_vars
+ self.n_linear_years = n_linear_years
+ self.minmax = minmax
+ self.verbose_level = verbose_level
+ self.preproc_obs_data = preproc_obs_data
+ self.preproc_transfer_data = preproc_transfer_data
+ self.cmip_transfer_data = cmip_transfer_data
+
+ self.load_or_instantiate_norm_params_dict()
+ self.set_obs_train_dates()
+ self.set_up_folder_hierarchy()
+
+ if self.verbose_level >= 1:
+ print("Loading and normalising the raw input maps.\n")
+ tic = time.time()
+
+ self.preproc_and_save_icenet_data()
+
+ if self.verbose_level >= 1:
+ print("\nPreprocessing completed in {:.0f}s.\n".format(time.time() - tic))
+
+ def load_or_instantiate_norm_params_dict(self):
+
+ # Path to JSON file storing normalisation parameters for each variable
+ self.norm_params_fpath = os.path.join(
+ config.network_dataset_folder, self.config['dataset_name'], 'norm_params.json')
+
+ if not os.path.exists(self.norm_params_fpath):
+ self.norm_params = {}
+
+ else:
+ with open(self.norm_params_fpath, 'r') as readfile:
+ self.norm_params = json.load(readfile)
+
+ def set_obs_train_dates(self):
+
+ forecast_start_date_ends = self.config['sample_IDs']['obs_train_dates']
+
+ if forecast_start_date_ends is not None:
+
+ # Convert to Pandas Timestamps
+ forecast_start_date_ends = [
+ pd.Timestamp(date).to_pydatetime() for date in forecast_start_date_ends
+ ]
+
+ self.obs_train_dates = list(pd.date_range(
+ forecast_start_date_ends[0],
+ forecast_start_date_ends[1],
+ freq='MS',
+ closed='right',
+ ))
+
+ def set_up_folder_hierarchy(self):
+
+ """
+ Initialise the folders to store the datasets.
+ """
+
+ if self.verbose_level >= 1:
+ print('Setting up the folder hierarchy for {}... '.format(self.config['dataset_name']),
+ end='', flush=True)
+
+ # Parent folder for this dataset
+ self.dataset_path = os.path.join(config.data_folder, 'network_datasets', self.config['dataset_name'])
+
+ # Dictionary data structure to store folder paths
+ self.paths = {}
+
+ # Set up the folder hierarchy
+ self.paths['obs'] = {}
+
+ for varname, vardict in self.preproc_vars.items():
+
+ if 'metadata' not in vardict.keys():
+ self.paths['obs'][varname] = {}
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format] is True:
+ path = os.path.join(self.dataset_path, 'obs',
+ varname, data_format)
+
+ self.paths['obs'][varname][data_format] = path
+
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ self.paths['transfer'] = {}
+
+ for model_name, member_ids in self.cmip_transfer_data.items():
+ self.paths['transfer'][model_name] = {}
+ for member_id in member_ids:
+ self.paths['transfer'][model_name][member_id] = {}
+
+ for varname, vardict in self.preproc_vars.items():
+
+ if 'metadata' not in vardict.keys():
+ self.paths['transfer'][model_name][member_id][varname] = {}
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format] is True:
+ path = os.path.join(self.dataset_path, 'transfer',
+ model_name, member_id,
+ varname, data_format)
+
+ self.paths['transfer'][model_name][member_id][varname][data_format] = path
+
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ for varname, vardict in self.preproc_vars.items():
+ if 'metadata' in vardict.keys():
+
+ if vardict['include'] is True:
+ path = os.path.join(self.dataset_path, 'meta')
+
+ self.paths['meta'] = path
+
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ if self.verbose_level >= 1:
+ print('Done.')
+
+ @staticmethod
+ def standardise_cmip6_time_coord(da):
+
+ """
+ Convert the cmip6 xarray time dimension to use day=1, hour=0 convention
+ used in the rest of the project.
+ """
+
+ standardised_dates = []
+ for datetime64 in da.time.values:
+ date = pd.Timestamp(datetime64, unit='s')
+ date = date.replace(day=1, hour=0)
+ standardised_dates.append(date)
+ da = da.assign_coords({'time': standardised_dates})
+
+ return da
+
+ @staticmethod
+ def mean_and_std(list, verbose_level=2):
+
+ # Must use float64s to be JSON serialisable
+ mean = np.nanmean(list, dtype=np.float64)
+ std = np.nanstd(list, dtype=np.float64)
+
+ return mean, std
+
+ def normalise_array_using_all_training_months(self, da, minmax=False,
+ mean=None, std=None,
+ min=None, max=None):
+
+ """
+ Using the *training* months only, compute the mean and
+ standard deviation of the input raw satellite DataArray (`da`)
+ and return a normalised version. If minmax=True,
+ instead normalise to lie between min and max of the elements of `array`.
+
+ If min, max, mean, or std are given values other than None,
+ those values are used rather than being computed from the training months.
+
+ Returns:
+ new_da (xarray.DataArray): Normalised array.
+
+ mean, std (float): Pre-computed mean and standard deviation for the
+ normalisation.
+
+ min, max (float): Pre-computed min and max for the normalisation.
+ """
+
+ if (min is not None and max is not None) or (mean is not None and std is not None):
+ # Function has been passed precomputed normalisation parameters
+ pass
+ else:
+ # Function will be computing new normalisation parameters
+ training_samples = da.sel(time=self.obs_train_dates).data
+ training_samples = training_samples.ravel()
+
+ if not minmax:
+ if mean is None and std is None:
+ # Compute mean and std
+ mean, std = IceNetDataPreProcessor.mean_and_std(
+ training_samples, self.verbose_level)
+ elif mean is not None and std is None:
+ # Compute std only
+ _, std = IceNetDataPreProcessor.mean_and_std(
+ training_samples, self.verbose_level)
+ elif mean is None and std is not None:
+ # Compute mean only
+ mean, _ = IceNetDataPreProcessor.mean_and_std(
+ training_samples, self.verbose_level)
+
+ new_da = (da - mean) / std
+
+ elif minmax:
+ if min is None:
+ # Compute min
+ min = np.nanmin(training_samples).astype(np.float64)
+ if max is None:
+ # Compute max
+ max = np.nanmax(training_samples).astype(np.float64)
+
+ new_da = (da - min) / (max - min)
+
+ if minmax:
+ return new_da, min, max
+ elif not minmax:
+ return new_da, mean, std
+
+ def save_xarray_in_monthly_averages(self, da, dataset_type, varname, data_format,
+ model_name=None, member_id=None):
+
+ """
+ Saves an xarray DataArray as monthly averaged .npy files using the
+ self.paths data structure.
+
+ Parameters:
+ da (xarray.DataArray): The DataArray to save.
+
+ dataset_type (str): Either 'obs' or 'transfer' (for CMIP6 data) - the type
+ of dataset being saved.
+
+ varname (str): Variable name being saved.
+
+ data_format (str): Either 'abs' or 'anom' - the format of the data
+ being saved.
+ """
+
+ if self.verbose_level >= 2:
+ print('Saving {} {} monthly averages... '.format(data_format, varname), end='', flush=True)
+
+ # Allow for datasets without a time dimension (a single time slice)
+ dates = da.time.values
+ if hasattr(dates, '__iter__'):
+ pass # Dataset has 'time' dimension; dates already iterable
+ else:
+ dates = [dates] # Convert single time value to iterable
+ da = da.expand_dims({'time': dates})
+
+ for date in dates:
+ slice = da.sel(time=date).data
+ date = pd.Timestamp(date)
+ year_str = '{:04d}'.format(date.year)
+ month_str = '{:02d}'.format(date.month)
+ fname = '{}_{}.npy'.format(year_str, month_str)
+
+ if dataset_type == 'obs':
+ np.save(os.path.join(self.paths[dataset_type][varname][data_format], fname),
+ slice)
+
+ if dataset_type == 'transfer':
+ np.save(os.path.join(self.paths[dataset_type][model_name][member_id][varname][data_format], fname),
+ slice)
+
+ if self.verbose_level >= 2:
+ print('Done.')
+
+ def build_linear_trend_da(self, input_da, dataset):
+
+ """
+ Construct a DataArray `linea_trend_da` containing the linear trend SIC
+ forecasts based on the input DataArray `input_da`.
+
+ `linear_trend_da` will be saved in monthly averages using
+ the `save_xarray_in_monthly_averages` method.
+
+ Parameters:
+ `input_da` (xarray.DataArray): Input DataArray to produce linear SIC
+ forecasts for.
+
+ `dataset` (str): 'obs' or 'cmip6' (dictates whether to skip missing
+ observational months in the linear trend extrapolation)
+
+ Returns:
+ `linear_trend_da` (xarray.DataArray): DataArray whose time slices
+ correspond to the linear trend SIC projection for that month.
+ """
+
+ linear_trend_da = input_da.copy(data=np.zeros(input_da.shape, dtype=np.float32))
+
+ # No prediction possible for the first year of data
+ forecast_dates = input_da.time.values[12:]
+
+ # Convert from datetime64 to pd.Timestamp
+ forecast_dates = [pd.Timestamp(date) for date in forecast_dates]
+
+ # Add on the future year
+ last_year = forecast_dates[-12:]
+ forecast_dates.extend([date + pd.DateOffset(years=1) for date in last_year])
+
+ linear_trend_da = linear_trend_da.assign_coords({'time': forecast_dates})
+
+ for forecast_date in forecast_dates:
+ linear_trend_da.loc[dict(time=forecast_date)] = \
+ linear_trend_forecast(forecast_date, self.n_linear_years, da=input_da, dataset=dataset)[0]
+
+ return linear_trend_da
+
+ def check_if_params_precomputed(self, varname, data_format):
+ ''' Searches self.norm_params for normalisation parameters
+ for a given variable name and data format. '''
+
+ if varname == 'siconca':
+ # No normalisation for SIC
+ return True
+
+ # Grab existing parameters if stored in norm_params JSON file
+ precomputed_params_exists = False
+ if varname in self.norm_params.keys():
+ if data_format in self.norm_params[varname].keys():
+ params = self.norm_params[varname][data_format]
+ if self.minmax:
+ if 'min' in params.keys() and 'max' in params.keys():
+ precomputed_params_exists = True
+ elif not self.minmax:
+ if 'mean' in params.keys() and 'std' in params.keys():
+ precomputed_params_exists = True
+
+ return precomputed_params_exists
+
+ def save_variable(self, varname, data_format, dates=None):
+
+ """
+ Save a normalised 3-dimensional satellite/reanalysis dataset as monthly
+ averages (either the absolute values or the monthly anomalies
+ computed with xarray).
+
+ This method assumes there is only one variable stored in the NetCDF files.
+
+ Parameters:
+ varname (str): Name of the variable to load & save
+
+ data_format (str): 'abs' for absolute values, or 'anom' to compute the
+ anomalies, or 'linear_trend' for SIC linear trend projections.
+
+ dates (list of dates): Months to use to compute the monthly
+ climatologies (defaults to the months used for training).
+ """
+
+ if data_format == 'anom':
+ if dates is None:
+ dates = self.obs_train_dates
+
+ ########################################################################
+ # Observational variable
+ ########################################################################
+
+ if self.preproc_obs_data:
+ if self.verbose_level >= 2:
+ print("Preprocessing {} data for {}... ".format(data_format, varname), end='', flush=True)
+ tic = time.time()
+
+ fpath = os.path.join(config.obs_data_folder, '{}_EASE.nc'.format(varname))
+ with xr.open_dataset(fpath) as ds:
+ da = next(iter(ds.data_vars.values()))
+
+ if data_format == 'anom':
+
+ # Check if climatology already computed
+ train_start = self.obs_train_dates[0].strftime('%Y')
+ train_end = self.obs_train_dates[-1].strftime('%Y')
+
+ climatology_fpath = os.path.join(
+ config.obs_data_folder,
+ '{}_climatology_{}_{}.nc'.format(varname, train_start, train_end))
+
+ if os.path.exists(climatology_fpath):
+ with xr.open_dataset(climatology_fpath) as ds:
+ climatology = next(iter(ds.data_vars.values()))
+ else:
+ climatology = da.sel(time=dates). \
+ groupby("time.month", restore_coord_dims=True).mean("time")
+ climatology.to_netcdf(climatology_fpath)
+
+ da = da.groupby("time.month", restore_coord_dims=True) - climatology
+
+ elif data_format == 'linear_trend':
+ da = self.build_linear_trend_da(da, dataset='obs')
+
+ # Realise the array
+ da.data = np.asarray(da.data, dtype=np.float32)
+
+ # Normalise the array
+ if varname == 'siconca':
+ # Don't normalise SIC - already betw 0 and 1
+ mean, std = None, None
+ min, max = None, None
+
+ elif varname != 'siconca':
+ precomputed_params_exists = self.check_if_params_precomputed(varname, data_format)
+
+ if precomputed_params_exists:
+ if self.minmax:
+ min = self.norm_params[varname][data_format]['min']
+ max = self.norm_params[varname][data_format]['max']
+ if self.verbose_level >= 2:
+ print("Using precomputed min/max: {}/{}... ".format(min, max),
+ end='', flush=True)
+ elif not self.minmax:
+ mean = self.norm_params[varname][data_format]['mean']
+ std = self.norm_params[varname][data_format]['std']
+ if self.verbose_level >= 2:
+ print("Using precomputed mean/std: {}/{}... ".format(mean, std),
+ end='', flush=True)
+ elif not precomputed_params_exists:
+ mean, std = None, None
+ min, max = None, None
+ self.norm_params[varname] = {}
+ self.norm_params[varname][data_format] = {}
+
+ if self.minmax:
+ da, min, max = self.normalise_array_using_all_training_months(
+ da, self.minmax, min=min, max=max)
+ if not precomputed_params_exists:
+ if self.verbose_level >= 2:
+ print("Newly computed min/max: {}/{}... ".format(min, max),
+ end='', flush=True)
+ self.norm_params[varname][data_format]['min'] = min
+ self.norm_params[varname][data_format]['max'] = max
+ elif not self.minmax:
+ da, mean, std = self.normalise_array_using_all_training_months(
+ da, self.minmax, mean=mean, std=std)
+ if not precomputed_params_exists:
+ if self.verbose_level >= 2:
+ print("Newly computed mean/std: {}/{}... ".format(mean, std),
+ end='', flush=True)
+ self.norm_params[varname][data_format]['mean'] = mean
+ self.norm_params[varname][data_format]['std'] = std
+
+ da.data[np.isnan(da.data)] = 0. # Convert any NaNs to zeros
+
+ self.save_xarray_in_monthly_averages(da, 'obs', varname, data_format)
+
+ if self.verbose_level >= 2:
+ print("Done in {:.0f}s.\n".format(time.time() - tic))
+
+ ########################################################################
+ # Transfer variable
+ ########################################################################
+
+ if self.preproc_transfer_data:
+ if self.verbose_level >= 2:
+ print("Preprocessing CMIP6 {} data for {}... ".format(data_format, varname), end='', flush=True)
+ tic = time.time()
+
+ if not self.check_if_params_precomputed(varname, data_format):
+ raise ValueError('Normalisation parameters must be computed '
+ 'from observational data before preprocessing '
+ 'CMIP6 data.')
+
+ elif varname != 'siconca' and self.minmax:
+ min = self.norm_params[varname][data_format]['min']
+ max = self.norm_params[varname][data_format]['max']
+ if self.verbose_level >= 2:
+ print("Using precomputed min/max: {}/{}... ".format(min, max),
+ end='', flush=True)
+
+ elif varname != 'siconca' and not self.minmax:
+ mean = self.norm_params[varname][data_format]['mean']
+ std = self.norm_params[varname][data_format]['std']
+ if self.verbose_level >= 2:
+ print("Using precomputed mean/std: {}/{}... ".format(mean, std),
+ end='', flush=True)
+
+ for model_name, member_ids in self.cmip_transfer_data.items():
+ print('{}: '.format(model_name), end='', flush=True)
+
+ for member_id in member_ids:
+ print('{}, '.format(member_id), end='', flush=True)
+
+ fname = '{}_EASE_cmpr.nc'.format(varname)
+ fpath = os.path.join(config.cmip6_data_folder, model_name, member_id, fname)
+
+ with xr.open_dataset(fpath) as ds:
+ da = next(iter(ds.data_vars.values()))
+
+ # Convert to my month convention of day=1 and time=00:00
+ da = IceNetDataPreProcessor.standardise_cmip6_time_coord(da)
+
+ # Realise the array
+ da.data = np.asarray(da.data, dtype=np.float32)
+
+ if data_format == 'anom':
+
+ climatology = da.sel(time=dates). \
+ groupby("time.month", restore_coord_dims=True).mean("time")
+ da = da.groupby("time.month", restore_coord_dims=True) - climatology
+
+ elif data_format == 'linear_trend':
+ da = self.build_linear_trend_da(da, dataset='cmip6')
+
+ # Normalise the array
+ if varname != 'siconca':
+ if self.minmax:
+ da, _, _ = self.normalise_array_using_all_training_months(
+ da, self.minmax, min=min, max=max)
+ elif not self.minmax:
+ da, _, _ = self.normalise_array_using_all_training_months(
+ da, self.minmax, mean=mean, std=std)
+
+ self.save_xarray_in_monthly_averages(da, 'transfer', varname, data_format,
+ model_name, member_id)
+
+ if self.verbose_level >= 2:
+ print("Done in {:.0f}s.\n".format(time.time() - tic))
+
+ def preproc_and_save_icenet_data(self):
+
+ '''
+ Loop through each variable, preprocessing and saving.
+ '''
+
+ for varname, vardict in self.preproc_vars.items():
+
+ if 'metadata' not in vardict.keys():
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format] is True:
+
+ self.save_variable(varname, data_format)
+
+ elif 'metadata' in vardict.keys():
+
+ if vardict['include']:
+ if varname == 'land':
+ if self.verbose_level >= 2:
+ print("Setting up the land map: ", end='', flush=True)
+
+ land_mask = np.load(os.path.join(config.mask_data_folder, config.land_mask_filename))
+ land_map = np.ones(self.config['raw_data_shape'], np.float32)
+ land_map[~land_mask] = -1.
+
+ np.save(os.path.join(self.paths['meta'], 'land.npy'), land_map)
+
+ print('\n')
+
+ elif varname == 'circmonth':
+ if self.verbose_level >= 2:
+ print("Computing circular month values... ", end='', flush=True)
+ tic = time.time()
+
+ for month in np.arange(1, 13):
+ cos_month = np.cos(2 * np.pi * month / 12, dtype='float32')
+ sin_month = np.sin(2 * np.pi * month / 12, dtype='float32')
+
+ np.save(os.path.join(self.paths['meta'], 'cos_month_{:02d}.npy'.format(month)), cos_month)
+ np.save(os.path.join(self.paths['meta'], 'sin_month_{:02d}.npy'.format(month)), sin_month)
+
+ if self.verbose_level >= 2:
+ print("Done in {:.0f}s.\n".format(time.time() - tic))
+
+ with open(self.norm_params_fpath, 'w') as outfile:
+ json.dump(self.norm_params, outfile)
+
+
+class IceNetDataLoader(tf.keras.utils.Sequence):
+ """
+ Custom data loader class for generating batches of input-output tensors for
+ training IceNet. Inherits from keras.utils.Sequence, which ensures each the
+ network trains once on each sample per epoch. Must implement a __len__
+ method that returns the number of batches and a __getitem__ method that
+ returns a batch of data. The on_epoch_end method is called after each
+ epoch.
+ See: https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence
+
+ """
+
+ def __init__(self, dataloader_config_fpath, seed=None):
+
+ '''
+ Params:
+ dataloader_config_fpath (str): Path to the data loader configuration
+ settings JSON file, defining IceNet's input-output data configuration.
+
+ seed (int): Random seed used for shuffling the training samples before
+ each epoch.
+ '''
+
+ with open(dataloader_config_fpath, 'r') as readfile:
+ self.config = json.load(readfile)
+
+ if seed is None:
+ self.set_seed(self.config['default_seed'])
+ else:
+ self.set_seed(seed)
+
+ self.do_transfer_learning = False
+
+ self.set_obs_forecast_IDs(dataset='train')
+ self.set_transfer_forecast_IDs()
+ self.all_forecast_IDs = self.obs_forecast_IDs
+ self.remove_missing_dates()
+ self.set_variable_path_formats()
+ self.set_number_of_input_channels_for_each_input_variable()
+ self.load_polarholes()
+ self.determine_tot_num_channels()
+ self.on_epoch_end()
+
+ if self.config['verbose_level'] >= 1:
+ print("Setup complete.\n")
+
+ def set_obs_forecast_IDs(self, dataset='train'):
+ """
+ Build up a list of forecast initialisation dates for the train, val, or
+ test sets based on the configuration JSON file start & end points for
+ each dataset.
+ """
+
+ forecast_start_date_ends = self.config['sample_IDs']['obs_{}_dates'.format(dataset)]
+
+ if forecast_start_date_ends is not None:
+
+ # Convert to Pandas Timestamps
+ forecast_start_date_ends = [
+ pd.Timestamp(date).to_pydatetime() for date in forecast_start_date_ends
+ ]
+
+ self.obs_forecast_IDs = list(pd.date_range(
+ forecast_start_date_ends[0],
+ forecast_start_date_ends[1],
+ freq='MS',
+ closed='right',
+ ))
+
+ def set_transfer_forecast_IDs(self):
+
+ '''
+ Use self.cmip6_transfer_train_dict to set up a list array of
+ 3-tuples of the form:
+ (cmip6_model_name, member_id, forecast_start_date)
+
+ This list is used as IDs into the transfer data hierarchy
+ to train on all cmip6 models and and their runs simultaneously.
+ '''
+
+ self.transfer_forecast_IDs = []
+ for cmip6_model_name, member_id_dict in self.config['cmip6_run_dict'].items():
+ for member_id, (start_date, end_date) in member_id_dict.items():
+
+ member_id_dates = list(pd.date_range(
+ start_date,
+ end_date,
+ freq='MS',
+ closed='right',
+ ))
+
+ self.transfer_forecast_IDs.extend(
+ itertools.product([cmip6_model_name], [member_id], member_id_dates)
+ )
+
+ def set_variable_path_formats(self):
+
+ """
+ Initialise the paths to the .npy files of each variable based on
+ `self.config['input_data']`.
+ """
+
+ if self.config['verbose_level'] >= 1:
+ print('Setting up the variable paths for {}... '.format(self.config['dataset_name']),
+ end='', flush=True)
+
+ # Parent folder for this dataset
+ self.dataset_path = os.path.join(config.network_dataset_folder, self.config['dataset_name'])
+
+ # Dictionary data structure to store image variable paths
+ self.variable_paths = {}
+
+ for varname, vardict in self.config['input_data'].items():
+
+ if 'metadata' not in vardict.keys():
+ self.variable_paths[varname] = {}
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format]['include'] is True:
+
+ if not self.do_transfer_learning:
+ path = os.path.join(
+ self.dataset_path, 'obs',
+ varname, data_format, '{:04d}_{:02d}.npy'
+ )
+ elif self.do_transfer_learning:
+ path = os.path.join(
+ self.dataset_path, 'transfer', '{}', '{}',
+ varname, data_format, '{:04d}_{:02d}.npy'
+ )
+
+ self.variable_paths[varname][data_format] = path
+
+ elif 'metadata' in vardict.keys():
+
+ if vardict['include'] is True:
+
+ if varname == 'land':
+ path = os.path.join(self.dataset_path, 'meta', 'land.npy')
+ self.variable_paths['land'] = path
+
+ elif varname == 'circmonth':
+ path = os.path.join(self.dataset_path, 'meta',
+ '{}_month_{:02d}.npy')
+ self.variable_paths['circmonth'] = path
+
+ if self.config['verbose_level'] >= 1:
+ print('Done.')
+
+ def set_seed(self, seed):
+ """
+ Set the seed used by the random generator (used to randomly shuffle
+ the ordering of training samples after each epoch).
+ """
+ if self.config['verbose_level'] >= 1:
+ print("Setting the data generator's random seed to {}".format(seed))
+ self.rng = np.random.default_rng(seed)
+
+ def determine_variable_names(self):
+ """
+ Set up a list of strings for the names of each input variable (in the
+ correct order) by looping over the `input_data` dictionary.
+ """
+ variable_names = []
+
+ for varname, vardict in self.config['input_data'].items():
+ # Input variables that span time
+ if 'metadata' not in vardict.keys():
+ for data_format in vardict.keys():
+ if vardict[data_format]['include']:
+ if data_format != 'linear_trend':
+ for lag in np.arange(1, vardict[data_format]['max_lag'] + 1):
+ variable_names.append(varname + '_{}_{}'.format(data_format, lag))
+ elif data_format == 'linear_trend':
+ for leadtime in np.arange(1, self.config['n_forecast_months'] + 1):
+ variable_names.append(varname + '_{}_{}'.format(data_format, leadtime))
+
+ # Metadata input variables that don't span time
+ elif 'metadata' in vardict.keys() and vardict['include']:
+ if varname == 'land':
+ variable_names.append(varname)
+
+ elif varname == 'circmonth':
+ variable_names.append('cos(month)')
+ variable_names.append('sin(month)')
+
+ return variable_names
+
+ def set_number_of_input_channels_for_each_input_variable(self):
+ """
+ Build up the dict `self.num_input_channels_dict` to store the number of input
+ channels spanned by each input variable.
+ """
+
+ if self.config['verbose_level'] >= 1:
+ print("Setting the number of input months for each input variable.")
+
+ self.num_input_channels_dict = {}
+
+ for varname, vardict in self.config['input_data'].items():
+ if 'metadata' not in vardict.keys():
+ # Variables that span time
+ for data_format in vardict.keys():
+ if vardict[data_format]['include']:
+ varname_format = varname + '_{}'.format(data_format)
+ if data_format != 'linear_trend':
+ self.num_input_channels_dict[varname_format] = vardict[data_format]['max_lag']
+ elif data_format == 'linear_trend':
+ self.num_input_channels_dict[varname_format] = self.config['n_forecast_months']
+
+ # Metadata input variables that don't span time
+ elif 'metadata' in vardict.keys() and vardict['include']:
+ if varname == 'land':
+ self.num_input_channels_dict[varname] = 1
+
+ if varname == 'circmonth':
+ self.num_input_channels_dict[varname] = 2
+
+ def determine_tot_num_channels(self):
+ """
+ Determine the number of channels for the input 3D volumes.
+ """
+
+ self.tot_num_channels = 0
+ for varname, num_channels in self.num_input_channels_dict.items():
+ self.tot_num_channels += num_channels
+
+ def all_sic_input_dates_from_forecast_start_date(self, forecast_start_date):
+ """
+ Return a list of all the SIC dates used as input for a particular forecast
+ date, based on the "max_lag" options of self.config['input_data'].
+ """
+
+ # Find all SIC lags
+ max_lags = []
+ if self.config['input_data']['siconca']['abs']['include']:
+ max_lags.append(self.config['input_data']['siconca']['abs']['max_lag'])
+ if self.config['input_data']['siconca']['anom']['include']:
+ max_lags.append(self.config['input_data']['siconca']['anom']['max_lag'])
+ max_lag = np.max(max_lags)
+
+ input_dates = [
+ forecast_start_date - pd.DateOffset(months=int(lag)) for lag in np.arange(1, max_lag + 1)
+ ]
+
+ return input_dates
+
+ def check_for_missing_date_dependence(self, forecast_start_date):
+ """
+ Check a forecast ID and return a bool for whether any of the input SIC maps
+ are missing. Used to remove forecast IDs that depend on missing SIC data.
+
+ Note: If one of the _forecast_ dates are missing but not _input_ dates,
+ the sample weight matrix for that date will be all zeroes so that the
+ samples for that date do not appear in the loss function.
+ """
+ contains_missing_date = False
+
+ # Check SIC input dates
+ input_dates = self.all_sic_input_dates_from_forecast_start_date(forecast_start_date)
+
+ for input_date in input_dates:
+ if any([input_date == missing_date for missing_date in config.missing_dates]):
+ contains_missing_date = True
+ break
+
+ return contains_missing_date
+
+ def remove_missing_dates(self):
+
+ '''
+ Remove dates from self.obs_forecast_IDs that depend on a missing
+ observation of SIC.
+ '''
+
+ if self.config['verbose_level'] >= 2:
+ print('Checking forecast start dates for missing SIC dates... ', end='', flush=True)
+
+ new_obs_forecast_IDs = []
+ for forecast_start_date in self.obs_forecast_IDs:
+ if self.check_for_missing_date_dependence(forecast_start_date):
+ if self.config['verbose_level'] >= 3:
+ print('Removing {}, '.format(
+ forecast_start_date.strftime('%Y_%m_%d')), end='', flush=True)
+
+ else:
+ new_obs_forecast_IDs.append(forecast_start_date)
+
+ self.obs_forecast_IDs = new_obs_forecast_IDs
+
+ def load_polarholes(self):
+ """
+ Loads each of the polar holes.
+ """
+
+ if self.config['verbose_level'] >= 1:
+ tic = time.time()
+ print("Loading and augmenting the polar holes... ", end='', flush=True)
+
+ polarhole_path = os.path.join(config.mask_data_folder, config.polarhole1_fname)
+ self.polarhole1_mask = np.load(polarhole_path)
+
+ polarhole_path = os.path.join(config.mask_data_folder, config.polarhole2_fname)
+ self.polarhole2_mask = np.load(polarhole_path)
+
+ if config.use_polarhole3:
+ polarhole_path = os.path.join(config.mask_data_folder, config.polarhole3_fname)
+ self.polarhole3_mask = np.load(polarhole_path)
+
+ self.nopolarhole_mask = np.full((432, 432), False)
+
+ if self.config['verbose_level'] >= 1:
+ print("Done in {:.0f}s.\n".format(time.time() - tic))
+
+ def determine_polar_hole_mask(self, forecast_start_date):
+ """
+ Determine which polar hole mask to use (if any) by finding the oldest SIC
+ input month based on the current output month. The polar hole active for
+ the oldest input month is used (because the polar hole size decreases
+ monotonically over time, and we wish to use the largest polar hole for
+ the input data).
+
+ Parameters:
+ forecast_start_date (pd.Timestamp): Timepoint for the forecast initialialisation.
+
+ Returns:
+ polarhole_mask: Mask array with NaNs on polar hole grid cells and 1s
+ elsewhere.
+ """
+
+ oldest_input_date = min(self.all_sic_input_dates_from_forecast_start_date(forecast_start_date))
+
+ if oldest_input_date <= config.polarhole1_final_date:
+ polarhole_mask = self.polarhole1_mask
+ if self.config['verbose_level'] >= 3:
+ print("Forecast start date: {}, polar hole: {}".format(
+ forecast_start_date.strftime("%Y_%m"), 1))
+
+ elif oldest_input_date <= config.polarhole2_final_date:
+ polarhole_mask = self.polarhole2_mask
+ if self.config['verbose_level'] >= 3:
+ print("Forecast start date: {}, polar hole: {}".format(
+ forecast_start_date.strftime("%Y_%m"), 2))
+
+ else:
+ polarhole_mask = self.nopolarhole_mask
+ if self.config['verbose_level'] >= 3:
+ print("Forecast start date: {}, polar hole: {}".format(
+ forecast_start_date.strftime("%Y_%m"), "none"))
+
+ return polarhole_mask
+
+ def determine_active_grid_cell_mask(self, forecast_date):
+ """
+ Determine which active grid cell mask to use (a boolean array with
+ True on active cells and False on inactive cells). The cells with 'True'
+ are where predictions are to be made with IceNet. The active grid cell
+ mask for a particular month is determined by the sum of the land cells,
+ the ocean cells (for that month), and the missing polar hole.
+
+ The mask is used for removing 'inactive' cells (such as land or polar
+ hole cells) from the loss function in self.data_generation.
+ """
+
+ output_month_str = '{:02d}'.format(forecast_date.month)
+ output_active_grid_cell_mask_fname = config.active_grid_cell_file_format. \
+ format(output_month_str)
+ output_active_grid_cell_mask_path = os.path.join(
+ config.mask_data_folder, output_active_grid_cell_mask_fname)
+ output_active_grid_cell_mask = np.load(output_active_grid_cell_mask_path)
+
+ # Only use the polar hole mask if predicting observational data
+ if not self.do_transfer_learning:
+ polarhole_mask = self.determine_polar_hole_mask(forecast_date)
+
+ # Add the polar hole mask to that land/ocean mask for the current month
+ output_active_grid_cell_mask[polarhole_mask] = False
+
+ return output_active_grid_cell_mask
+
+ def turn_on_transfer_learning(self):
+
+ '''
+ Converts the data loader to use CMIP6 pre-training data
+ for transfer learning.
+ '''
+
+ self.do_transfer_learning = True
+ self.all_forecast_IDs = self.transfer_forecast_IDs
+ self.on_epoch_end() # Shuffle transfer training indexes
+ self.set_variable_path_formats()
+
+ def turn_off_transfer_learning(self):
+
+ '''
+ Converts the data loader back to using ERA5/OSI-SAF observational
+ training data.
+ '''
+
+ self.do_transfer_learning = False
+ self.all_forecast_IDs = self.obs_forecast_IDs
+ self.on_epoch_end() # Shuffle transfer training indexes
+ self.set_variable_path_formats()
+
+ def convert_to_validation_data_loader(self):
+
+ """
+ Resets the `all_forecast_IDs` array to correspond to the validation
+ months defined by the data loader configuration file.
+ """
+
+ self.set_obs_forecast_IDs(dataset='val')
+ self.remove_missing_dates()
+ self.all_forecast_IDs = self.obs_forecast_IDs
+
+ def convert_to_test_data_loader(self):
+
+ """
+ As above but for the testing months.
+ """
+
+ self.set_obs_forecast_IDs(dataset='test')
+ self.remove_missing_dates()
+ self.all_forecast_IDs = self.obs_forecast_IDs
+
+ def data_generation(self, forecast_IDs):
+ """
+ Generate input-output data for IceNet for a given forecast ID.
+
+ Parameters:
+ forecast_IDs (list):
+ If self.do_transfer_learning is False, a list of pd.Timestamp objects
+ corresponding to the forecast initialisation dates (first month
+ being forecast) for the batch of X-y data to load.
+
+ If self.do_transfer_learning is True, a list of tuples
+ of the form (cmip6_model_name, member_id, forecast_start_date).
+
+ Returns:
+ X (ndarray): Batch of input 3D volumes.
+
+ y (ndarray): Batch of ground truth output SIC class maps
+
+ sample_weight (ndarray): Batch of pixelwise weights for weighting the
+ loss function (masking outside the active grid cell region and
+ up-weighting summer months).
+ """
+
+ # Allow non-list input for single forecasts
+ forecast_IDs = pd.Timestamp(forecast_IDs) if isinstance(forecast_IDs, str) else forecast_IDs
+ forecast_IDs = [forecast_IDs] if not isinstance(forecast_IDs, list) else forecast_IDs
+
+ current_batch_size = len(forecast_IDs)
+
+ if self.do_transfer_learning:
+ cmip6_model_names = [forecast_ID[0] for forecast_ID in forecast_IDs]
+ cmip6_member_ids = [forecast_ID[1] for forecast_ID in forecast_IDs]
+ forecast_start_dates = [forecast_ID[2] for forecast_ID in forecast_IDs]
+ else:
+ forecast_start_dates = forecast_IDs
+
+ ########################################################################
+ # OUTPUT LABELS
+ ########################################################################
+
+ # Build up the set of N_samps output SIC time-series
+ # (each n_forecast_months long in the time dimension)
+
+ # To become array of shape (N_samps, *raw_data_shape, n_forecast_months)
+ batch_sic_list = []
+
+ # True = forecasts months corresponding to no data
+ missing_month_dict = {}
+
+ for sample_idx, forecast_date in enumerate(forecast_start_dates):
+
+ # To become array of shape (*raw_data_shape, n_forecast_months)
+ sample_sic_list = []
+
+ # List of forecast indexes with missing data
+ missing_month_dict[sample_idx] = []
+
+ for forecast_leadtime_idx in range(self.config['n_forecast_months']):
+
+ forecast_date = forecast_start_dates[sample_idx] + pd.DateOffset(months=forecast_leadtime_idx)
+
+ if self.do_transfer_learning:
+ sample_sic_list.append(
+ np.load(self.variable_paths['siconca']['abs'].format(
+ cmip6_model_names[sample_idx], cmip6_member_ids[sample_idx],
+ forecast_date.year, forecast_date.month))
+ )
+
+ elif not self.do_transfer_learning:
+ if any([forecast_date == missing_date for missing_date in config.missing_dates]):
+ # Output file does not exist
+ sample_sic_list.append(np.zeros(self.config['raw_data_shape']))
+
+ else:
+ fpath = self.variable_paths['siconca']['abs'].format(
+ forecast_date.year, forecast_date.month)
+ if os.path.exists(fpath):
+ sample_sic_list.append(np.load(fpath))
+ else:
+ # Ground truth data doesn't exist: fill with NaNs
+ sample_sic_list.append(
+ np.full(self.config['raw_data_shape'], np.nan, dtype=np.float32))
+
+ batch_sic_list.append(np.stack(sample_sic_list, axis=2))
+
+ batch_sic = np.stack(batch_sic_list, axis=0)
+
+ no_ice_gridcells = batch_sic <= 0.15
+ ice_gridcells = batch_sic >= 0.80
+ marginal_ice_gridcells = ~((no_ice_gridcells) | (ice_gridcells))
+
+ # Categorical representation with channel dimension for class probs
+ y = np.zeros((
+ current_batch_size,
+ *self.config['raw_data_shape'],
+ self.config['n_forecast_months'],
+ 3
+ ), dtype=np.float32)
+
+ y[no_ice_gridcells, 0] = 1
+ y[marginal_ice_gridcells, 1] = 1
+ y[ice_gridcells, 2] = 1
+
+ # Move lead time to final axis
+ y = np.moveaxis(y, source=3, destination=4)
+
+ # Missing months
+ for sample_idx, forecast_leadtime_idx_list in missing_month_dict.items():
+ if len(forecast_leadtime_idx_list) > 0:
+ y[sample_idx, :, :, :, forecast_leadtime_idx_list] = 0
+
+ ########################################################################
+ # PIXELWISE LOSS FUNCTION WEIGHTING
+ ########################################################################
+
+ sample_weight = np.zeros((
+ current_batch_size,
+ *self.config['raw_data_shape'],
+ 1, # Broadcastable class dimension
+ self.config['n_forecast_months']
+ ), dtype=np.float32)
+ for sample_idx, forecast_date in enumerate(forecast_start_dates):
+
+ for forecast_leadtime_idx in range(self.config['n_forecast_months']):
+
+ forecast_date = forecast_start_dates[sample_idx] + pd.DateOffset(months=forecast_leadtime_idx)
+
+ if any([forecast_date == missing_date for missing_date in config.missing_dates]):
+ # Leave sample weighting as all-zeros
+ pass
+
+ else:
+ # Zero loss outside of 'active grid cells'
+ sample_weight_ij = self.determine_active_grid_cell_mask(forecast_date)
+ sample_weight_ij = sample_weight_ij.astype(np.float32)
+
+ # Scale the loss for each month s.t. March is
+ # scaled by 1 and Sept is scaled by 1.77
+ if self.config['loss_weight_months']:
+ sample_weight_ij *= 33928. / np.sum(sample_weight_ij)
+
+ sample_weight[sample_idx, :, :, 0, forecast_leadtime_idx] = \
+ sample_weight_ij
+
+ ########################################################################
+ # INPUT FEATURES
+ ########################################################################
+
+ # Batch tensor
+ X = np.zeros((
+ current_batch_size,
+ *self.config['raw_data_shape'],
+ self.tot_num_channels
+ ), dtype=np.float32)
+
+ # Build up the batch of inputs
+ for sample_idx, forecast_start_date in enumerate(forecast_start_dates):
+
+ present_date = forecast_start_date - relativedelta(months=1)
+
+ # Initialise variable indexes used to fill the input tensor `X`
+ variable_idx1 = 0
+ variable_idx2 = 0
+
+ for varname, vardict in self.config['input_data'].items():
+
+ if 'metadata' not in vardict.keys():
+
+ for data_format in vardict.keys():
+
+ if vardict[data_format]['include']:
+
+ varname_format = '{}_{}'.format(varname, data_format)
+
+ if data_format != 'linear_trend':
+ lbs = range(vardict[data_format]['max_lag'])
+ input_months = [present_date - relativedelta(months=lb) for lb in lbs]
+ elif data_format == 'linear_trend':
+ input_months = [present_date + relativedelta(months=forecast_leadtime)
+ for forecast_leadtime in np.arange(1, self.config['n_forecast_months'] + 1)]
+
+ variable_idx2 += self.num_input_channels_dict[varname_format]
+
+ if not self.do_transfer_learning:
+ X[sample_idx, :, :, variable_idx1:variable_idx2] = \
+ np.stack([np.load(self.variable_paths[varname][data_format].format(
+ date.year, date.month))
+ for date in input_months], axis=-1)
+ elif self.do_transfer_learning:
+ cmip6_model_name = cmip6_model_names[sample_idx]
+ cmip6_member_id = cmip6_member_ids[sample_idx]
+
+ X[sample_idx, :, :, variable_idx1:variable_idx2] = \
+ np.stack([np.load(self.variable_paths[varname][data_format].format(
+ cmip6_model_name, cmip6_member_id, date.year, date.month))
+ for date in input_months], axis=-1)
+
+ variable_idx1 += self.num_input_channels_dict[varname_format]
+
+ elif 'metadata' in vardict.keys() and vardict['include']:
+
+ variable_idx2 += self.num_input_channels_dict[varname]
+
+ if varname == 'land':
+ X[sample_idx, :, :, variable_idx1] = np.load(self.variable_paths['land'])
+
+ elif varname == 'circmonth':
+ X[sample_idx, :, :, variable_idx1] = \
+ np.load(self.variable_paths['circmonth'].format('cos', forecast_start_date.month))
+ X[sample_idx, :, :, variable_idx1 + 1] = \
+ np.load(self.variable_paths['circmonth'].format('sin', forecast_start_date.month))
+
+ variable_idx1 += self.num_input_channels_dict[varname]
+
+ return X, y, sample_weight
+
+ def __getitem__(self, batch_idx):
+ '''
+ Generate one batch of data of size `batch_size` at batch index `batch_idx`
+ into the set of batches in the epoch.
+ '''
+
+ batch_start = batch_idx * self.config['batch_size']
+ batch_end = np.min([(batch_idx + 1) * self.config['batch_size'], len(self.all_forecast_IDs)])
+
+ sample_idxs = np.arange(batch_start, batch_end)
+ batch_IDs = [self.all_forecast_IDs[sample_idx] for sample_idx in sample_idxs]
+
+ return self.data_generation(batch_IDs)
+
+ def __len__(self):
+ ''' Returns the number of batches per training epoch. '''
+ return int(np.ceil(len(self.all_forecast_IDs) / self.config['batch_size']))
+
+ def on_epoch_end(self):
+ """ Randomly shuffles training samples after each epoch. """
+
+ if self.config['verbose_level'] >= 2:
+ print("on_epoch_end called")
+
+ # Randomly shuffle the forecast IDs in-place
+ self.rng.shuffle(self.all_forecast_IDs)
+
+
+# MISC FUNCTIONS
+################################################################################
+
+
+def create_results_dataset_index(model_compute_list, leadtimes,
+ all_target_dates, icenet_ID,
+ icenet_seeds):
+
+ '''
+ Returns a pandas.MultiIndex object of results dataset indexes for a
+ given list of models to compute metrics for. For IceNet, the 'Ensemble
+ member' column delineates the performance of each IceNet ensemble
+ member (identified by the integer random seed value it was trained
+ with) and the ensemble mean models ('ensemble' or 'ensemble_tempscaled').
+ '''
+
+ multi_index = pd.MultiIndex.from_product(
+ [model_compute_list, leadtimes, all_target_dates])
+
+ idxs = []
+ for row in multi_index:
+ model = row[0]
+ row = [[item] for item in row]
+ if model == icenet_ID:
+ idxs.extend(list(itertools.product(*row, icenet_seeds)))
+ else:
+ idxs.extend(list(itertools.product(*row, ['NA'])))
+
+ multi_index = pd.MultiIndex.from_tuples(
+ idxs, names=['Model', 'Leadtime', 'Forecast date', 'Ensemble member']).\
+ reorder_levels(['Model', 'Ensemble member', 'Leadtime', 'Forecast date'])
+
+ return multi_index
+
+
+def make_varname_verbose(varname, leadtime, fc_month_idx):
+
+ '''
+ Takes IceNet short variable name (e.g. siconca_abs_3) and converts it to a
+ long name for a given forecast calendar month and lead time (e.g.
+ 'Feb SIC').
+
+ Inputs:
+ varname: Short variable name.
+ leadtime: Lead time of the forecast.
+ fc_month_index: Mod-12 calendar month index for the month being forecast
+ (e.g. 8 for September)
+
+ Returns:
+ verbose_varname: Long variable name.
+ '''
+
+ month_names = np.array(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec'])
+
+ varname_regex = re.compile('^(.*)_(abs|anom|linear_trend)_([0-9]+)$')
+
+ var_lookup_table = {
+ 'siconca': 'SIC',
+ 'tas': '2m air temperature',
+ 'ta500': '500 hPa air temperature',
+ 'tos': 'sea surface temperature',
+ 'rsds': 'downwelling solar radiation',
+ 'rsus': 'upwelling solar radiation',
+ 'psl': 'sea level pressure',
+ 'zg500': '500 hPa geopotential height',
+ 'zg250': '250 hPa geopotential height',
+ 'ua10': '10 hPa zonal wind speed',
+ 'uas': 'x-direction wind',
+ 'vas': 'y-direction wind'
+ }
+
+ initialisation_month_idx = (fc_month_idx - leadtime) % 12
+
+ varname_match = varname_regex.match(varname)
+
+ field = varname_match[1]
+ data_format = varname_match[2]
+ lead_or_lag = int(varname_match[3])
+
+ verbose_varname = ''
+
+ month_suffix = ' '
+ month_prefix = ''
+ if data_format != 'linear_trend':
+ # Read back from initialisation month to get input lag month
+ lag = lead_or_lag # In no of months
+ input_month_name = month_names[(initialisation_month_idx - lag + 1) % 12]
+
+ if (initialisation_month_idx - lag + 1) // 12 == -1:
+ # Previous calendar year
+ month_prefix = 'Previous '
+
+ elif data_format == 'linear_trend':
+ # Read forward from initialisation month to get linear trend forecast month
+ lead = lead_or_lag # In no of months
+ input_month_name = month_names[(initialisation_month_idx + lead) % 12]
+
+ if (initialisation_month_idx + lead) // 12 == 1:
+ # Next calendar year
+ month_prefix = 'Next '
+
+ # Month the input corresponds to
+ verbose_varname += month_prefix + input_month_name + month_suffix
+
+ # verbose variable name
+ if data_format != 'linear_trend':
+ verbose_varname += var_lookup_table[field]
+ if data_format == 'anom':
+ verbose_varname += ' anomaly'
+ elif data_format == 'linear_trend':
+ verbose_varname += 'linear trend SIC forecast'
+
+ return verbose_varname
+
+
+def make_varname_verbose_any_leadtime(varname):
+
+ ''' As above, but agnostic to what the target month or lead time is. E.g.
+ "SIC (1)" for sea ice concentration at a lag of 1 month. '''
+
+ varname_regex = re.compile('^(.*)_(abs|anom|linear_trend)_([0-9]+)$')
+
+ var_lookup_table = {
+ 'siconca': 'SIC',
+ 'tas': '2m air temperature',
+ 'ta500': '500 hPa air temperature',
+ 'tos': 'sea surface temperature',
+ 'rsds': 'downwelling solar radiation',
+ 'rsus': 'upwelling solar radiation',
+ 'psl': 'sea level pressure',
+ 'zg500': '500 hPa geopotential height',
+ 'zg250': '250 hPa geopotential height',
+ 'ua10': '10 hPa zonal wind speed',
+ 'uas': 'x-direction wind',
+ 'vas': 'y-direction wind',
+ 'land': 'land mask',
+ 'cos(month)': 'cos(init month)',
+ 'sin(month)': 'sin(init month)',
+ }
+
+ exception_vars = ['cos(month)', 'sin(month)', 'land']
+
+ if varname in exception_vars:
+ return var_lookup_table[varname]
+ else:
+ varname_match = varname_regex.match(varname)
+
+ field = varname_match[1]
+ data_format = varname_match[2]
+ lead_or_lag = int(varname_match[3])
+
+ # verbose variable name
+ if data_format != 'linear_trend':
+ verbose_varname = var_lookup_table[field]
+ if data_format == 'anom':
+ verbose_varname += ' anomaly'
+ elif data_format == 'linear_trend':
+ verbose_varname = 'Linear trend SIC forecast'
+
+ verbose_varname += ' ({:.0f})'.format(lead_or_lag)
+
+ return verbose_varname
+
+
+################################################################################
+# FUNCTIONS
+################################################################################
+
+
+def assignLatLonCoordSystem(cube):
+ ''' Assign coordinate system to iris cube to allow regridding. '''
+
+ cube.coord('latitude').coord_system = iris.coord_systems.GeogCS(6367470.0)
+ cube.coord('longitude').coord_system = iris.coord_systems.GeogCS(6367470.0)
+
+ return cube
+
+
+def fix_near_real_time_era5_func(latlon_path):
+
+ '''
+ Near-real-time ERA5 data is classed as a different dataset called 'ERA5T'.
+ This results in a spurious 'expver' dimension. This method detects
+ whether that dim is present and removes it, concatenating into one array
+ '''
+
+ ds = xr.open_dataarray(latlon_path)
+
+ if len(ds.data.shape) == 4:
+ print('Fixing spurious ERA5 "expver dimension for {}.'.format(latlon_path))
+
+ arr = xr.open_dataarray(latlon_path).data
+ arr = ds.data
+ # Expver 1 (ERA5)
+ era5_months = ~np.isnan(arr[:, 0, :, :]).all(axis=(1, 2))
+
+ # Expver 2 (ERA5T - near real time)
+ era5t_months = ~np.isnan(arr[:, 1, :, :]).all(axis=(1, 2))
+
+ ds = xr.concat((ds[era5_months, 0, :], ds[era5t_months, 1, :]), dim='time')
+
+ ds = ds.reset_coords('expver', drop=True)
+
+ os.remove(latlon_path)
+ ds.load().to_netcdf(latlon_path)
+
+
+###############################################################################
+# LEARNING RATE SCHEDULER
+###############################################################################
+
+
+def make_exp_decay_lr_schedule(rate, start_epoch=1, end_epoch=np.inf, verbose=False):
+
+ ''' Returns an exponential learning rate function that multiplies by
+ exp(-rate) each epoch after `start_epoch`. '''
+
+ def lr_scheduler_exp_decay(epoch, lr):
+ ''' Learning rate scheduler for fine tuning.
+ Exponential decrease after start_epoch until end_epoch. '''
+
+ if epoch >= start_epoch and epoch < end_epoch:
+ lr = lr * np.math.exp(-rate)
+
+ if verbose:
+ print('\nSetting learning rate to: {}\n'.format(lr))
+
+ return lr
+
+ return lr_scheduler_exp_decay
+
+
+###############################################################################
+# REGRIDDING VECTOR DATA
+###############################################################################
+
+
+def rotate_grid_vectors(u_cube, v_cube, angles):
+ """
+ Author: Tony Phillips (BAS)
+
+ Wrapper for :func:`~iris.analysis.cartography.rotate_grid_vectors`
+ that can rotate multiple masked spatial fields in one go by iterating
+ over the horizontal spatial axes in slices
+ """
+ # lists to hold slices of rotated vectors
+ u_r_all = iris.cube.CubeList()
+ v_r_all = iris.cube.CubeList()
+
+ # get the X and Y dimension coordinates for each source cube
+ u_xy_coords = [u_cube.coord(axis='x', dim_coords=True),
+ u_cube.coord(axis='y', dim_coords=True)]
+ v_xy_coords = [v_cube.coord(axis='x', dim_coords=True),
+ v_cube.coord(axis='y', dim_coords=True)]
+
+ # iterate over X, Y slices of the source cubes, rotating each in turn
+ for u, v in zip(u_cube.slices(u_xy_coords, ordered=False),
+ v_cube.slices(v_xy_coords, ordered=False)):
+ u_r, v_r = iris.analysis.cartography.rotate_grid_vectors(u, v, angles)
+ u_r_all.append(u_r)
+ v_r_all.append(v_r)
+
+ # return the slices, merged back together into a pair of cubes
+ return (u_r_all.merge_cube(), v_r_all.merge_cube())
+
+
+def gridcell_angles_from_dim_coords(cube):
+ """
+ Author: Tony Phillips (BAS)
+
+ Wrapper for :func:`~iris.analysis.cartography.gridcell_angles`
+ that derives the 2D X and Y lon/lat coordinates from 1D X and Y
+ coordinates identifiable as 'x' and 'y' axes
+
+ The provided cube must have a coordinate system so that its
+ X and Y coordinate bounds (which are derived if necessary)
+ can be converted to lons and lats
+ """
+
+ # get the X and Y dimension coordinates for the cube
+ x_coord = cube.coord(axis='x', dim_coords=True)
+ y_coord = cube.coord(axis='y', dim_coords=True)
+
+ # add bounds if necessary
+ if not x_coord.has_bounds():
+ x_coord = x_coord.copy()
+ x_coord.guess_bounds()
+ if not y_coord.has_bounds():
+ y_coord = y_coord.copy()
+ y_coord.guess_bounds()
+
+ # get the grid cell bounds
+ x_bounds = x_coord.bounds
+ y_bounds = y_coord.bounds
+ nx = x_bounds.shape[0]
+ ny = y_bounds.shape[0]
+
+ # make arrays to hold the ordered X and Y bound coordinates
+ x = np.zeros((ny, nx, 4))
+ y = np.zeros((ny, nx, 4))
+
+ # iterate over the bounds (in order BL, BR, TL, TR), mesh them and
+ # put them into the X and Y bound coordinates (in order BL, BR, TR, TL)
+ c = [0, 1, 3, 2]
+ cind = 0
+ for yi in [0, 1]:
+ for xi in [0, 1]:
+ xy = np.meshgrid(x_bounds[:, xi], y_bounds[:, yi])
+ x[:, :, c[cind]] = xy[0]
+ y[:, :, c[cind]] = xy[1]
+ cind += 1
+
+ # convert the X and Y coordinates to longitudes and latitudes
+ source_crs = cube.coord_system().as_cartopy_crs()
+ target_crs = ccrs.PlateCarree()
+ pts = target_crs.transform_points(source_crs, x.flatten(), y.flatten())
+ lons = pts[:, 0].reshape(x.shape)
+ lats = pts[:, 1].reshape(x.shape)
+
+ # get the angles
+ angles = iris.analysis.cartography.gridcell_angles(lons, lats)
+
+ # add the X and Y dimension coordinates from the cube to the angles cube
+ angles.add_dim_coord(y_coord, 0)
+ angles.add_dim_coord(x_coord, 1)
+
+ # if the cube's X dimension preceeds its Y dimension
+ # transpose the angles to match
+ if cube.coord_dims(x_coord)[0] < cube.coord_dims(y_coord)[0]:
+ angles.transpose()
+
+ return angles
+
+
+def invert_gridcell_angles(angles):
+ """
+ Author: Tony Phillips (BAS)
+
+ Negate a cube of gridcell angles in place, transforming
+ gridcell_angle_from_true_east <--> true_east_from_gridcell_angle
+ """
+ angles.data *= -1
+
+ names = ['true_east_from_gridcell_angle', 'gridcell_angle_from_true_east']
+ name = angles.name()
+ if name in names:
+ angles.rename(names[1 - names.index(name)])
+
+
+###############################################################################
+# CMIP6
+###############################################################################
+
+
+# Below taken from https://hub.binder.pangeo.io/user/pangeo-data-pan--cmip6-examples-ro965nih/lab
+def esgf_search(server="https://esgf-node.llnl.gov/esg-search/search",
+ files_type="OPENDAP", local_node=False, latest=True, project="CMIP6",
+ verbose1=False, verbose2=False, format="application%2Fsolr%2Bjson",
+ use_csrf=False, **search):
+ client = requests.session()
+ payload = search
+ payload["project"] = project
+ payload["type"] = "File"
+ if latest:
+ payload["latest"] = "true"
+ if local_node:
+ payload["distrib"] = "false"
+ if use_csrf:
+ client.get(server)
+ if 'csrftoken' in client.cookies:
+ # Django 1.6 and up
+ csrftoken = client.cookies['csrftoken']
+ else:
+ # older versions
+ csrftoken = client.cookies['csrf']
+ payload["csrfmiddlewaretoken"] = csrftoken
+
+ payload["format"] = format
+
+ offset = 0
+ numFound = 10000
+ all_files = []
+ files_type = files_type.upper()
+ while offset < numFound:
+ payload["offset"] = offset
+ url_keys = []
+ for k in payload:
+ url_keys += ["{}={}".format(k, payload[k])]
+
+ url = "{}/?{}".format(server, "&".join(url_keys))
+ if verbose1:
+ print(url)
+ r = client.get(url)
+ r.raise_for_status()
+ resp = r.json()["response"]
+ numFound = int(resp["numFound"])
+ resp = resp["docs"]
+ offset += len(resp)
+ for d in resp:
+ if verbose2:
+ for k in d:
+ print("{}: {}".format(k, d[k]))
+ url = d["url"]
+ for f in d["url"]:
+ sp = f.split("|")
+ if sp[-1] == files_type:
+ all_files.append(sp[0].split(".html")[0])
+ return sorted(all_files)
+
+
+def regrid_cmip6(cmip6_cube, grid_cube, verbose=False):
+
+ if verbose:
+ tic = time.time()
+ print("regridding... ", end='', flush=True)
+
+ cs = grid_cube.coord_system().ellipsoid
+
+ for coord in ['longitude', 'latitude']:
+ cmip6_cube.coord(coord).coord_system = cs
+
+ cmip6_ease = cmip6_cube.regrid(grid_cube, iris.analysis.Linear())
+
+ if verbose:
+ dur = time.time() - tic
+ print("done in {}m:{:.0f}s... ".format(np.floor(dur / 60), dur % 60), end='', flush=True)
+
+ return cmip6_ease
+
+
+def save_cmip6(cmip6_ease, fpath, compress=True, verbose=False):
+ tic = time.time()
+
+ if compress:
+ if verbose:
+ print('compressing & saving... ', end='', flush=True)
+ iris.fileformats.netcdf.save(cmip6_ease, fpath, complevel=7, zlib=True)
+ else:
+ if verbose:
+ print('saving uncompressed... ', end='', flush=True)
+ iris.save(cmip6_ease, fpath)
+
+ if verbose:
+ dur = time.time() - tic
+ print("done in {}m:{:.0f}s... ".format(np.floor(dur / 60), dur % 60), end='', flush=True)
+
+
+###############################################################################
+# PLOTTING
+###############################################################################
+
+
+def compute_heatmap(results_df, model, seed='NA', metric='Binary accuracy'):
+ '''
+ Returns a binary accuracy heatmap of lead time vs. calendar month
+ for a given model.
+ '''
+
+ month_names = np.array(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
+ 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec'])
+
+ # Mean over calendar month
+ mean_df = results_df.loc[model, seed].reset_index().\
+ groupby(['Calendar month', 'Leadtime']).mean()
+
+ # Pivot
+ heatmap_df = mean_df.reset_index().\
+ pivot('Calendar month', 'Leadtime', metric).reindex(month_names)
+
+ return heatmap_df
+
+
+def arr_to_ice_edge_arr(arr, thresh, land_mask, region_mask):
+
+ '''
+ Compute a boolean mask with True over ice edge contour grid cells using
+ matplotlib.pyplot.contour and an input threshold to define the ice edge
+ (e.g. 0.15 for the 15% SIC ice edge or 0.5 for SIP forecasts). The contour
+ along the coastline is removed using the region mask.
+ '''
+
+ X, Y = np.meshgrid(np.arange(arr.shape[0]), np.arange(arr.shape[1]))
+ X = X.T
+ Y = Y.T
+
+ cs = plt.contour(X, Y, arr, [thresh], alpha=0) # Do not plot on any axes
+ x = []
+ y = []
+ for p in cs.collections[0].get_paths():
+ x_i, y_i = p.vertices.T
+ x.extend(np.round(x_i))
+ y.extend(np.round(y_i))
+ x = np.array(x, int)
+ y = np.array(y, int)
+ ice_edge_arr = np.zeros(arr.shape, dtype=bool)
+ ice_edge_arr[x, y] = True
+ # Mask out ice edge contour that hugs the coastline
+ ice_edge_arr[land_mask] = False
+ ice_edge_arr[region_mask == 13] = False
+
+ return ice_edge_arr
+
+
+def arr_to_ice_edge_rgba_arr(arr, thresh, land_mask, region_mask, rgb):
+
+ ice_edge_arr = arr_to_ice_edge_arr(arr, thresh, land_mask, region_mask)
+
+ # Contour pixels -> alpha=1, alpha=0 elsewhere
+ ice_edge_rgba_arr = np.zeros((*arr.shape, 4))
+ ice_edge_rgba_arr[:, :, 3] = ice_edge_arr
+ ice_edge_rgba_arr[:, :, :3] = rgb
+
+ return ice_edge_rgba_arr
+
+
+###############################################################################
+# VIDEOS
+###############################################################################
+
+
+def xarray_to_video(da, video_path, fps, mask=None, mask_type='contour', clim=None,
+ crop=None, data_type='abs', video_dates=None, cmap='viridis',
+ figsize=15, dpi=300):
+
+ '''
+ Generate video of an xarray.DataArray. Optionally input a list of
+ `video_dates` to show, otherwise the full set of time coordiantes
+ of the dataset is used.
+
+ Parameters:
+ da (xr.DataArray): Dataset to create video of.
+
+ video_path (str): Path to save the video to.
+
+ fps (int): Frames per second of the video.
+
+ mask (np.ndarray): Boolean mask with True over masked elements to overlay
+ as a contour or filled contour. Defaults to None (no mask plotting).
+
+ mask_type (str): 'contour' or 'contourf' dictating whether the mask is overlaid
+ as a contour line or a filled contour.
+
+ data_type (str): 'abs' or 'anom' describing whether the data is in absolute
+ or anomaly format. If anomaly, the colorbar is centred on 0.
+
+ video_dates (list): List of Pandas Timestamps or datetime.datetime objects
+ to plot video from the dataset.
+
+ crop (list): [(a, b), (c, d)] to crop the video from a:b and c:d
+
+ clim (list): Colormap limits. Default is None, in which case the min and max values
+ of the array are used.
+
+ cmap (str): Matplotlib colormap.
+
+ figsize (int or float): Figure size in inches.
+
+ dpi (int): Figure DPI.
+ '''
+
+ if clim is not None:
+ min = clim[0]
+ max = clim[1]
+ elif clim is None:
+ max = da.max().values
+ min = da.min().values
+
+ if data_type == 'anom':
+ if np.abs(max) > np.abs(min):
+ min = -max
+ elif np.abs(min) > np.abs(max):
+ max = -min
+
+ def make_frame(date):
+ fig, ax = plt.subplots(figsize=(figsize, figsize))
+ fig.set_dpi(dpi)
+ im = ax.imshow(da.sel(time=date), cmap=cmap, clim=(min, max))
+ if mask is not None:
+ if mask_type == 'contour':
+ ax.contour(mask, levels=[.5, 1], colors='k')
+ elif mask_type == 'contourf':
+ ax.contourf(mask, levels=[.5, 1], colors='k')
+ ax.axes.xaxis.set_visible(False)
+ ax.axes.yaxis.set_visible(False)
+
+ ax.set_title('{:04d}/{:02d}/{:02d}'.format(date.year, date.month, date.day), fontsize=figsize * 4)
+
+ divider = make_axes_locatable(ax)
+ cax = divider.append_axes('right', size='5%', pad=0.05)
+ plt.colorbar(im, cax)
+
+ # TEMP crop to image
+ # fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
+
+ fig.canvas.draw()
+ image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')
+ image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+
+ plt.close()
+ return image
+
+ if video_dates is None:
+ video_dates = [pd.Timestamp(date).to_pydatetime() for date in da.time.values]
+
+ if crop is not None:
+ a = crop[0][0]
+ b = crop[0][1]
+ c = crop[1][0]
+ d = crop[1][1]
+ da = da.isel(xc=np.arange(a, b), yc=np.arange(c, d))
+ if mask is not None:
+ mask = mask[a:b, c:d]
+
+ imageio.mimsave(video_path,
+ [make_frame(date) for date in tqdm(video_dates)],
+ fps=fps)