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utils.py
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utils.py
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from .transformers import CustomUnpickler, RatioTransformer, ColumnTransformer
from .meta import get_sensor_bands, SENSOR_BANDS, ANCILLARY, PERIODIC
from .parameters import update, hypers
from .__version__ import __version__
from collections import defaultdict as dd
from datetime import datetime as dt
from pathlib import Path
from tqdm import trange
import pickle as pkl
import numpy as np
import hashlib, re, warnings, functools
from .meta import get_sensor_bands
import pandas as pd
import numpy as np
def set_kwargs_PC(sensor):
sensor_hash = {
'S3A-noBnoNIR' : 'c81355fa03928c9deb2e5d9519780eff42d3a961dda234a4d18327b0469843ff',
'S3B-noBnoNIR' : 'b03734e194de026165126378b565a9b262ac52625bfb84bd4be0d19d7a1b6313',
'HICO-noBnoNIR' : '08b2bce6e53b12d544b2424854430499db38c468224477bdecf247a67cc7d77f',
'PRISMA-noBnoNIR' : 'e9249d7825c92d34b91c21af17e4c58ddd1576ad9746205129c0a0d46833d3f0',
}
band_ratios_thresholded = {
'HICO-noBnoNIR' : [[507, 501], [512, 501], [512, 507], [518, 501], [518, 507], [518, 512], [524, 501], [524, 507], [524, 512], [524, 518], [530, 501], [530, 507], [530, 512], [530, 518], [530, 524], [535, 501], [535, 507], [535, 512], [535, 518], [535, 524], [535, 530], [541, 501], [541, 507], [541, 512], [541, 518], [541, 524], [541, 530], [541, 535], [547, 501], [547, 507], [547, 512], [547, 518], [547, 524], [547, 530], [547, 535], [547, 541], [553, 501], [553, 507], [553, 512], [553, 518], [553, 524], [553, 530], [553, 535], [553, 541], [558, 501], [558, 507], [558, 512], [558, 518], [558, 524], [558, 530], [558, 535], [564, 501], [564, 507], [564, 512], [564, 518], [564, 524], [564, 530], [570, 501], [570, 507], [570, 512], [570, 518], [570, 524], [575, 501], [575, 507], [575, 512], [575, 518], [575, 524], [581, 501], [581, 507], [581, 512], [581, 518], [587, 501], [587, 507], [587, 512], [587, 518], [593, 501], [593, 507], [593, 512], [593, 518], [598, 501], [598, 507], [598, 512], [604, 501], [604, 507], [604, 512], [610, 501], [610, 507], [616, 501], [616, 507], [616, 604], [616, 610], [621, 501], [621, 604], [621, 610], [621, 616], [627, 501], [633, 501], [633, 507], [633, 621], [633, 627], [638, 501], [638, 507], [638, 512], [638, 610], [638, 616], [638, 621], [638, 627], [638, 633], [644, 501], [644, 507], [644, 512], [644, 518], [644, 593], [644, 598], [644, 604], [644, 610], [644, 616], [644, 621], [644, 627], [644, 633], [644, 638], [650, 501], [650, 507], [650, 512], [650, 518], [650, 524], [650, 581], [650, 587], [650, 593], [650, 598], [650, 604], [650, 610], [650, 616], [650, 621], [650, 627], [650, 633], [650, 638], [650, 644], [656, 501], [656, 507], [656, 512], [656, 518], [656, 524], [656, 581], [656, 587], [656, 593], [656, 598], [656, 604], [656, 610], [656, 616], [656, 621], [656, 627], [656, 633], [656, 638], [656, 644], [661, 501], [661, 507], [661, 512], [661, 518], [661, 524], [661, 587], [661, 593], [661, 598], [661, 604], [661, 610], [661, 616], [661, 621], [661, 627], [661, 633], [661, 638], [661, 656], [667, 501], [667, 507], [667, 512], [667, 621], [667, 627], [667, 650], [667, 656], [667, 661], [673, 501], [673, 507], [673, 644], [673, 650], [673, 656], [673, 661], [673, 667], [679, 501], [679, 507], [679, 650], [679, 656], [679, 661], [684, 501], [684, 507], [684, 512], [684, 518], [684, 587], [684, 593], [684, 598], [684, 604], [684, 610], [684, 616], [684, 621], [684, 627], [684, 633], [684, 638], [684, 667], [684, 673], [684, 679], [690, 501], [690, 507], [690, 512], [690, 518], [690, 524], [690, 530], [690, 535], [690, 541], [690, 547], [690, 553], [690, 558], [690, 564], [690, 570], [690, 575], [690, 581], [690, 587], [690, 593], [690, 598], [690, 604], [690, 610], [690, 616], [690, 621], [690, 627], [690, 633], [690, 638], [690, 644], [690, 650], [690, 656], [690, 661], [690, 667], [690, 673], [690, 679], [690, 684], [696, 501], [696, 507], [696, 512], [696, 518], [696, 524], [696, 530], [696, 535], [696, 541], [696, 547], [696, 553], [696, 558], [696, 564], [696, 570], [696, 575], [696, 581], [696, 587], [696, 593], [696, 598], [696, 604], [696, 610], [696, 616], [696, 621], [696, 627], [696, 633], [696, 638], [696, 644], [696, 650], [696, 656], [696, 661], [696, 667], [696, 673], [696, 679], [696, 684], [696, 690], [701, 501], [701, 507], [701, 512], [701, 518], [701, 524], [701, 530], [701, 535], [701, 541], [701, 547], [701, 553], [701, 558], [701, 564], [701, 570], [701, 575], [701, 581], [701, 587], [701, 593], [701, 598], [701, 604], [701, 610], [701, 616], [701, 621], [701, 627], [701, 633], [701, 638], [701, 644], [701, 650], [701, 656], [701, 661], [701, 667], [701, 673], [701, 679], [701, 684], [701, 690], [701, 696], [707, 501], [707, 507], [707, 512], [707, 518], [707, 524], [707, 530], [707, 535], [707, 541], [707, 547], [707, 553], [707, 558], [707, 564], [707, 570], [707, 575], [707, 581], [707, 587], [707, 593], [707, 598], [707, 604], [707, 610], [707, 616], [707, 621], [707, 627], [707, 633], [707, 638], [707, 644], [707, 650], [707, 656], [707, 661], [707, 667], [707, 673], [707, 679], [707, 684], [707, 690], [707, 696], [707, 701], [713, 501], [713, 507], [713, 512], [713, 518], [713, 524], [713, 530], [713, 535], [713, 541], [713, 547], [713, 553], [713, 558], [713, 564], [713, 570], [713, 575], [713, 581], [713, 587], [713, 593], [713, 598], [713, 604], [713, 610], [713, 616], [713, 621], [713, 627], [713, 633], [713, 638], [713, 644], [713, 650], [713, 656], [713, 661], [713, 667], [713, 673], [713, 679], [713, 684], [713, 690], [713, 696], [713, 701], [713, 707], [719, 501], [719, 507], [719, 512], [719, 518], [719, 524], [719, 530], [719, 535], [719, 541], [719, 547], [719, 553], [719, 558], [719, 564], [719, 570], [719, 575], [719, 581], [719, 587], [719, 593], [719, 598], [719, 604], [719, 610], [719, 616], [719, 621], [719, 627], [719, 633], [719, 638], [719, 644], [719, 650], [719, 656], [719, 661], [719, 667], [719, 673], [719, 679], [719, 684], [719, 690], [719, 696], [719, 701], [719, 707], [719, 713], [724, 501], [724, 507], [724, 512], [724, 518], [724, 524], [724, 530], [724, 535], [724, 541], [724, 547], [724, 553], [724, 558], [724, 564], [724, 570], [724, 575], [724, 581], [724, 587], [724, 593], [724, 598], [724, 604], [724, 610], [724, 616], [724, 621], [724, 627], [724, 633], [724, 638], [724, 644], [724, 650], [724, 656], [724, 661], [724, 667], [724, 673], [724, 679], [724, 684], [724, 690], [724, 696], [724, 701], [724, 707]],
'PRISMA-noBnoNIR' : [[507, 500], [515, 500], [515, 507], [523, 500], [523, 507], [523, 515], [530, 500], [530, 507], [530, 515], [530, 523], [538, 500], [538, 507], [538, 515], [538, 523], [538, 530], [546, 500], [546, 507], [546, 515], [546, 523], [546, 530], [546, 538], [554, 500], [554, 507], [554, 515], [554, 523], [554, 530], [554, 538], [563, 500], [563, 507], [563, 515], [563, 523], [563, 530], [571, 500], [571, 507], [571, 515], [571, 523], [579, 500], [579, 507], [579, 515], [579, 523], [588, 500], [588, 507], [588, 515], [596, 500], [596, 507], [596, 515], [605, 500], [605, 507], [605, 515], [614, 500], [614, 507], [614, 605], [623, 500], [623, 605], [623, 614], [632, 500], [632, 507], [632, 623], [641, 500], [641, 507], [641, 515], [641, 605], [641, 614], [641, 623], [641, 632], [651, 500], [651, 507], [651, 515], [651, 523], [651, 588], [651, 596], [651, 605], [651, 614], [651, 623], [651, 632], [651, 641], [660, 500], [660, 507], [660, 515], [660, 523], [660, 588], [660, 596], [660, 605], [660, 614], [660, 623], [660, 632], [660, 641], [670, 500], [670, 507], [670, 515], [670, 651], [670, 660], [679, 500], [679, 507], [679, 651], [679, 660], [689, 500], [689, 507], [689, 515], [689, 523], [689, 530], [689, 538], [689, 546], [689, 554], [689, 563], [689, 571], [689, 579], [689, 588], [689, 596], [689, 605], [689, 614], [689, 623], [689, 632], [689, 641], [689, 651], [689, 660], [689, 670], [689, 679], [699, 500], [699, 507], [699, 515], [699, 523], [699, 530], [699, 538], [699, 546], [699, 554], [699, 563], [699, 571], [699, 579], [699, 588], [699, 596], [699, 605], [699, 614], [699, 623], [699, 632], [699, 641], [699, 651], [699, 660], [699, 670], [699, 679], [699, 689], [709, 500], [709, 507], [709, 515], [709, 523], [709, 530], [709, 538], [709, 546], [709, 554], [709, 563], [709, 571], [709, 579], [709, 588], [709, 596], [709, 605], [709, 614], [709, 623], [709, 632], [709, 641], [709, 651], [709, 660], [709, 670], [709, 679], [709, 689], [709, 699], [719, 500], [719, 507], [719, 515], [719, 523], [719, 530], [719, 538], [719, 546], [719, 554], [719, 563], [719, 571], [719, 579], [719, 588], [719, 596], [719, 605], [719, 614], [719, 623], [719, 632], [719, 641], [719, 651], [719, 660], [719, 670], [719, 679], [719, 689], [719, 699], [719, 709]],
'S3A-noBnoNIR' : [[560, 510], [664, 510], [664, 619], [673, 664], [681, 510], [681, 664], [681, 673], [708, 510], [708, 560], [708, 619], [708, 664], [708, 673], [708, 681]],
'S3B-noBnoNIR' : [[560, 510], [664, 510], [664, 619], [673, 664], [681, 510], [681, 664], [681, 673], [708, 510], [708, 560], [708, 619], [708, 664], [708, 673], [708, 681]],
}
line_heights_thresholded = {
'HICO-noBnoNIR' : [[507, 5], [512, 10], [518, 15], [535, 5], [535, 10], [541, 5], [541, 10], [541, 15], [541, 20], [547, 5], [547, 10], [547, 15], [547, 20], [553, 5], [553, 10], [553, 15], [553, 20], [558, 5], [558, 10], [558, 15], [558, 20], [575, 5], [575, 10], [581, 5], [581, 10], [581, 15], [604, 5], [616, 5], [616, 10], [616, 15], [616, 20], [621, 5], [621, 10], [621, 15], [621, 20], [627, 5], [627, 10], [627, 15], [627, 20], [633, 5], [633, 10], [633, 15], [633, 20], [638, 15], [638, 20], [644, 10], [644, 15], [644, 20], [650, 5], [650, 10], [650, 15], [650, 20], [656, 5], [656, 10], [656, 15], [656, 20], [661, 10], [661, 15], [661, 20], [667, 5], [667, 10], [667, 15], [667, 20], [673, 5], [673, 10], [673, 15], [673, 20], [679, 5], [679, 10], [679, 15], [679, 20], [684, 5], [684, 10], [684, 15], [684, 20], [690, 5], [690, 10], [690, 15], [690, 20], [696, 10], [696, 15], [696, 20], [701, 5], [701, 10], [701, 15], [701, 20], [707, 5], [707, 10], [707, 15], [713, 5], [713, 10], [719, 5]],
'PRISMA-noBnoNIR' : [[507, 5], [515, 5], [515, 10], [515, 15], [523, 5], [523, 10], [538, 15], [546, 5], [546, 10], [546, 15], [546, 20], [554, 5], [554, 10], [554, 15], [554, 20], [563, 5], [563, 10], [563, 15], [563, 20], [579, 5], [579, 10], [579, 15], [579, 20], [605, 5], [605, 10], [623, 5], [623, 10], [623, 15], [623, 20], [632, 5], [632, 10], [632, 15], [632, 20], [651, 5], [651, 10], [651, 15], [651, 20], [660, 10], [660, 15], [660, 20], [670, 5], [670, 10], [670, 15], [670, 20], [679, 10], [679, 15], [679, 20], [689, 10], [689, 15], [689, 20], [699, 10], [699, 15], [699, 20], [709, 10]],
'S3A-noBnoNIR' : [[673, 5], [673, 10]],
'S3B-noBnoNIR' : [[673, 5], [673, 10]]
}
kwargs_PC = {
'sensor' : sensor,
'model_hash' : sensor_hash[sensor],
'band_ratios_thresholded' : band_ratios_thresholded[sensor],
'line_heights_thresholded' : line_heights_thresholded[sensor],
}
return kwargs_PC
def ignore_warnings(func):
''' Decorator to silence warnings (Runtime, User, Deprecation, etc.) '''
@functools.wraps(func)
def helper(*args, **kwargs):
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
return func(*args, **kwargs)
return helper
def find_wavelength(k, waves, validate=True, tol=5):
''' Index of closest wavelength '''
i = np.abs(np.array(waves) - k).argmin()
assert(not validate or (abs(k-waves[i]) <= tol)), f'Needed {k}nm, but closest was {waves[i]}nm in {waves}'
return i
def closest_wavelength(k, waves, validate=True, tol=5):
''' Value of closest wavelength '''
return waves[find_wavelength(k, waves, validate, tol)]
def safe_int(v):
''' Parse int if possible, and return None otherwise '''
try: return int(v)
except: return None
def get_wvl(nc_data, key):
''' Get all wavelengths associated with the given key, available within the netcdf '''
wvl = [safe_int(v.replace(key, '')) for v in nc_data.variables.keys() if key in v]
return np.array(sorted([w for w in wvl if w is not None]))
def line_messages(messages):
'''
Allow multiline message updates via tqdm.
Need to call print() after the tqdm loop,
equal to the number of messages which were
printed via this function (to reset cursor).
Usage:
for i in trange(5):
messages = [i, i/2, i*2]
line_messages(messages)
for _ in range(len(messages)): print()
'''
for i, m in enumerate(messages, 1):
trange(1, desc=str(m), position=i, bar_format='{desc}')
def get_labels(wavelengths, slices, n_out=None):
'''
Helper to get label for each target output. Assumes
that any variable in <slices> which has more than a
single slice index, will have an associated wavelength
label.
Usage:
wavelengths = [443, 483, 561, 655]
slices = {'bbp':slice(0,4), 'chl':slice(4,5), 'tss':slice(5,6)}
n_out = 5
labels = get_labels(wavelengths, slices, n_out)
# labels -> ['bbp443', 'bbp483', 'bbp561', 'bbp655', 'chl']
'''
return [k + (f'{wavelengths[i]:.0f}' if (v.stop - v.start) > 1 else '')
for k,v in sorted(slices.items(), key=lambda s: s[1].start)
for i in range(v.stop - v.start)][:n_out]
def store_pkl(filename, output):
''' Helper to write pickle file '''
with Path(filename).open('wb') as f:
pkl.dump(output, f)
return output
def read_pkl(filename):
''' Helper to read pickle file '''
with Path(filename).open('rb') as f:
return CustomUnpickler(f).load()
def cache(filename, recache=False):
''' Decorator for caching function outputs '''
path = Path(filename)
def wrapper(function):
def inner(*args, **kwargs):
if not recache and path.exists():
return read_pkl(path)
return store_pkl(path, function(*args, **kwargs))
return inner
return wrapper
def using_feature(args, flag):
'''
Certain hyperparameter flags have a yet undecided default value,
which means there are two possible names: using the feature, or
not using it. This method simply combines both into a single
boolean signal, which indicates whether to add the feature.
For example:
use_flag = hasattr(args, 'use_ratio') and args.use_ratio
no_flag = hasattr(args, 'no_ratio') and not args.no_ratio
signal = use_flag or no_flag # if true, we add ratios
becomes
signal = using_feature(args, 'ratio') # if true, we add ratios
'''
assert(hasattr(args,f'use_{flag}') or hasattr(args, f'no_{flag}')), f'"{flag}" flag not found'
return getattr(args, f'use_{flag}', False) or not getattr(args, f'no_{flag}', True)
def split_data(x_data, other_data=[], n_train=0.5, n_valid=0, seed=None, shuffle=True,split_by_set=False,testing_dataset=None,data_subset_name_list=None):
'''
Split the given data into training, validation, and testing
subsets, randomly shuffling the original data order.
'''
if not isinstance(other_data, list): other_data = [other_data]
data = [d.iloc if hasattr(d, 'iloc') else d for d in [x_data] + other_data]
if split_by_set:
testing_indices = [i for i, current_subset_name in enumerate(data_subset_name_list) if current_subset_name == testing_dataset]
training_indices = [i for i, current_subset_name in enumerate(data_subset_name_list) if current_subset_name != testing_dataset]
test = [d[testing_indices] for d in data]
train = [d[training_indices] for d in data]
return train, test
else:
random = np.random.RandomState(seed)
idxs = np.arange(len(x_data))
if shuffle: random.shuffle(idxs)
# Allow both a percent to be passed in, as well as an absolute number
if 0 < n_train <= 1: n_train = int(n_train * len(idxs))
if 0 < n_valid <= 1: n_valid = int(n_valid * len(idxs))
assert((n_train+n_valid) <= len(x_data)), \
'Too many training/validation samples requested: {n_train}, {n_valid} ({len(x_data)} available)'
train = [d[ idxs[:n_train] ] for d in data]
valid = [d[ idxs[n_train:n_valid+n_train] ] for d in data]
test = [d[ idxs[n_train+n_valid:] ] for d in data]
# Return just the split x_data if no other data was given
if len(data) == 1:
train = train[0]
valid = valid[0]
test = test[0]
# If no validation data was requested, just return train/test
if n_valid == 0:
return train, test
return train, valid, test
@ignore_warnings
def mask_land(data, bands, threshold=0.2, verbose=False):
''' Modified Normalized Difference Water Index, or NDVI if 1500nm+ is not available '''
green = closest_wavelength(560, bands, validate=False)
red = closest_wavelength(700, bands, validate=False)
nir = closest_wavelength(900, bands, validate=False)
swir = closest_wavelength(1600, bands, validate=False)
b1, b2 = (green, swir) if swir > 1500 else (red, nir)
i1, i2 = find_wavelength(b1, bands), find_wavelength(b2, bands)
n_diff = lambda a, b: np.ma.masked_invalid((a-b) / (a+b))
if verbose: print(f'Using bands {b1} & {b2} for land masking')
return n_diff(data[..., i1], data[..., i2]).filled(fill_value=threshold-1) <= threshold
@ignore_warnings
def _get_tile_wavelengths(nc_data, key, sensor, allow_neg=True, landmask=False):
''' Return the Rrs/rhos data within the netcdf file, for wavelengths of the given sensor '''
has_key = lambda k: any([k in v for v in nc_data.variables])
wvl_key = f'{key}_' if has_key(f'{key}_') or key != 'Rrs' else 'Rw' # Polymer stores Rw=Rrs*pi
if has_key(wvl_key):
avail = get_wvl(nc_data, wvl_key)
bands = [closest_wavelength(b, avail) for b in get_sensor_bands(sensor)]
div = np.pi if wvl_key == 'Rw' else 1
data = np.ma.stack([nc_data[f'{wvl_key}{b}'][:] / div for b in bands], axis=-1)
if not allow_neg: data[data <= 0] = np.nan
if landmask: data[ mask_land(data, bands) ] = np.nan
return bands, data.filled(fill_value=np.nan)
return [], np.array([])
def get_tile_data(filenames, sensor, allow_neg=True, rhos=False, anc=False):
''' Gather the correct Rrs/rhos bands from a given scene, as well as ancillary features if necessary '''
from netCDF4 import Dataset
filenames = np.atleast_1d(filenames)
features = ['rhos' if rhos else 'Rrs'] + (ANCILLARY if anc or rhos else [])
data = {}
available = []
# Some sensors use different bands for their rhos models
if rhos and '-rho' not in sensor: sensor += '-rho'
for filename in filenames:
with Dataset(filename, 'r') as nc_data:
if 'geophysical_data' in nc_data.groups.keys():
nc_data = nc_data['geophysical_data']
for feature in features:
if feature not in data:
if feature in ['Rrs', 'rhos']:
bands, band_data = _get_tile_wavelengths(nc_data, feature, sensor, allow_neg, landmask=rhos)
if len(bands) > 0:
assert(len(band_data.shape) == 3), \
f'Different shape than expected: {band_data.shape}'
data[feature] = band_data
elif feature in nc_data.variables:
var = nc_data[feature][:]
assert(len(var.shape) == 2), f'Different shape than expected: {var.shape}'
if feature in PERIODIC:
assert(var.min() >= -180 and var.max() <= 180), \
f'Need to adjust transformation for variables not within [-180,180]: {feature}=[{var.min()}, {var.max()}]'
data[feature] = np.stack([
np.sin(2*np.pi*(var+180)/360),
np.cos(2*np.pi*(var+180)/360),
], axis=-1)
else: data[feature] = var
# Time difference should just be 0: we want estimates for the exact time of overpass
if 'time_diff' in features:
assert(features[0] in data), f'Missing {features[0]} data: {list(data.keys())}'
data['time_diff'] = np.zeros_like(data[features[0]][:, :, 0])
assert(len(data) == len(features)), f'Missing features: Found {list(data.keys())}, Expecting {features}'
return bands, np.dstack([data[f] for f in features])
def generate_config(args, create=True, verbose=True):
'''
Create a config file for the current settings, and store in
a folder location determined by certain parameters:
MDN/model_loc/sensor/model_lbl/model_hash/config
"model_hash" is computed within this function, but a value can
also be passed in manually via args.model_hash in order to allow
previous MDN versions to run.
'''
root = Path(__file__).parent.resolve().joinpath(args.model_loc, args.sensor, args.model_lbl)
# Can override the model hash in order to allow prior MDN versions to be run
if hasattr(args, 'model_hash'):
if args.verbose: print(f'Using manually set model hash: {args.model_hash}')
return root.joinpath(args.model_hash)
dependents = [getattr(act, 'dest', '') for group in [hypers, update] for act in group._group_actions]
dependents+= ['x_scalers', 'y_scalers']
config = [f'Version: {__version__}']
config+= [''.join(['-']*len(config[-1]))]
others = [''.join(['-']*len(config[-1]))]
for k,v in sorted(args.__dict__.items(), key=lambda z: z[0]):
if k in ['x_scalers', 'y_scalers']:
v = [(s[0].__name__,)+s[1:] for s in v] # stringify scaler and its arguments
if k in dependents: config.append(f'{k}: {v}')
else: others.append(f'{k}: {v}')
config = '\n'.join(config) # Model is dependent on some arguments, so they change the hash
others = '\n'.join(others) # Other arguments are stored for replicability
ver_re = r'(Version\: \d+\.\d+)(?:\.\d+\n[-]+\n)' # Match major/minor version within subgroup, patch/dashes within pattern
h_str = re.sub(ver_re, r'\1.0\n', config) # Substitute patch version for ".0" to allow patches within the same hash
uid = hashlib.sha256(h_str.encode('utf-8')).hexdigest()
folder = root.joinpath(uid)
c_file = folder.joinpath('config')
if args.verbose:
print(f'Using model path {folder}')
if create:
folder.mkdir(parents=True, exist_ok=True)
if not c_file.exists():
with c_file.open('w+') as f:
f.write(f'Created: {dt.now()}\n{config}\n{others}')
elif not c_file.exists() and verbose:
print('\nCould not find config file with the following parameters:')
print('\t'+config.replace('\n','\n\t'),'\n')
return folder
def _load_datasets(keys, locs, wavelengths, allow_missing=False,args=None,load_lat_lon=False):
'''
Load data from [<locs>] using <keys> as the columns.
Only loads data which has all the bands defined by
<wavelengths> (if necessary, e.g. for Rrs or bbp).
First key is assumed to be the x_data, remaining keys
(if any) are y_data.
- allow_missing=True will allow datasets which are missing bands
to be included in the returned data
Usage:
# Here, data/loc/Rrs.csv, data/loc/Rrs_wvl.csv, data/loc/bbp.csv,
# and data/chl.csv all exist, with the correct wavelengths available
# for Rrs and bbp (which is determined by Rrs_wvl.csv)
keys = ['Rrs', 'bbp', '../chl']
locs = 'data/loc'
wavelengths = [443, 483, 561, 655]
_load_datasets(keys, locs, wavelengths) # -> [Rrs443, Rrs483, Rrs561, Rrs665],
[bbp443, bbp483, bbp561, bbp655, chl],
{'bbp':slice(0,4), 'chl':slice(4,5)}
'''
def loadtxt(name, loc, _wavelengths):
''' Error handling wrapper over np.loadtxt, with the addition of wavelength selection'''
#If the key is Rrs_full_wavelengths, the name is reset to Rrs
# print(name,loc,_wavelengths)
if name == 'Rrs_full_wavelengths':
name = 'Rrs'
return_full_wavelength = True
else:
return_full_wavelength = False
dloc = Path(loc).joinpath(f'{name}.csv')
# TSS / TSM are synonymous
if 'tss' in name and not dloc.exists():
dloc = Path(loc).joinpath(f'{name.replace("tss","tsm")}.csv')
# CDOM is just an alias for a_cdom(443)
if 'cdom' in name and not dloc.exists():
dloc = Path(loc).joinpath('ag.csv')
try:
assert(dloc.exists()), (f'Key {name} does not exist at {loc} ({dloc})')
data = np.loadtxt(dloc, delimiter=',', dtype=float if name not in ['../Dataset', '../meta'] else str, comments=None)
if len(data.shape) == 1:
data = data[:, None]
if data.shape[1] > 1 and data.dtype.type is not np.str_:
# print('Within IF')
# If we want to get all data, regardless of if bands are available...
if allow_missing:
new_data = [[np.nan]*len(data)] * len(_wavelengths)
wvls = np.loadtxt(Path(loc).joinpath(f'{name}_wvl.csv'), delimiter=',')[:,None]
idxs = np.abs(wvls - np.atleast_2d(_wavelengths)).argmin(0)
valid = np.abs(wvls - np.atleast_2d(_wavelengths)).min(0) < 2
for j, (i, v) in enumerate(zip(idxs, valid)):
if v: new_data[j] = data[:, i]
data = np.array(new_data).T
else:
if str(name) == "../Rrs_OG" or str(name) == "../Rrs_OG_wvl":
pad_size = 1000-np.shape(data)[1]
data = np.pad(data,[(0,0),(0,pad_size)],'constant',constant_values=(np.nan,np.nan))
else:
if return_full_wavelength:
sensor = args.sensor.split('-')[0]
bands = get_sensor_bands(sensor+'-SimisFull', args)
data = data[:, get_valid_full(name, loc, _wavelengths,bands)]
else:
data = data[:, get_valid(name, loc, _wavelengths)]
if 'cdom' in name and dloc.stem == 'ag':
data = data[:, find_wavelength(443, np.loadtxt(Path(loc).joinpath(f'{dloc.stem}_wvl.csv'), delimiter=','))].flatten()[:, None]
return data
except Exception as e:
if dloc.exists():
print(f'Error fetching {name} from {loc}: {e}')
if name not in ['Rrs']:# ['../chl', '../tss', '../cdom']:
return np.array([]).reshape((0,0))
assert(0), e
def get_valid(name, loc, _wavelengths, margin=2):
''' Dataset at <loc> must have all bands in <_wavelengths> within <margin>nm '''
if 'HYPER' in str(loc): margin=1
wvls = np.loadtxt(Path(loc).joinpath(f'{name}_wvl.csv'), delimiter=',')[:,None]
assert(np.all([np.abs(wvls-w).min() <= margin for w in _wavelengths])), (
f'{loc} is missing wavelengths: \n{_wavelengths} needed,\n{wvls.flatten()} found')
if len(wvls) != len(_wavelengths):
valid = np.abs(wvls - np.atleast_2d(_wavelengths)).min(1) < margin
assert(valid.sum() == len(_wavelengths)), [wvls[valid].flatten(), _wavelengths]
return valid
return np.array([True] * len(_wavelengths))
def get_valid_full(name, loc, _wavelengths, wavelengths_full, margin=2):
''' Dataset at <loc> must have all bands in <_wavelengths> within <margin>nm '''
if 'HYPER' in str(loc): margin=1
wvls = np.loadtxt(Path(loc).joinpath(f'{name}_wvl.csv'), delimiter=',')[:,None]
assert(np.all([np.abs(wvls-w).min() <= margin for w in _wavelengths])), (
f'{loc} is missing wavelengths: \n{_wavelengths} needed,\n{wvls.flatten()} found')
if len(wvls) != len(wavelengths_full):
valid = np.abs(wvls - np.atleast_2d(wavelengths_full)).min(1) < margin
assert(valid.sum() == len(wavelengths_full)), [wvls[valid].flatten(), _wavelengths]
return valid
return np.array([True] * len(wavelengths_full))
print('\n-------------------------')
print(f'Loading data for sensor {locs[0].parts[-1]}')
if allow_missing:
print('Allowing data regardless of whether all bands exist')
x_data = []
y_data = []
l_data = []
z_data = []
z_w_data = []
x_data_full = []
lat_data = []
lon_data = []
for loc in np.atleast_1d(locs):
try:
loc_data = [loadtxt(key, loc, wavelengths) for key in keys]
print(f'\tN={len(loc_data[0]):>5} | {loc.parts[-1]} / {loc.parts[-2]} ({[np.isfinite(ld).all(1).sum() for ld in loc_data[1:]]})')
assert(all([len(l) in [len(loc_data[0]), 0] for l in loc_data])), dict(zip(keys, map(np.shape, loc_data)))
if all([l.shape[1] == 0 for l in loc_data[1:]]):
print(f'Skipping dataset {loc}: missing all features')
continu
x_data += [loc_data.pop(0)]
z_data += [loc_data.pop(0)]
z_w_data += [loc_data.pop(0)]
x_data_full += [loc_data.pop(0)]
if load_lat_lon:
lat_data += [loc_data.pop(0)]
lon_data += [loc_data.pop(0)]
starting_len = 6
else:
starting_len = 4
y_data += [loc_data]
l_data += list(zip([loc.parent.name] * len(x_data[-1]), np.arange(len(x_data[-1]))))
except Exception as e:
print(f'Error {loc}: {e}')
if len(np.atleast_1d(locs)) == 1:
raise e
assert(len(x_data) > 0 or len(locs) == 0), 'No datasets are valid with the given wavelengths'
assert(all([x.shape[1] == x_data[0].shape[1] for x in x_data])), f'Differing number of {keys[0]} wavelengths: {[x.shape for x in x_data]}'
# Determine the number of features each key should have
slices = []
for i, key in enumerate(keys[starting_len:]):
print('KEYS',key)
shapes = [y[i].shape[1] for y in y_data]
slices.append(max(shapes))
for x, y in zip(x_data, y_data):
if y[i].shape[1] == 0:
y[i] = np.full((x.shape[0], max(shapes)), np.nan)
assert(all([y[i].shape[1] == y_data[0][i].shape[1] for y in y_data])), f'{key} shape mismatch: {[y.shape for y in y_data]}'
# Drop any missing features
drop = []
for i, s in enumerate(slices):
if s == 0:
print(f'Dropping {keys[i+1]}: feature has no samples available')
drop.append(i)
slices = np.cumsum([0] + [s for i,s in enumerate(slices) if i not in drop])
keys = [k for i,k in enumerate(keys[starting_len:]) if i not in drop]
for y in y_data:
y = [z for i,z in enumerate(y) if i not in drop]
# Combine everything together
l_data = np.vstack(l_data)
x_data = np.vstack(x_data)
y_data = np.vstack([np.hstack(y) for y in y_data])
z_data = np.vstack(z_data)
x_data_full = np.vstack(x_data_full)
z_w_data = np.vstack(z_w_data)
if load_lat_lon:
lon_data = np.vstack(lon_data)
lat_data = np.vstack(lat_data)
assert(slices[-1] == y_data.shape[1]), [slices, y_data.shape]
assert(y_data.shape[0] == x_data.shape[0]), [x_data.shape, y_data.shape]
slices = {k.replace('../','') : slice(slices[i], s) for i,(k,s) in enumerate(zip(keys, slices[1:]))}
print(f'\tTotal prior to filtering: {len(x_data)}')
# Fit exponential function to ad and ag values, and eliminate samples with too much error
for product in ['ad', 'ag']:
if product in slices:
from .metrics import mdsa
from scipy.optimize import curve_fit
exponential = lambda x, a, b, c: a * np.exp(-b*x) + c
remove = np.zeros_like(y_data[:,0]).astype(bool)
for i, sample in enumerate(y_data):
sample = sample[slices[product]]
assert(len(sample) > 5), f'Number of bands should be larger, when fitting exponential: {product}, {sample.shape}'
assert(len(sample) == len(wavelengths)), f'Sample size / wavelengths mismatch: {len(sample)} vs {len(wavelengths)}'
if np.all(np.isfinite(sample)) and np.min(sample) > -0.1:
try:
x = np.array(wavelengths) - np.min(wavelengths)
params, _ = curve_fit(exponential, x, sample, bounds=((1e-3, 1e-3, 0), (1e2, 1e0, 1e1)))
new_sample = exponential(x, *params)
# Should be < 10% error between original and fitted exponential
if mdsa(sample[None,:], new_sample[None,:]) < 10:
y_data[i, slices[product]] = new_sample
else: remove[i] = True # Exponential could be fit, but error was too high
except: remove[i] = True # Sample deviated so much from a smooth exponential decay that it could not be fit
# else: remove[i] = True # NaNs / negatives in the sample
# Don't actually drop them yet, in case we are fetching all samples regardless of nan composition
x_data[remove] = np.nan
y_data[remove] = np.nan
l_data[remove] = np.nan
if remove.sum():
print(f'Removed {remove.sum()} / {len(remove)} samples due to poor quality {product} spectra')
assert((~remove).sum()), f'All data removed due to {product} spectra quality...'
return x_data, y_data, slices, l_data, z_data, z_w_data,x_data_full, lat_data, lon_data
def _filter_invalid(x_data, y_data, slices, allow_nan_inp=False, allow_nan_out=False, other=[],z_data=[],z_w_data=[],x_data_full=[],lat_data=[],lon_data=[],load_lat_lon=False):
'''
Filter the given data to only include samples which are valid. By
default, valid samples include all which are not nan, and greater
than zero (for all target features).
- allow_nan_inp=True can be set to allow a sample as valid if _any_
of a sample's input x features are not nan and greater than zero.
- allow_nan_out=True can be set to allow a sample as valid if _any_
of a sample's target y features are not nan and greater than zero.
- "other" is an optional set of parameters which will be pruned with the
test sets (i.e. passing a list of indices will return the indices which
were kept)
Multiple data sets can also be passed simultaneously as a list to the
respective parameters, in order to filter the same samples out of all
data sets (e.g. OLI and S2B data, containing same samples but different
bands, can be filtered so they end up with the same samples relative to
each other).
'''
# Allow multiple sets to be given, and align them all to the same sample subset
if type(x_data) is not list: x_data = [x_data]
if type(y_data) is not list: y_data = [y_data]
if type(z_data) is not list: z_data = [z_data]
if type(z_w_data) is not list: z_w_data = [z_w_data]
if type(x_data_full) is not list: x_data_full = [x_data_full]
if type(lat_data) is not list: lat_data = [lat_data]
if type(lon_data) is not list: lon_data = [lon_data]
if type(other) is not list: other = [other]
both_data = [x_data, y_data]
set_length = [len(fullset) for fullset in both_data]
set_shape = [[len(subset) for subset in fullset] for fullset in both_data]
assert(np.all([length == len(x_data) for length in set_length])), \
f'Mismatching number of subsets: {set_length}'
assert(np.all([[shape == len(fullset[0]) for shape in shapes]
for shapes, fullset in zip(set_shape, both_data)])), \
f'Mismatching number of samples: {set_shape}'
assert(len(other) == 0 or all([len(o) == len(x_data[0]) for o in other])), \
f'Mismatching number of samples within other data: {[len(o) for o in other]}'
# Ensure only positive / finite testing features, but allow the
# possibility of some nan values in x_data (if allow_nan_inp is
# set) or in y_data (if allow_nan_out is set) - so long as the
# sample has other non-nan values in the respective feature set
valid = np.ones(len(x_data[0])).astype(np.bool)
for i, fullset in enumerate(both_data):
for subset in fullset:
subset[np.isnan(subset)] = -999.
subset[np.logical_or(subset <= 0, not i and (subset >= 10))] = np.nan
has_nan = np.any if (i and allow_nan_out) or (not i and allow_nan_inp) else np.all
valid = np.logical_and(valid, has_nan(np.isfinite(subset), 1))
x_data = [x[valid] for x in x_data]
y_data = [y[valid] for y in y_data]
z_data = [z[valid] for z in z_data]
x_data_full =[x_full[valid] for x_full in x_data_full]
z_w_data = [z_w[valid] for z_w in z_w_data]
if load_lat_lon:
lat_data = [lat[valid] for lat in lat_data]
lon_data = [lon[valid] for lon in lon_data]
else:
lat_data = [0]
lon_data = [0]
print(f'Removed {(~valid).sum()} invalid samples ({valid.sum()} remaining)')
assert(valid.sum()), 'All samples have nan or negative values'
if len(other) > 0:
return x_data, y_data, [np.array(o)[valid] for o in other], z_data, z_w_data, x_data_full,lat_data,lon_data
return x_data, y_data, z_data, z_w_data, x_data_full,lat_data,lon_data
def get_data(args,return_PRISMA_testing=False,return_full_bands=False,load_lat_lon=False):
''' Main function for gathering datasets '''
np.random.seed(args.seed)
sensor = args.sensor.split('-')[0]
products = args.product.split(',')
bands = get_sensor_bands(args.sensor, args)
# Using Hydrolight simulated data
if using_feature(args, 'sim'):
assert(not using_feature(args, 'ratio')), 'Too much memory needed for simulated+ratios'
data_folder = ['848']
data_keys = ['Rrs', 'bb_p', 'a_p', '../chl', '../tss', '../cdom']
data_path = Path(args.sim_loc)
else:
if products[0] == 'all':
products = ['chl', 'tss', 'cdom', 'ad', 'ag', 'aph']# + ['a*ph', 'apg', 'a']
data_folder = []
data_keys = ['Rrs']
data_keys += ['../Rrs_OG']
data_keys += ['../Rrs_OG_wvl']
data_keys += ['Rrs_full_wavelengths']
if load_lat_lon:
data_keys += ['../lat']
data_keys += ['../lon']
if return_PRISMA_testing:
data_path = Path("/home/ryanoshea/MDN_PC/MDN/script_formatted_data/Ryan_data/Data/Test_21_PRISMA_Curonia")
else:
data_path = Path(args.data_loc)
get_dataset = lambda path, p: Path(path.as_posix().replace(f'/{sensor}','').replace(f'/{p}.csv','')).stem
for product in products:
if product in ['chl', 'tss', 'cdom','PC']:
product = f'../{product}'
# MSI / OLCI paper
if args.dataset == 'sentinel_paper' and product == '../chl':
datasets = ['Sundar', 'UNUSED/Taihu_old', 'UNUSED/Taihu2', 'UNUSED/Schalles_old', 'SeaBASS2', 'Vietnam']
# Find all datasets with the given product available
else:
safe_prod = product.replace('*', '[*]') # Prevent glob from getting confused by wildcard
datasets = [get_dataset(path, product) for path in data_path.glob(f'*/{sensor}/{safe_prod}.csv')]
if product == 'aph':
datasets = [d for d in datasets if d not in ['PACE']]
if product == '../chl':
datasets = [d for d in datasets if d not in ['Arctic']] # Bunkei contained within sundar's set
data_folder += datasets
data_keys += [product]
# Get only unique entries, while also preserving insertion order
order_unique = lambda a: [a[i] for i in sorted(np.unique(a, return_index=True)[1])]
data_folder = order_unique(data_folder)
data_keys = order_unique(data_keys)
assert(len(data_folder)), f'No datasets found for {products} within {data_path}/*/{sensor}'
assert(len(data_keys)), f'No variables found for {products} within {data_path}/*/{sensor}'
sensor_loc = [data_path.joinpath(f, sensor) for f in data_folder]
x_data, y_data, slices, sources,z_data, z_w_data,x_data_full,lat_data,lon_data = _load_datasets(data_keys, sensor_loc, bands, allow_missing=('-nan' in args.sensor) or (getattr(args, 'align', None) is not None),args=args,load_lat_lon=load_lat_lon)
# Hydrolight simulated CDOM is incorrectly scaled
if using_feature(args, 'sim') and 'cdom' in slices:
y_data[:, slices['cdom']] *= 0.18
# Allow data from one sensor to be aligned with other sensors (so the samples will be the same across sensors)
if getattr(args, 'align', None) is not None:
assert('-nan' not in args.sensor), 'Cannot allow all samples via "-nan" while also aligning to other sensors'
align = args.align.split(',')
if 'all' in align:
align = [s for s in SENSOR_LABELS.keys() if s != 'HYPER']
align_loc = [[data_path.joinpath(f, a.split('-')[0]) for f in data_folder] for a in align]
print(f'\nLoading alignment data for {align}...')
x_align, y_align, slices_align, sources_align,z_data, z_w_data,x_data_full,lat_data,lon_data = map(list,
zip(*[_load_datasets(data_keys, loc, get_sensor_bands(a, args), allow_missing=True,load_lat_lon=load_lat_lon) for a, loc in zip(align, align_loc)]))
x_data = [x_data] + x_align
y_data = [y_data] + y_align
# if -nan IS in the sensor label: do not filter samples; allow all, regardless of nan compositio
if '-nan' not in args.sensor:
(x_data, *_), (y_data, *_), (sources, *_),(z_data,*_),(z_w_data,*_),(x_data_full,*_),(lat_data,*_),(lon_data,*_)= _filter_invalid(x_data, y_data, slices, other=[sources], allow_nan_out=args.allow_nan_out,z_data=z_data,z_w_data=z_w_data,x_data_full=x_data_full,lat_data=lat_data,lon_data=lon_data,load_lat_lon=load_lat_lon)
print('\nFinal counts:')
print('\n'.join([f'\tN={num:>5} | {loc}' for loc, num in zip(*np.unique(sources[:, 0], return_counts=True))]))
print(f'\tTotal: {len(sources)}')
# Correct chl data for pheopigments
if 'chl' in args.product and using_feature(args, 'tchlfix'):
assert(not using_feature(args, 'sim')), 'Simulated data does not need TChl correction'
y_data = _fix_tchl(y_data, sources, slices, data_path)
return x_data, y_data, slices, sources, z_data, z_w_data,x_data_full,lat_data,lon_data
def _fix_tchl(y_data, sources, slices, data_path, debug=False):
''' Very roughly correct chl for pheopigments '''
import pandas as pd
dataset_name, sample_idx = sources.T
sample_idx.astype(int)
fix = np.ones(len(y_data)).astype(np.bool)
old = y_data.copy()
set_idx = np.where(dataset_name == 'Sundar')[0]
dataset = np.loadtxt(data_path.joinpath('Sundar', 'Dataset.csv'), delimiter=',', dtype=str)[sample_idx[set_idx]]
fix[set_idx[dataset == 'ACIX_Krista']] = False
fix[set_idx[dataset == 'ACIX_Moritz']] = False
set_idx = np.where(data_lbl == 'SeaBASS2')[0]
meta = pd.read_csv(data_path.joinpath('SeaBASS2', 'meta.csv')).iloc[sample_idx[set_idx]]
lonlats = meta[['east_longitude', 'west_longitude', 'north_latitude', 'south_latitude']].apply(lambda v: v.apply(lambda v2: v2.split('||')[0]))
# assert(lonlats.apply(lambda v: v.apply(lambda v2: v2.split('::')[0] == 'rrs')).all().all()), lonlats[~lonlats.apply(lambda v: v.apply(lambda v2: v2.split('::')[0] == 'rrs')).all(1)]
lonlats = lonlats.apply(lambda v: pd.to_numeric(v.apply(lambda v2: v2.split('::')[1].replace('[deg]','')), 'coerce'))
lonlats = lonlats[['east_longitude', 'north_latitude']].to_numpy()
# Only needs correction in certain areas, and for smaller chl magnitudes
fix[set_idx[np.logical_and(lonlats[:,0] < -117, lonlats[:,1] > 32)]] = False
fix[y_data[:,0] > 80] = False
print(f'Correcting {fix.sum()} / {len(fix)} samples')
coef = [0.04, 0.776, 0.015, -0.00046, 0.000004]
# coef = [-0.12, 0.9, 0.001]
y_data[fix, slices['chl']] = np.sum(np.array(coef) * y_data[fix, slices['chl']] ** np.arange(len(coef)), 1, keepdims=False)
if debug:
import matplotlib.pyplot as plt
from .plot_utils import add_identity
plt.scatter(old, y_data)
plt.xlabel('Old')
plt.ylabel('New')
plt.xscale('log')
plt.yscale('log')
add_identity(plt.gca(), color='k', ls='--')
plt.xlim((y_data[y_data > 0].min()/10, y_data.max()*10))
plt.ylim((y_data[y_data > 0].min()/10, y_data.max()*10))
plt.show()
return y_data
def assemble_resampled_matchups(sensor,matchup_folder_name,bands):
Rrs_insitu_resampled = pd.read_csv(matchup_folder_name + '/' + sensor + '/Rrs.csv',header=None)
Rrs_insitu_resampled = pd.DataFrame(Rrs_insitu_resampled)
Rrs_insitu_resampled = Rrs_insitu_resampled.values
Rrs_insitu_resampled_wvl = pd.DataFrame(pd.read_csv(matchup_folder_name + '/' + sensor + '/Rrs_wvl.csv',header=None))
Rrs_insitu_resampled_wvl = Rrs_insitu_resampled_wvl.values
Rrs_insitu_resampled_full = Rrs_insitu_resampled[:,return_valid_wavelengths(desired_wavelengths=get_sensor_bands(sensor+'-noNIR_LB'),available_wavelengths=Rrs_insitu_resampled_wvl)]
Rrs_insitu_resampled_full_NIR = Rrs_insitu_resampled[:,return_valid_wavelengths(desired_wavelengths=get_sensor_bands(sensor+'-SimisFull'),available_wavelengths=Rrs_insitu_resampled_wvl)]
valid_wavelength_bool = return_valid_wavelengths(desired_wavelengths=get_sensor_bands(sensor+'-SimisFull'),available_wavelengths=Rrs_insitu_resampled_wvl)
if sum(valid_wavelength_bool) == 0:
Rrs_insitu_resampled_full_NIR = np.empty((len(Rrs_insitu_resampled_full_NIR),len(get_sensor_bands(sensor+'-SimisFull'))))
Rrs_insitu_resampled_full_NIR[:] = np.NaN
print('PRINTING SHAPE OF 0: ', np.shape(Rrs_insitu_resampled_full_NIR))
Rrs_insitu_resampled = Rrs_insitu_resampled[:,return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_insitu_resampled_wvl)]
def recover_values(matchup_folder_name,desired_value):
if desired_value == 'Rrs_retrieved':
desired_product_df = pd.read_csv(matchup_folder_name + '/' + desired_value + '.csv')
else:
desired_product_df = pd.read_csv(matchup_folder_name + '/' + desired_value + '.csv',header=None)
desired_product_df = pd.DataFrame(desired_product_df)
desired_product_values = desired_product_df.values
return desired_product_values
def recover_header(matchup_folder_name,desired_value):
desired_product_df = pd.read_csv(matchup_folder_name + '/' + desired_value + '.csv')
desired_product_df = pd.DataFrame(desired_product_df)
desired_product_columns = desired_product_df.columns
return desired_product_columns
products = ['chl','PC','dataset','insitu_datetime','path','Rrs_retrieved','site_label','plotting_labels','lat','lon']
dict_of_product_vals=dict()
dict_of_product_vals['insitu_Rrs_resampled'] = Rrs_insitu_resampled
dict_of_product_vals['insitu_Rrs_resampled_full'] = Rrs_insitu_resampled_full
dict_of_product_vals['Rrs_insitu_resampled_full_NIR'] = Rrs_insitu_resampled_full_NIR
if return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_insitu_resampled_wvl).any() == False or return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_insitu_resampled_wvl).any() == False:
print(return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_insitu_resampled_wvl))
print('MISSING DESIRED WAVELENGTHS for :',matchup_folder_name)
return False
dict_of_product_vals['insitu_Rrs_resampled_wvl'] = np.transpose(Rrs_insitu_resampled_wvl[return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_insitu_resampled_wvl)])
dict_of_product_vals['insitu_Rrs_resampled_wvl_full'] = np.transpose(Rrs_insitu_resampled_wvl[return_valid_wavelengths(desired_wavelengths=get_sensor_bands(sensor+'-noNIR_LB'),available_wavelengths=Rrs_insitu_resampled_wvl)])
Rrs_retieved_wvl = np.reshape((np.asarray(recover_header(matchup_folder_name,'Rrs_retrieved'))).astype(float).astype(int),(-1,1))
if return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_retieved_wvl).any() == False:
print(return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_retieved_wvl))
print('MISSING DESIRED WAVELENGTHS for Remote matchup:',matchup_folder_name)
return False
dict_of_product_vals['Rrs_retrieved_wvl'] = np.transpose(np.reshape(np.asarray(Rrs_retieved_wvl[return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_retieved_wvl)]),(-1,1)))
dict_of_product_vals['Rrs_retrieved_wvl_full'] = np.transpose(np.reshape(np.asarray(Rrs_retieved_wvl[return_valid_wavelengths(desired_wavelengths=get_sensor_bands(sensor+'-noNIR_LB'),available_wavelengths=Rrs_retieved_wvl)]),(-1,1)))
for current_product in products:
dict_of_product_vals[current_product] = recover_values(matchup_folder_name,current_product)
Rrs_retrieved = dict_of_product_vals['Rrs_retrieved']
dict_of_product_vals['Rrs_retrieved'] = Rrs_retrieved[:,return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_retieved_wvl)]
dict_of_product_vals['Rrs_retrieved_full'] = Rrs_retrieved[:,return_valid_wavelengths(desired_wavelengths=get_sensor_bands(sensor+'-noNIR_LB'),available_wavelengths=Rrs_retieved_wvl)]
return dict_of_product_vals;
def return_valid_wavelengths(desired_wavelengths,available_wavelengths,margin=1):
available_wavelengths = np.asarray(available_wavelengths)
if (np.all([np.abs(available_wavelengths-w).min() <= margin for w in desired_wavelengths])) == False:
print(f'Missing wavelengths: \n{desired_wavelengths} needed,\n{available_wavelengths.flatten()} found')
return np.array([False] * len(available_wavelengths))
if len(available_wavelengths) != len(desired_wavelengths):
valid = np.abs(available_wavelengths - np.atleast_2d(desired_wavelengths)).min(1) < margin
assert(valid.sum() == len(desired_wavelengths)), [available_wavelengths[valid].flatten(), desired_wavelengths]
return valid
return np.array([True] * len(desired_wavelengths))
def get_matchups(sensor):
dict_of_product_vals = dict()
matchup_folder_name = '/home/ryanoshea/MDN_PC/MDN/script_formatted_data/Ryan_data/Data/Matchups_lat_lon/in-situ/'
extension_folders = ['set_0','set_1','set_2','set_3']
Rrs_insitu_resampled = []
bands = get_sensor_bands(sensor)
sensor = sensor.split('-')[0]
for i,extension in enumerate(extension_folders):
if i ==0:
dict_of_product_vals = assemble_resampled_matchups(sensor=sensor,matchup_folder_name=matchup_folder_name+extension,bands=bands)
else:
dict_of_product_vals_current = assemble_resampled_matchups(sensor=sensor,matchup_folder_name=matchup_folder_name+extension,bands=bands)
if dict_of_product_vals_current != False:
for key in dict_of_product_vals.keys():
dict_of_product_vals[key] = np.concatenate((dict_of_product_vals[key],(dict_of_product_vals_current[key])))
return dict_of_product_vals
def get_in_situ_PRISMA(sensor):
matchup_folder_name = '/home/ryanoshea/MDN_PC/MDN/script_formatted_data/Ryan_data/Data/Test_21_PRISMA/Curonia'
Rrs_insitu_resampled = pd.read_csv(matchup_folder_name + '/' + sensor + '/Rrs.csv',header=None)
Rrs_insitu_resampled = pd.DataFrame(Rrs_insitu_resampled)
Rrs_insitu_resampled = Rrs_insitu_resampled.values
Rrs_insitu_resampled_wvl = pd.DataFrame(pd.read_csv(matchup_folder_name + '/' + sensor + '/Rrs_wvl.csv',header=None))
Rrs_insitu_resampled_wvl = Rrs_insitu_resampled_wvl.values
Rrs_insitu_resampled_full = Rrs_insitu_resampled[:,return_valid_wavelengths(desired_wavelengths=get_sensor_bands(sensor),available_wavelengths=Rrs_insitu_resampled_wvl)]
Rrs_insitu_resampled = Rrs_insitu_resampled[:,return_valid_wavelengths(desired_wavelengths=bands,available_wavelengths=Rrs_insitu_resampled_wvl)]
chl = pd.read_csv(matchup_folder_name + '/' + 'chl' + '.csv',header=None)
PC = pd.read_csv(matchup_folder_name + '/' + 'PC' + '.csv',header=None)
return 0
# Define a function that you pass z_data (raw Rrs) and z_w_data (raw Rrs wavelengths) and then applies SAM to sort them. SAM function should apply to individual spectra
def assign_spectra_OWT(z_data,z_w_data):
OWT_assignments=[]
import pickle
#load the end members
with open('OWT_end_members.pkl','rb') as f:
Rrs_wvl_list = pickle.load(f)
loaded_Rrs_list = Rrs_wvl_list[0]
loaded_Rrs_wvl_list = Rrs_wvl_list[1]
for Rrs_counter,Rrs in enumerate(z_data):
wavelengths = z_w_data[Rrs_counter]
OWT_assignments.append( assign_spectrum_OWT(Rrs,wavelengths,loaded_Rrs_list,loaded_Rrs_wvl_list))
return OWT_assignments
def assign_spectrum_OWT(z_data,z_w_data,end_members,end_member_wavelengths):
from spectral import spectral_angles
from scipy.interpolate import interp1d
min_end_member_wavelength = np.min(end_member_wavelengths[0])
max_end_member_wavelength = np.max(end_member_wavelengths[0])
z_w_data_bool_array = [True if wavelength<max_end_member_wavelength and wavelength>min_end_member_wavelength else False for wavelength in z_w_data ]
z_data=z_data[z_w_data_bool_array]
z_w_data=z_w_data[z_w_data_bool_array]
interpolated_member_to_z_w_data_list = []
for end_member_counter,end_member in enumerate(end_members):
interp_function = interp1d(end_member_wavelengths[end_member_counter],end_member,kind='cubic')
interpolated_member_to_z_w_data_list.append(interp_function(z_w_data))
array_of_end_members = np.asarray(interpolated_member_to_z_w_data_list)
z_data_reshaped = z_data.reshape(1,1,-1)
spectral_angles_available = spectral_angles(z_data_reshaped,array_of_end_members)
minimum_spectral_angle = np.argmin(spectral_angles_available)
minimum_spectral_angle = minimum_spectral_angle +1
return minimum_spectral_angle
def assign_spectrum_OWT_angle_Rrs(z_data,z_w_data,end_members,end_member_wavelengths):
from spectral import spectral_angles
from scipy.interpolate import interp1d
min_end_member_wavelength = np.min(end_member_wavelengths[0])
max_end_member_wavelength = np.max(end_member_wavelengths[0])
z_w_data_bool_array = [True if wavelength<max_end_member_wavelength and wavelength>min_end_member_wavelength else False for wavelength in z_w_data ]
z_data=z_data[z_w_data_bool_array]
z_w_data=z_w_data[z_w_data_bool_array]
interpolated_member_to_z_w_data_list = []
for end_member_counter,end_member in enumerate(end_members):
interp_function = interp1d(end_member_wavelengths[end_member_counter],end_member,kind='cubic')
interpolated_member_to_z_w_data_list.append(interp_function(z_w_data))
array_of_end_members = np.asarray(interpolated_member_to_z_w_data_list)
z_data_reshaped = z_data.reshape(1,1,-1)
spectral_angles_available = spectral_angles(z_data_reshaped,array_of_end_members)
minimum_spectral_angle = np.argmin(spectral_angles_available)
minimum_spectral_angle_value = spectral_angles_available[0][0][minimum_spectral_angle]
nearest_member_Rrs = interpolated_member_to_z_w_data_list[minimum_spectral_angle]
minimum_spectral_angle = minimum_spectral_angle +1
return minimum_spectral_angle, minimum_spectral_angle_value, nearest_member_Rrs,z_w_data
def assign_spectra_OWT_angle_nearest_Rrs(z_data,z_w_data):
OWT_assignments=[]
nearest_member_Rrs = []
min_OWT_angle = []
member_w_list = []
import pickle