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dataloader.py
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from matplotlib import pyplot as plt
import numpy as np
import os
from os.path import isdir, isfile
import pandas as pd
from numpy.random import default_rng
from pathlib import Path
import scipy.ndimage as ndimage
import torch
import lampe
import zuko
import torchvision.transforms as T
import torchvision.transforms.functional as F
from torch.utils.data import WeightedRandomSampler, BatchSampler
from itertools import islice
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
import h5py
import numpy as np
from bisect import bisect
from numpy import ndarray as Array
from pathlib import Path
from torch import Tensor, Size
from torch.distributions import Distribution
from torch.utils.data import DataLoader, Dataset, IterableDataset
from tqdm import tqdm
from typing import *
from corner import corner
from sklearn.preprocessing import PowerTransformer
import dill
from uncertainties import unumpy
from lampe.data import H5Dataset, JointDataset
class PosteriorDataset(Dataset):
def __init__(
self,
file,
batch_size: int = None,
sample_size: int = 64,
shuffle: bool = False,
):
super().__init__()
self.file = file
self.batch_size = batch_size
self.sample_size = sample_size
self.shuffle = shuffle
self.pairs = []
self.names = None
self.valid_traces = None
self.traces = None
self.weights = None
self.traces_samples = None
self.fm_theta = None
self.x = None
self.n_trace_pairs = None
self.batched_wg_rand_sampler = torch.vmap(self.wg_rand_samp, in_dims=0, out_dims=0, randomness="different")
def __len__(self) -> int:
return len(self.pairs)
# def __getitem__(self, i: Union[int, slice]) -> Tuple[str, Tensor, Tensor, Tensor]:
# sample_idx = WeightedRandomSampler(self.weights[i], self.sample_size, replacement=False)
# return self.names[i], self.traces[i][sample_idx], self.fm_theta[i], self.x[i]
#
# def __iter__(self) -> Iterator[Tuple[str, Tensor, Tensor, Tensor]]:
# if self.shuffle:
# order = torch.randperm(len(self))
#
# if self.batch_size is None:
# return (self[i] for i in order)
# else:
# return (self[i] for i in order.split(self.batch_size))
# else:
# order = torch.arange(len(self))
# if self.batch_size is None:
# return (self[i] for i in order)
# else:
# return (self[i] for i in order.split(self.batch_size))
def __getitem__(self, i: Union[int, slice]) -> Tuple[str, bool, Tensor, Tensor, Tensor, Tensor]:
return self.pairs[i]
def __iter__(self) -> Iterator[Tuple[str, bool, Tensor, Tensor, Tensor, Tensor]]:
if self.shuffle:
order = torch.randperm(len(self))
if self.batch_size is None:
return (self[i] for i in order)
else:
return (self[i] for i in order.split(self.batch_size))
else:
order = torch.arange(len(self))
if self.batch_size is None:
return (self[i] for i in order)
else:
return (self[i] for i in order.split(self.batch_size))
# TODO: all below improve using BatchSampler
# https://pytorch.org/docs/stable/_modules/torch/utils/data/sampler.html#BatchSampler
def __get_theta_x__(self, i: Union[int, slice]) -> Tuple[Tensor, Tensor]:
return self.fm_theta[i], self.x[i]
def __iter_theta_x__(self) -> Iterator[Tuple[Tensor, Tensor]]:
if self.shuffle:
order = torch.randperm(len(self))
if self.batch_size is None:
return (self.__get_theta_x__(i) for i in order)
else:
return (self.__get_theta_x__(i) for i in order.split(self.batch_size)[:-1])
else:
order = torch.arange(len(self))
if self.batch_size is None:
return (self.__get_theta_x__(i) for i in order)
else:
return (self.__get_theta_x__(i) for i in order.split(self.batch_size)[:-1])
def wg_rand_samp(self, weights):
return torch.tensor(WeightedRandomSampler(weights, num_samples=self.sample_size, replacement=False))
# def __get_trace_x__(self, i):
# print(i)
# print(self.weights[i].shape)
# sample_idx = self.batched_wg_rand_sampler(self.weights[i])
# return self.traces[i][sample_idx], self.x[i]
# def __get_trace_x__(self, i: Union[int, slice]) -> Tuple[Tensor, Tensor]:
# sample_idx = list(WeightedRandomSampler(self.weights[i], (self.sample_size), replacement=False)
# return self.traces[i][sample_idx], self.x[i]
def __get_trace_x__(self, i: Union[int, slice]) -> Tuple[Tensor, Tensor]:
sample_idx = torch.tensor([list(WeightedRandomSampler(weights, self.sample_size, replacement=False))
for weights in self.weights[i]]).cuda()
traces = torch.gather(self.traces[i], 1, sample_idx[..., None].tile((1, 1, 7))) # JFC, but otherwise I get CUDA errors
return traces, self._x[i]
def __iter_trace_x__(self) -> Iterator[Tuple[Tensor, Tensor]]:
if self.shuffle:
order = torch.randperm(self.n_trace_pairs)
if self.batch_size is None:
return (self.__get_trace_x__(i) for i in order)
else:
return (self.__get_trace_x__(i) for i in order.split(self.batch_size)[:-1])
else:
order = torch.arange(len(self))
if self.batch_size is None:
return (self.__get_trace_x__(i) for i in order)
else:
return (self.__get_trace_x__(i) for i in order.split(self.batch_size)[:-1])
def __get_ttrace_x__(self, _i: Union[int, slice]) -> Tuple[Tensor, Tensor]:
# print(_i)
i = _i[:, 0].flatten()
it = _i[:, 1].flatten()
# print(i.shape)
# print(it.shape)
sample_idx = torch.tensor([list(WeightedRandomSampler(weights, self.sample_size, replacement=False))
for weights in self.weights[i]]).cuda()
traces = torch.gather(self.traces[i], 1, sample_idx[..., None].tile((1, 1, 7))).view(-1, 7) # JFC, but otherwise I get CUDA errors
# traces = torch.gather(self.traces[i], 1, sample_idx[..., None].tile((1, 1, 7))) # JFC, but otherwise I get CUDA errors
# traces = traces.mean(dim=1)
theta, x = self.__get_theta_x__(it)
# print("theta x", theta.shape, x.shape)
# print("trace x", traces.shape, self._x[i].tile(self.sample_size, 1, 1).shape)
ttrace = torch.cat((traces, theta))
tx = torch.cat((self._x[i].tile(self.sample_size, 1, 1), x)) # is samples are not collapsed
# tx = torch.cat((self._x[i], x)) # if samples are collapsed
# print(ttrace.shape, tx.shape)
return ttrace, tx
def __iter_ttrace_x__(self) -> Iterator[Tuple[Tensor, Tensor]]:
split_batch_size = int(self.batch_size / 2)
if self.shuffle:
order = torch.randperm(self.n_trace_pairs)
theta_order = torch.randperm(len(self))[:self.n_trace_pairs]
stack = torch.stack((order, theta_order), dim=1)
# print(stack.shape)
if self.batch_size is None:
return (self.__get_ttrace_x__(i) for i in stack)
else:
return (self.__get_ttrace_x__(i) for i in stack.split(split_batch_size)[:-1])
else:
order = torch.arange(len(self))
theta_order = torch.arange(len(self))[:self.n_trace_pairs]
stack = torch.stack((order, theta_order), dim=1)
if self.batch_size is None:
return (self.__get_ttrace_x__(i) for i in stack)
else:
return (self.__get_ttrace_x__(i) for i in stack.split(split_batch_size)[:-1])
def __get_id_theta_trace_x__(self, _i: Union[int, slice]) -> Tuple[Tensor, Tensor]:
# print(_i)
i = _i[:, 0].flatten()
it = _i[:, 1].flatten()
# print(i.shape)
# print(it.shape)
sample_idx = torch.tensor([list(WeightedRandomSampler(weights, self.sample_size, replacement=False))
for weights in self.weights[i]]).cuda()
traces = torch.gather(self.traces[i], 1, sample_idx[..., None].tile((1, 1, 7))).view(-1, 7) # JFC, but otherwise I get CUDA errors
# traces = torch.gather(self.traces[i], 1, sample_idx[..., None].tile((1, 1, 7))) # JFC, but otherwise I get CUDA errors
# traces = traces.mean(dim=1)
theta, x = self.__get_theta_x__(it)
# print("theta x", theta.shape, x.shape)
# print("trace x", traces.shape, self._x[i].shape)
ttrace = torch.cat((traces, theta)).cuda()
# print("ttrace", ttrace.shape)
# tx = torch.cat((self._x[i].tile(self.sample_size, 1, 1), x)) # is samples are not collapsed
tx = torch.cat((self._x[i], x)).cuda() # if samples are collapsed
# print("tx", tx.shape)
# print(ttrace.shape, tx.shape)
return ttrace, tx
def __iter_id_theta_trace_x__(self) -> Iterator[Tuple[Tensor, Tensor]]:
# print("iter start:")
# print(self.fm_theta.device)
# print(self.x.device)
# print(self.traces.device)
# print(self.weights.device)
# print(self._fm_theta.device)
# print(self._x.device)
# print("==========")
split_batch_size = int(self.batch_size / 2)
if self.shuffle:
order = torch.randperm(self.n_trace_pairs)
theta_order = torch.randperm(len(self))[:self.n_trace_pairs]
stack = torch.stack((order, theta_order), dim=1)
# print(stack.shape)
if self.batch_size is None:
return (self.__get_id_theta_trace_x__(i) for i in stack)
else:
return (self.__get_id_theta_trace_x__(i) for i in stack.split(split_batch_size)[:-1])
else:
order = torch.arange(len(self))
theta_order = torch.arange(len(self))[:self.n_trace_pairs]
stack = torch.stack((order, theta_order), dim=1)
if self.batch_size is None:
return (self.__get_id_theta_trace_x__(i) for i in stack)
else:
return (self.__get_id_theta_trace_x__(i) for i in stack.split(split_batch_size)[:-1])
def __get_trace_theta_x__(self, i: Union[int, slice]) -> Tuple[Tensor, Tensor, Tensor]:
sample_idx = torch.tensor([list(WeightedRandomSampler(weights, self.sample_size, replacement=False))
for weights in self.weights[i]]).cuda()
traces = torch.gather(self.traces[i], 1, sample_idx[..., None].tile((1, 1, 7))) # JFC, but otherwise I get CUDA errors
return traces, self._fm_theta[i], self._x[i]
def __iter_trace_theta_x__(self) -> Iterator[Tuple[Tensor, Tensor, Tensor]]:
if self.shuffle:
order = torch.randperm(self.n_trace_pairs)
if self.batch_size is None:
return (self.__get_trace_theta_x__(i) for i in order)
else:
return (self.__get_trace_theta_x__(i) for i in order.split(self.batch_size)[:-1])
else:
order = torch.arange(len(self))
if self.batch_size is None:
return (self.__get_trace_theta_x__(i) for i in order)
else:
return (self.__get_trace_theta_x__(i) for i in order.split(self.batch_size)[:-1])
def _load_to_memory(self,):
self.file = h5py.File(Path(self.file), mode='r')
self.batch_size = self.batch_size
self.shuffle = self.shuffle
keys = self.file.keys()
self.names = keys
self.pairs = []
for name in tqdm(keys, total=len(keys), unit=' pairs from h5 file'):
self.pairs.append((
name,
bool(self.file[name]["valid_trace"][()]),
torch.tensor(self.file[name]["trace"][()], device=device),
torch.tensor(self.file[name]["weights"][()], device=device),
torch.tensor(self.file[name]["fm_theta"][()], device=device),
torch.tensor(self.file[name]["x"][()], device=device)
))
self.valid_traces = torch.tensor([
p[1] for p in self.pairs
], device=device)
self.traces_samples = torch.tensor([
len(p[3]) if p[1] else 0
for p in self.pairs
], device=device)
def load_contiguous_theta_x(self):
if self.names is None:
self._load_to_memory()
n_pairs = len(self.traces_samples)
ndim = len(self.pairs[0][4])
x_shape = self.pairs[0][5].shape
self.fm_theta = torch.empty(size=(n_pairs, ndim), dtype=torch.float, device=device)
self.x = torch.empty(size=(n_pairs, *x_shape), dtype=torch.float, device=device)
for i, n_samples in tqdm(enumerate(self.traces_samples),
total=len(self.pairs), unit=' theta-x pairs to contiguous memory'):
self.fm_theta[i] = self.pairs[i][4]
self.x[i] = self.pairs[i][5]
def load_contiguous_trace_x(self):
if self.names is None:
self._load_to_memory()
valid_idxs = torch.argwhere(self.valid_traces)
max_sample_size = torch.min(self.traces_samples[valid_idxs])
# print(torch.quantile(self.traces_samples[valid_idxs].to(torch.double),
# q=torch.tensor([0.01, 0.04, 0.5, 0.96, 0.99],
# dtype=torch.double, device=device)))
# print(f"Smallest number of available samples: {max_sample_size}")
assert self.sample_size <= max_sample_size, f"The samples to be drawn in each trace must be less than the " \
f"smallest number of available samples. The tensors are padded with zeros.\n" \
f"Smallest number of available samples: {max_sample_size}"
max_samples = 2048
self.n_trace_pairs = torch.sum(self.valid_traces)
self.names = self.file.keys()
ndim = len(self.pairs[0][4])
x_shape = self.pairs[0][5].shape
self.traces = torch.zeros(size=(self.n_trace_pairs, max_samples, ndim), dtype=torch.float, device=device)
self.weights = torch.zeros(size=(self.n_trace_pairs, max_samples), dtype=torch.float, device=device)
self._fm_theta = torch.empty(size=(self.n_trace_pairs, ndim), dtype=torch.float, device=device)
self._x = torch.empty(size=(self.n_trace_pairs, *x_shape), dtype=torch.float, device=device)
for i, (idx) in tqdm(enumerate(valid_idxs),
total=len(valid_idxs), unit=' trace-x pairs to contiguous memory'):
pair = self.pairs[idx]
n_samples = self.traces_samples[idx]
trace_n_samples = torch.minimum(torch.tensor(max_samples), n_samples)
sample_idx = list(WeightedRandomSampler(pair[3], int(trace_n_samples), replacement=False))
self.traces[i, :trace_n_samples] = pair[2][sample_idx]
self.weights[i, :trace_n_samples] = pair[3][sample_idx]
self._fm_theta[i] = pair[4]
self._x[i] = pair[5]
def to_memory(self):
self._load_to_memory()
self.load_contiguous_theta_x()
self.load_contiguous_trace_x()
@staticmethod
def store(
pairs: List[Tuple[str, bool, Array, Array, Array, Array]],
file: Union[str, Path],
overwrite: bool = False,
dtype: np.dtype = np.float32,
**meta,
) -> None:
# Pairs
pair_iter = iter(pairs)
# File
file = Path(file)
file.parent.mkdir(parents=True, exist_ok=True)
with h5py.File(file, 'w' if overwrite else 'w-') as f:
## Attributes
f.attrs.update(meta)
## Store
for i in tqdm(range(len(pairs)), total=len(pairs), unit='pair'):
## Datasets
name, valid_trace, trace, weights, fm_theta, x = next(pair_iter)
grp = f.create_group(str(name))
grp.create_dataset(name="valid_trace", data=valid_trace, dtype=bool)
grp.create_dataset(name="trace", data=trace, dtype=dtype)
grp.create_dataset(name="weights", data=weights, dtype=dtype)
grp.create_dataset(name="fm_theta", data=fm_theta, dtype=dtype)
grp.create_dataset(name="x", data=x, dtype=dtype)
print(f"\nFinished saving data file: {str(file.name)}\n")
def rescale_output(input, forward=False):
input = torch.tensor(input)
device = str(input.device)
input = input.clone().detach().cuda()
scale = torch.tensor([
1., # planet_radius
1.e-3, # planet_temp
1., # log_H2O
1., # log_CO2
1., # log_CO
1., # log_CH4
1., # log_NH3
], device="cuda")
shift = torch.tensor([
- 0.5, # planet_radius
- 1000., # planet_temp
+ 6., # log_H2O
+ 7., # log_CO2
+ 5., # log_CO
+ 6., # log_CH4
+ 7. # log_NH3
], device="cuda")
out = None
if forward:
out = (input + shift) * scale
elif ~forward:
out = (input / scale) - shift
else:
raise NotImplementedError
if "cuda" not in device:
out.cpu()
return out.cpu().numpy()
def rescale_aux_input(df):
df['star_distance'] = np.log10(df['star_distance']) - 3.
df['star_mass_kg'] = np.log10(df['star_mass_kg']) - 30.3
df['star_radius_m'] = np.log10(df['star_radius_m']) - 8.9
df['star_temperature'] = np.log(df['star_temperature']) - 8.6
df['planet_mass_kg'] = np.log10(df['planet_mass_kg']) - 26.1
df['planet_orbital_period'] = np.log10(df['planet_orbital_period']) - 1.
df['planet_distance'] = np.log10(df['planet_distance']) + 1
df['planet_surface_gravity'] = np.log10(df['planet_surface_gravity']) - 1.
return df
def rescale_fm_theta_input(df):
df['planet_radius'] = df['planet_radius'] - 0.5
df['planet_temp'] = (df['planet_temp'] - 1000.) / 1000
df['log_H2O'] = df['log_H2O'] + 6.
df['log_CO2'] = df['log_CO2'] + 7.
df['log_CO'] = df['log_CO'] + 5.
df['log_CH4'] = df['log_CH4'] + 6.
df['log_NH3'] = df['log_NH3'] + 7.
return df
def zca_whitening_matrix(X):
"""
Function to compute ZCA whitening matrix (aka Mahalanobis whitening).
INPUT: X: [M x N] matrix.
Rows: Variables
Columns: Observations
OUTPUT: ZCAMatrix: [M x M] matrix
"""
# Covariance matrix [column-wise variables]: Sigma = (X-mu)' * (X-mu) / N
sigma = np.cov(X, rowvar=True) # [M x M]
# Singular Value Decomposition. X = U * np.diag(S) * V
U,S,V = np.linalg.svd(sigma)
# U: [M x M] eigenvectors of sigma.
# S: [M x 1] eigenvalues of sigma.
# V: [M x M] transpose of U
# Whitening constant: prevents division by zero
epsilon = 1e-5
# ZCA Whitening matrix: U * Lambda * U'
ZCAMatrix = np.dot(U, np.dot(np.diag(1.0/np.sqrt(S + epsilon)), U.T)) # [M x M]
return ZCAMatrix
def zca_whiten(X):
ZCAMatrix = zca_whitening_matrix(X)
return np.dot(ZCAMatrix, X)
def plot_random_spectra(spectra, noise):
fig, ax = plt.subplots(1, 1, constrained_layout=True, figsize=(12, 6))
wlgrid = np.array([0.55, 0.7, 0.95, 1.156375, 1.27490344,
1.40558104, 1.5496531, 1.70849254, 1.88361302, 1.9695975,
2.00918641, 2.04957106, 2.09076743, 2.13279186, 2.17566098,
2.21939176, 2.26400154, 2.30950797, 2.35592908, 2.40328325,
2.45158925, 2.50086619, 2.5511336, 2.60241139, 2.65471985,
2.70807972, 2.76251213, 2.81803862, 2.8746812, 2.93246229,
2.99140478, 3.05153202, 3.11286781, 3.17543645, 3.23926272,
3.30437191, 3.37078978, 3.43854266, 3.50765736, 3.57816128,
3.65008232, 3.72344897, 4.03216667, 4.30545796, 4.59727234,
4.90886524, 5.24157722, 5.59683967, 5.97618103, 6.3812333,
6.81373911, 7.2755592])
rng = np.random.default_rng()
for i in rng.integers(0, len(spectra), 5):
ax.errorbar(wlgrid,
spectra[i], # - np.mean(mean_spectrum),
yerr=noise[i],
fmt="D", markersize=4, alpha=0.9)
ax.set_xlabel(r"$\lambda$ [$\mu m$]")
ax.set_ylabel(r"Transit Depth [-]")
ax.set_title(f"Mean Spectrum")
plt.show()
def make_full_pairs(spec_data, aux_data, trace_data, FM_parameters, weight_threshold=1e-8):
# prepare aux
aux_data_names = list(['star_distance', 'star_mass_kg', 'star_radius_m', 'star_temperature', 'planet_mass_kg',
'planet_orbital_period', 'planet_distance', 'planet_surface_gravity'])
theta_names = list(['planet_radius', 'planet_temp', 'log_H2O', 'log_CO2', 'log_CO', 'log_CH4', 'log_NH3'])
aux_arr = np.zeros((len(FM_parameters), 52))
aux_arr[:, :len(aux_data_names)] = aux_data[aux_data_names].to_numpy()
# prepare spectra
spec_matrix = to_matrix(spec_data)
ind_means = np.mean(spec_matrix[:, :, 1], axis=1).reshape((-1, 1))
mean_stds = (ind_means - 0.00825) / 0.01165
ind_mean_spectra = spec_matrix[:, :, 1] - ind_means
scale_factor = 3.
noises = spec_matrix[:, :, 2].reshape(-1, 52)
scaled_noise = np.tanh(np.sqrt(np.square(noises - np.mean(noises, axis=1).reshape((-1, 1))) / (0.0001285 ** 2)))
pkl_file = "/home/lwelzel/Documents/git/maldcope/data/TrainingData/training_power_transformer.pkl"
# with open(pkl_file, "wb+") as f:
# pt = PowerTransformer(method="yeo-johnson")
# tspectra = (pt.fit_transform(ind_mean_spectra.T).T) / scale_factor
# dill.dump(pt, f)
with open(pkl_file, "rb") as f:
pt = dill.load(f)
tspectra = (pt.transform(ind_mean_spectra.T).T) / scale_factor
plot_random_spectra(tspectra, scaled_noise)
planet_list = spec_data.keys()
pairs = []
valid_traces = []
for i, p in tqdm(enumerate(planet_list), total=len(planet_list), unit=' pairs constructed'):
arr_id = int(p.replace("Planet_train", "").replace("Planet_test", "")) - 1
csv_name = p.replace("Planet_", "")
true_spectrum = spec_data[p]['instrument_spectrum'][:]
spectrum = tspectra[arr_id]
noise = scaled_noise[arr_id]
aux_arr = np.zeros(52)
aux_arr[:len(aux_data_names)] = aux_data[aux_data_names].loc[csv_name].to_numpy()
aux_arr[len(aux_data_names)] = (np.mean(true_spectrum) - 0.00825) / 0.01165
trace = trace_data[p]['tracedata'][()]
weights = trace_data[p]['weights'][()]
valid_trace = ~np.any(np.isnan(weights))
if valid_trace:
thr = weights > weight_threshold
trace = rescale_output(trace[thr], forward=True)
weights = weights[thr]
fm_theta = FM_parameters[theta_names].loc[csv_name].to_numpy().astype(float).flatten()
x = np.array([
spectrum,
noise,
aux_arr
], dtype=float)
pair = (
p,
valid_trace,
trace,
weights,
fm_theta,
x
)
valid_traces.append(valid_trace)
pairs.append(pair)
return pairs, valid_traces
def make_full_test_pairs(spec_data, aux_data, trace_data, FM_parameters, weight_threshold=1e-8):
# prepare aux
aux_data_names = list(['star_distance', 'star_mass_kg', 'star_radius_m', 'star_temperature', 'planet_mass_kg',
'planet_orbital_period', 'planet_distance', 'planet_surface_gravity'])
theta_names = list(['planet_radius', 'planet_temp', 'log_H2O', 'log_CO2', 'log_CO', 'log_CH4', 'log_NH3'])
aux_arr = np.zeros((len(FM_parameters), 52))
aux_arr[:, :len(aux_data_names)] = aux_data[aux_data_names].to_numpy()
# prepare spectra
spec_matrix = to_matrix(spec_data, id_name="Planet_test")
ind_means = np.mean(spec_matrix[:, :, 1], axis=1).reshape((-1, 1))
mean_stds = (ind_means - 0.00825) / 0.01165
ind_mean_spectra = spec_matrix[:, :, 1] - ind_means
scale_factor = 3.
noises = spec_matrix[:, :, 2].reshape(-1, 52)
scaled_noise = np.tanh(np.sqrt(np.square(noises - np.mean(noises, axis=1).reshape((-1, 1))) / (0.0001285 ** 2)))
pkl_file = "/home/lwelzel/Documents/git/maldcope/data/TrainingData/training_power_transformer.pkl"
# with open(pkl_file, "wb+") as f:
# pt = PowerTransformer(method="yeo-johnson")
# tspectra = (pt.fit_transform(ind_mean_spectra.T).T) / scale_factor
# dill.dump(pt, f)
with open(pkl_file, "rb") as f:
pt = dill.load(f)
tspectra = (pt.transform(ind_mean_spectra.T).T) / scale_factor
plot_random_spectra(tspectra, scaled_noise)
planet_list = spec_data.keys()
pairs = []
valid_traces = []
for i, p in tqdm(enumerate(planet_list), total=len(planet_list), unit=' pairs constructed'):
arr_id = int(p.replace("Planet_train", "").replace("Planet_test", "")) - 1
csv_name = p.replace("Planet_", "")
true_spectrum = spec_data[p]['instrument_spectrum'][:]
spectrum = tspectra[arr_id]
noise = scaled_noise[arr_id]
aux_arr = np.zeros(52)
aux_arr[:len(aux_data_names)] = aux_data[aux_data_names].loc[csv_name].to_numpy()
aux_arr[len(aux_data_names)] = (np.mean(true_spectrum) - 0.00825) / 0.01165
x = np.array([
spectrum,
noise,
aux_arr
], dtype=float)
pair = (
p,
True,
np.nan,
np.nan,
np.nan,
x
)
valid_traces.append(True)
pairs.append(pair)
return pairs, valid_traces
def split_datasets(valid_traces, train_f=0.90, val_f=0.08,
training_path=None):
if training_path is None:
training_path = Path("/home/lwelzel/Documents/git/maldcope/data/TrainingData")
try:
valid_traces = np.array(valid_traces)
except BaseException:
valid_traces = valid_traces.cpu().numpy()
total_samples = len(valid_traces)
n_theta_samples = np.sum(~valid_traces)
n_trace_samples = np.sum(valid_traces)
train_n_theta = int(n_theta_samples * train_f)
val_n_theta = int(n_theta_samples * val_f)
test_n_theta = int(n_theta_samples - train_n_theta - val_n_theta)
train_n_trace = int(n_trace_samples * train_f)
val_n_trace = int(n_trace_samples * val_f)
test_n_trace = int(n_trace_samples - train_n_trace - val_n_trace)
print(f"Split:\n"
f"\t Training: \n"
f"\t\t Theta: {train_n_theta:05} ({(train_n_theta / total_samples) * 100.:03.1f} % of total, {(train_n_theta / n_theta_samples) * 100.:03.1f} % of theta)\n"
f"\t\t Traces: {train_n_trace:05} ({(train_n_trace / total_samples) * 100.:03.1f} % of total, {(train_n_trace / n_trace_samples) * 100.:03.1f} % of trace)\n"
f"\t Validation: \n"
f"\t\t Theta: {val_n_theta:05} ({(val_n_theta / total_samples) * 100.:03.1f} % of total, {(val_n_theta / n_theta_samples) * 100.:03.1f} % of theta)\n"
f"\t\t Traces: {val_n_trace:05} ({(val_n_trace / total_samples) * 100.:03.1f} % of total, {(val_n_trace / n_trace_samples) * 100.:03.1f} % of trace)\n"
f"\t Testing: \n"
f"\t\t Theta: {test_n_theta:05} ({(test_n_theta / total_samples) * 100.:03.1f} % of total, {(test_n_theta / n_theta_samples) * 100.:03.1f} % of theta)\n"
f"\t\t Traces: {test_n_trace:05} ({(test_n_trace / total_samples) * 100.:03.1f} % of total, {(test_n_trace / n_trace_samples) * 100.:03.1f} % of trace)\n")
idxs_thetas = np.argwhere(~valid_traces).flatten()
idxs_traces = np.argwhere(valid_traces).flatten()
rng = default_rng()
rng.shuffle(idxs_thetas)
rng.shuffle(idxs_traces)
train_idx = list(idxs_thetas[:train_n_theta]) \
+ list(idxs_traces[:train_n_trace])
val_idx = list(idxs_thetas[train_n_theta:train_n_theta + val_n_theta]) \
+ list(idxs_traces[train_n_trace:train_n_trace + val_n_trace])
test_idx = list(idxs_thetas[train_n_theta + val_n_theta:]) \
+ list(idxs_traces[train_n_trace + val_n_trace:])
np.save(str(training_path / f'train_idx.npy'), train_idx)
np.save(str(training_path / f'val_idx.npy'), val_idx)
np.save(str(training_path / f'test_idx.npy'), test_idx)
return train_idx, val_idx, test_idx
def write_full_data():
training_path = Path("/home/lwelzel/Documents/git/maldcope/data/TrainingData/")
# X DATA
spec_data = h5py.File(training_path / 'SpectralData.hdf5')
aux_data = pd.read_csv(training_path / "AuxillaryTable.csv",
index_col=0)
aux_data = rescale_aux_input(aux_data)
# Y DATA - THETA
trace_data = h5py.File(training_path / 'Ground Truth Package/Tracedata.hdf5')
FM_parameters = pd.read_csv(training_path / "Ground Truth Package/FM_Parameter_Table.csv",
index_col=1)
trace_data = trace_data # NOT RESCALED!
FM_parameters = rescale_fm_theta_input(FM_parameters)
# Pairs
pairs, valid_traces = make_full_pairs(spec_data, aux_data, trace_data, FM_parameters)
# TRAIN - VAL - TEST SPLIT
train_idx, val_idx, test_idx = split_datasets(valid_traces)
# TRAINING_DATA
filename = training_path / f'training_dataset_full.h5'
PosteriorDataset.store(
pairs=[pairs[i] for i in train_idx],
file=filename,
overwrite=True,
)
# VALIDATION_DATA
filename = training_path / f'validation_dataset_full.h5'
PosteriorDataset.store(
pairs=[pairs[i] for i in val_idx],
file=filename,
overwrite=True,
)
# TEST_DATA
filename = training_path / f'testing_dataset_full.h5'
PosteriorDataset.store(
pairs=[pairs[i] for i in test_idx],
file=filename,
overwrite=True,
)
def write_full_test_data():
training_path = Path("/home/lwelzel/Documents/git/maldcope/data/TrainingData/")
# X DATA
spec_data = h5py.File(Path("/home/lwelzel/Documents/git/maldcope/data/TestData") / 'SpectralData.hdf5')
aux_data = pd.read_csv(Path("/home/lwelzel/Documents/git/maldcope/data/TestData") / "AuxillaryTable.csv",
index_col=0)
aux_data = rescale_aux_input(aux_data)
# Y DATA - THETA
trace_data = h5py.File(training_path / 'Ground Truth Package/Tracedata.hdf5')
FM_parameters = pd.read_csv(training_path / "Ground Truth Package/FM_Parameter_Table.csv",
index_col=1)
trace_data = trace_data # NOT RESCALED!
FM_parameters = rescale_fm_theta_input(FM_parameters)
# Pairs
pairs, valid_traces = make_full_test_pairs(spec_data, aux_data, trace_data, FM_parameters)
# TRAIN - VAL - TEST SPLIT
# train_idx, val_idx, test_idx = split_datasets(valid_traces)
# TRAINING_DATA
filename = Path("/home/lwelzel/Documents/git/maldcope/data/TestData") / f'testing_dataset.h5'
PosteriorDataset.store(
pairs=pairs,
file=filename,
overwrite=True,
)
def join_paras(df1, df2, l1, l2):
c1, c2 = len(l1), len(l2)
n1, n2 = len(df1), len(df2)
assert n1 == n2
x = np.zeros((n1, c1 + c2))
x[:, :c1] = df1[l1].to_numpy()
x[:, c1:] = df2[l2].to_numpy()
return x, l1 + l2
def to_matrix(SpectralData, id_name="Planet_train"):
# id is in ascending order
num = len(SpectralData.keys())
id_order = np.arange(num)
# we knew the instrument resolution beforehand
observed_spectrum = np.zeros((num, 52, 4))
for idx, x in tqdm(enumerate(id_order), total=num):
current_id = f'{id_name}{x + 1}'
wlgrid = SpectralData[current_id]['instrument_wlgrid'][:]
spectrum = SpectralData[current_id]['instrument_spectrum'][:]
noise = SpectralData[current_id]['instrument_noise'][:]
wlwidth = SpectralData[current_id]['instrument_width'][:]
observed_spectrum[idx, :, :] = np.concatenate(
[wlgrid[..., np.newaxis], spectrum[..., np.newaxis], noise[..., np.newaxis], wlwidth[..., np.newaxis]],
axis=-1)
return observed_spectrum
def make_pairs_base(spec_matrix, FM_parameters, idxs, theta_names, which_norm="median", **kwargs):
ind_mean_spectra = spec_matrix[:, :, 1] - np.mean(spec_matrix[:, :, 1], axis=1).reshape((-1, 1))
ind_mean_noise = np.abs(spec_matrix[:, :, 2] - np.mean(spec_matrix[:, :, 2], axis=1).reshape((-1, 1)))
# mean_spectrum = np.mean(ind_mean_spectra, axis=0)
# median_spectrum = np.median(ind_mean_spectra, axis=0)
#
# mean_noise = np.mean(spec_matrix[:, :, 2], axis=0)
# median_noise = np.median(spec_matrix[:, :, 2], axis=0)
if which_norm == "mean":
norm_spectrum = np.mean(ind_mean_spectra, axis=0)
norm_noise = np.mean(ind_mean_noise, axis=0)
elif which_norm == "median":
norm_spectrum = np.median(ind_mean_spectra, axis=0)
norm_noise = np.median(ind_mean_noise, axis=0)
else:
raise NotImplementedError
if isinstance(FM_parameters, pd.DataFrame):
pairs = [
(FM_parameters.iloc[i][theta_names].to_numpy().astype(float).reshape((1, -1)),
np.array([
ind_mean_spectra[i] - norm_spectrum,
np.abs(ind_mean_noise[i] - norm_noise)
],
dtype=float).reshape((1, 2, -1)))
for i in idxs
]
elif isinstance(FM_parameters, np.ndarray):
pairs = [
(FM_parameters.reshape((1, -1)),
np.array([
ind_mean_spectra[i] - norm_spectrum,
np.abs(ind_mean_noise[i] - norm_noise)
],
dtype=float).reshape((1, 2, -1)))
for i in idxs
]
else:
raise NotImplementedError
return pairs
def make_pairs_base_aux(spec_matrix, FM_parameters, idxs, theta_names, aux_data, which_norm="median",**kwargs):
ind_mean_spectra = spec_matrix[:, :, 1] - np.mean(spec_matrix[:, :, 1], axis=1).reshape((-1, 1))
ind_mean_noise = np.abs(spec_matrix[:, :, 2] - np.mean(spec_matrix[:, :, 2], axis=1).reshape((-1, 1)))
# mean_spectrum = np.mean(ind_mean_spectra, axis=0)
# median_spectrum = np.median(ind_mean_spectra, axis=0)
#
# mean_noise = np.mean(spec_matrix[:, :, 2], axis=0)
# median_noise = np.median(spec_matrix[:, :, 2], axis=0)
if which_norm == "mean":
norm_spectrum = np.mean(ind_mean_spectra, axis=0)
norm_noise = np.mean(ind_mean_noise, axis=0)
elif which_norm == "median":
norm_spectrum = np.median(ind_mean_spectra, axis=0)
norm_noise = np.median(ind_mean_noise, axis=0)
else:
raise NotImplementedError
aux_data_names = ['star_distance', 'star_mass_kg', 'star_radius_m', 'star_temperature', 'planet_mass_kg',
'planet_orbital_period', 'planet_distance', 'planet_surface_gravity']
aux_arr = np.zeros((len(FM_parameters), 52))
aux_arr[:, :len(aux_data_names)] = aux_data[aux_data_names].to_numpy()
if isinstance(FM_parameters, pd.DataFrame):
pairs = [
(FM_parameters.iloc[i][theta_names].to_numpy().astype(float).reshape((1, -1)),
np.array([
ind_mean_spectra[i] - norm_spectrum,
np.abs(ind_mean_noise[i] - norm_noise),
aux_arr[i]
],
dtype=float).reshape((1, 3, -1)))
for i in idxs
]
elif isinstance(FM_parameters, np.ndarray):
pairs = [
(FM_parameters.reshape((1, -1)),
np.array([
ind_mean_spectra[i] - norm_spectrum,
np.abs(ind_mean_noise[i] - norm_noise),
aux_arr[i]
],
dtype=float).reshape((1, 3, -1)))
for i in idxs
]
else:
raise NotImplementedError
return pairs
def make_pairs_full_norm_aux(spec_matrix, FM_parameters, idxs, theta_names, aux_data, which_norm="median",**kwargs):
ind_mean_spectra = spec_matrix[:, :, 1] - np.mean(spec_matrix[:, :, 1], axis=1).reshape((-1, 1))
std_spectra = np.std(spec_matrix[:, :, 1], axis=0)
ind_mean_noise = np.abs(spec_matrix[:, :, 2] - np.mean(spec_matrix[:, :, 2], axis=1).reshape((-1, 1)))
std_noise = np.std(spec_matrix[:, :, 2], axis=0)
# mean_spectrum = np.mean(ind_mean_spectra, axis=0)
# median_spectrum = np.median(ind_mean_spectra, axis=0)
#
# mean_noise = np.mean(spec_matrix[:, :, 2], axis=0)
# median_noise = np.median(spec_matrix[:, :, 2], axis=0)
if which_norm == "mean":
norm_spectrum = np.mean(ind_mean_spectra, axis=0)
norm_noise = np.mean(ind_mean_noise, axis=0)
elif which_norm == "median":
norm_spectrum = np.median(ind_mean_spectra, axis=0)
norm_noise = np.median(ind_mean_noise, axis=0)
else:
raise NotImplementedError
aux_data_names = ['star_distance', 'star_mass_kg', 'star_radius_m', 'star_temperature', 'planet_mass_kg',
'planet_orbital_period', 'planet_distance', 'planet_surface_gravity']
aux_arr = np.zeros((len(FM_parameters), 52))
aux_arr[:, :len(aux_data_names)] = aux_data[aux_data_names].to_numpy()
if isinstance(FM_parameters, pd.DataFrame):