notebooks/pz_examples.py

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# # $p(z)$ examples

# ## IMPACT OF TEMPS ON CONCRETE P(Z) EXAMPLES

# ### LOAD PYTHON MODULES

# %load_ext autoreload
# %autoreload 2

import pandas as pd
import numpy as np
import os
from astropy.io import fits
from astropy.table import Table
import torch
from pathlib import Path

# matplotlib settings
from matplotlib import rcParams
import matplotlib.pyplot as plt

rcParams["mathtext.fontset"] = "stix"
rcParams["font.family"] = "STIXGeneral"

from temps.archive import Archive
from temps.utils import nmad
from temps.temps_arch import EncoderPhotometry, MeasureZ
from temps.temps import TempsModule


# ### LOAD DATA

# define here the directory containing the photometric catalogues
parent_dir = Path(
    "/data/astro/scratch/lcabayol/insight/data/Euclid_EXT_MER_PHZ_DC2_v1.5"
)
modules_dir = Path("../data/models/")

filename_valid = "euclid_cosmos_DC2_S1_v2.1_valid_matched.fits"
path_file = parent_dir / filename_valid  # Creating the path to the file
hdu_list = fits.open(path_file)
cat = Table(hdu_list[1].data).to_pandas()
cat = cat[cat["FLAG_PHOT"] == 0]
cat = cat[cat["mu_class_L07"] == 1]
cat = cat[(cat["z_spec_S15"] > 0) | (cat["photo_z_L15"] > 0)]
cat = cat[cat["MAG_VIS"] < 25]


ztarget = [
    cat["z_spec_S15"].values[ii]
    if cat["z_spec_S15"].values[ii] > 0
    else cat["photo_z_L15"].values[ii]
    for ii in range(len(cat))
]
specz_or_photo = [
    0 if cat["z_spec_S15"].values[ii] > 0 else 1 for ii in range(len(cat))
]
ID = cat["ID"]
VISmag = cat["MAG_VIS"]
zsflag = cat["reliable_S15"]

photoz_archive = Archive(path=parent_dir, only_zspec=False)
f, ferr = photoz_archive._extract_fluxes(catalogue=cat)
col, colerr = photoz_archive._to_colors(f, ferr)

# ### LOAD TRAINED MODELS AND EVALUATE PDF OF RANDOM EXAMPLES

# The notebook 'Tutorial_temps' gives an example of how to train and save models.

# Initialize an empty dictionary to store DataFrames
ii = np.random.randint(0, len(col), 1)
pz_dict = {}
for il, lab in enumerate(["z", "L15", "DA"]):

    nn_features = EncoderPhotometry()
    nn_features.load_state_dict(
        torch.load(modules_dir / f"modelF_{lab}.pt", map_location=torch.device("cpu"))
    )
    nn_z = MeasureZ(num_gauss=6)
    nn_z.load_state_dict(
        torch.load(modules_dir / f"modelZ_{lab}.pt", map_location=torch.device("cpu"))
    )

    temps_module = TempsModule(nn_features, nn_z)

    z, pz, fodds = temps_module.get_pz(input_data=torch.Tensor(col[ii]), return_pz=True)

    # Assign the DataFrame to a key in the dictionary
    pz_dict[lab] = pz


# +
cmap = plt.get_cmap("Dark2")

plt.plot(np.linspace(0, 5, 1000), pz_dict["z"][0], label="z", color=cmap(0), ls="--")
plt.plot(np.linspace(0, 5, 1000), pz_dict["L15"][0], label="L15", color=cmap(1), ls=":")
plt.plot(
    np.linspace(0, 5, 1000), pz_dict["DA"][0], label="TEMPS", color=cmap(2), ls="-"
)
plt.axvline(x=np.array(ztarget)[ii][0], ls="-.", color="black")
# plt.xlim(0,2)
plt.legend()

plt.xlabel(r"$z$", fontsize=14)
plt.ylabel("Probability", fontsize=14)
# plt.savefig(f'pz_{ii[0]}.pdf', bbox_inches='tight')
# -