csiborgtools/notebooks/fits.ipynb

In [1]:

import numpy as np
import matplotlib.pyplot as plt
from h5py import File
from scipy.stats import spearmanr

import csiborgtools

%matplotlib inline
%load_ext autoreload
%autoreload 2

In [2]:

paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)

# d = np.load(paths.field_interpolated("SDSS", "csiborg2_main", 16817, "density", "SPH", 1024))

In [33]:

survey = csiborgtools.SDSS()(apply_selection=False)
# survey = csiborgtools.SDSSxALFALFA()(apply_selection=False)

In [35]:

for kind in ["main", "random"]:
    x, smooth = csiborgtools.summary.read_interpolated_field(survey, f"csiborg2_{kind}", "density", "SPH", 1024, paths)
    np .savez(f"../data/{survey.name}_{kind}_density_SPH_1024.npz", val=x, smooth_scales=smooth)

Reading fields:   0%|          | 0/20 [00:00<?, ?it/s]Reading fields: 100%|██████████| 20/20 [00:11<00:00,  1.80it/s]
Reading fields: 100%|██████████| 20/20 [00:10<00:00,  1.86it/s]

In [37]:

Out[37]:

(20, 641409, 5)

In [24]:

np.load("../data/SDSS_main_density_SPH_1024.npz")["val"]

Out[24]:

array([[[nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        ...,
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan]],

       [[nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        ...,
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan]],

       [[nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        ...,
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan]],

       ...,

       [[nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        ...,
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan]],

       [[nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        ...,
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan]],

       [[nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        ...,
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan],
        [nan, nan, nan, nan, nan]]], dtype=float32)

In [ ]: