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simplify loading
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parent
e4ec1c4daa
commit
d69012424e
1 changed files with 5 additions and 35 deletions
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@ -19,7 +19,6 @@ Notebook utility functions.
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import numpy
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from os.path import join
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from tqdm import trange
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from astropy.cosmology import FlatLambdaCDM
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try:
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@ -33,40 +32,6 @@ Nsplits = 200
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dumpdir = "/mnt/extraspace/rstiskalek/csiborg/"
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def load_mmain_convert(n):
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srcdir = "/users/hdesmond/Mmain"
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arr = csiborgtools.io.read_mmain(n, srcdir)
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csiborgtools.utils.convert_mass_cols(arr, "mass_cl")
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csiborgtools.utils.convert_position_cols(
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arr, ["peak_x", "peak_y", "peak_z"])
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csiborgtools.utils.flip_cols(arr, "peak_x", "peak_z")
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d, ra, dec = csiborgtools.utils.cartesian_to_radec(arr)
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arr = csiborgtools.utils.add_columns(
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arr, [d, ra, dec], ["dist", "ra", "dec"])
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return arr
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def load_mmains(N=None, verbose=True):
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ids = csiborgtools.io.get_csiborg_ids("/mnt/extraspace/hdesmond")
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N = ids.size if N is None else N
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if N > ids.size:
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raise ValueError("`N` cannot be larger than 101.")
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# If N less than num of CSiBORG, then radomly choose
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if N == ids.size:
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choices = numpy.arange(N)
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else:
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choices = numpy.random.choice(ids.size, N, replace=False)
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out = [None] * N
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iters = trange(N) if verbose else range(N)
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for i in iters:
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j = choices[i]
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out[i] = load_mmain_convert(ids[j])
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return out
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def load_processed(Nsim, Nsnap):
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simpath = csiborgtools.io.get_sim_path(Nsim)
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outfname = join(
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@ -76,6 +41,7 @@ def load_processed(Nsim, Nsnap):
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# Add mmain
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mmain = csiborgtools.io.read_mmain(Nsim, "/mnt/zfsusers/hdesmond/Mmain")
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data = csiborgtools.io.merge_mmain_to_clumps(data, mmain)
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csiborgtools.utils.flip_cols(data, "peak_x", "peak_z")
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# Cut on numbre of particles and finite m200
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data = data[(data["npart"] > 100) & numpy.isfinite(data["m200"])]
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@ -85,6 +51,10 @@ def load_processed(Nsim, Nsnap):
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"r200", "r500", "Rs", "rho0", "peak_x", "peak_y", "peak_z"]
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data = csiborgtools.units.convert_from_boxunits(
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data, convert_cols, boxunits)
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# Now calculate spherical coordinates
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d, ra, dec = csiborgtools.units.cartesian_to_radec(data)
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data = csiborgtools.utils.add_columns(
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data, [d, ra, dec], ["dist", "ra", "dec"])
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return data
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