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Add galaxy sampling (#88)
* Improve calculations * Improve flags * Add smoothed options * Remove some old comments * Edit little things * Save smoothed * Move files * Edit imports * Edit imports * Renaming imports * Renaming imports * Sort imports * Sort files * Sorting * Optionally make copies of the field * Add quijote backup check * Add direct field smoothing * Shorten stupid documentation * Shorten stupid docs * Update conversion * Add particles to ASCII conversion * Add a short comment * Add SDSS uncorrected distance * Adjust comment * Add FITS index to galaxies * Remove spare space * Remove a stupid line * Remove blank line * Make space separated * Add interpolated field path * Add field sampling * Sort imports * Return density in cells * Clear out observer velocity * Add 170817 sampling * Fix normalization * Update plot
This commit is contained in:
Richard Stiskalek
GitHub
parent
0af925e26a
commit
eccd8e3507
26 changed files with 610 additions and 365 deletions
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@ -38,15 +38,6 @@ except ModuleNotFoundError:
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def open_csiborg(nsim):
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"""
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Open a CSiBORG halo catalogue. Applies mass and distance selection.
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Parameters
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----------
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nsim : int
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Simulation index.
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Returns
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-------
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cat : csiborgtools.read.CSiBORGHaloCatalogue
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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bounds = {"totpartmass": (None, None), "dist": (0, 155)}
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@ -58,17 +49,6 @@ def open_csiborg(nsim):
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def open_quijote(nsim, nobs=None):
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"""
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Open a Quijote halo catalogue. Applies mass and distance selection.
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Parameters
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----------
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nsim : int
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Simulation index.
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nobs : int, optional
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Fiducial observer index.
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Returns
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-------
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cat : csiborgtools.read.QuijoteHaloCatalogue
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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cat = csiborgtools.read.QuijoteHaloCatalogue(
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@ -82,17 +62,6 @@ def open_quijote(nsim, nobs=None):
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def plot_mass_vs_ncells(nsim, pdf=False):
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"""
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Plot the halo mass vs. number of occupied cells in the initial snapshot.
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Parameters
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----------
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nsim : int
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Simulation index.
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pdf : bool, optional
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Whether to save the figure as a PDF file.
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Returns
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-------
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None
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"""
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cat = open_csiborg(nsim)
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mpart = 4.38304044e+09
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@ -123,15 +92,6 @@ def plot_mass_vs_ncells(nsim, pdf=False):
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def plot_hmf(pdf=False):
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"""
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Plot the FoF halo mass function of CSiBORG and Quijote.
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Parameters
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----------
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pdf : bool, optional
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Whether to save the figure as a PDF file.
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Returns
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-------
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None
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"""
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print("Plotting the HMF...", flush=True)
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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@ -189,13 +149,15 @@ def plot_hmf(pdf=False):
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std_csiborg = numpy.std(csiborg_counts, axis=0)
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ax[0].plot(x, mean_csiborg, label="CSiBORG", c=cols[0])
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ax[0].fill_between(x, mean_csiborg - std_csiborg,
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mean_csiborg + std_csiborg, alpha=0.5, color=cols[0])
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mean_csiborg + std_csiborg,
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alpha=0.5, color=cols[0])
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mean_quijote = numpy.mean(quijote_counts, axis=0)
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std_quijote = numpy.std(quijote_counts, axis=0)
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ax[0].plot(x, mean_quijote, label="Quijote", c=cols[1])
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ax[0].fill_between(x, mean_quijote - std_quijote,
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mean_quijote + std_quijote, alpha=0.5, color=cols[1])
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mean_quijote + std_quijote, alpha=0.5,
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color=cols[1])
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ax[0].plot(x, csiborg5511, label="CSiBORG 5511", c="k", ls="--")
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std5511 = numpy.sqrt(csiborg5511)
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@ -207,7 +169,7 @@ def plot_hmf(pdf=False):
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+ (std_quijote / mean_quijote / numpy.log(10))**2)
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ax[1].plot(x, 10**log_y, c=cols[0])
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ax[1].fill_between(x, 10**(log_y - err), 10**(log_y + err), alpha=0.5,
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color=col[0])
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color=cols[0])
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ax[1].plot(x, csiborg5511 / mean_quijote, c="k", ls="--")
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@ -239,11 +201,6 @@ def plot_hmf_quijote_full(pdf=False):
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"""
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Plot the FoF halo mass function of Quijote full run.
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Parameters
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----------
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pdf : bool, optional
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Whether to save the figure as a PDF file.
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Returns
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-------
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None
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@ -305,29 +262,13 @@ def plot_hmf_quijote_full(pdf=False):
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plt.close()
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def load_field(kind, nsim, grid, MAS, in_rsp=False):
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def load_field(kind, nsim, grid, MAS, in_rsp=False, smooth_scale=None):
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r"""
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Load a single field.
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Parameters
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----------
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kind : str
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Field kind.
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nsim : int
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Simulation index.
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grid : int
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Grid size.
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MAS : str
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Mass assignment scheme.
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in_rsp : bool, optional
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Whether to load the field in redshift space.
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Returns
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-------
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field : n-dimensional array
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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return numpy.load(paths.field(kind, MAS, grid, nsim, in_rsp=in_rsp))
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return numpy.load(paths.field(kind, MAS, grid, nsim, in_rsp=in_rsp,
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smooth_scale=smooth_scale))
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###############################################################################
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@ -338,35 +279,8 @@ def load_field(kind, nsim, grid, MAS, in_rsp=False):
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def plot_projected_field(kind, nsim, grid, in_rsp, smooth_scale, MAS="PCS",
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vel_component=0, highres_only=True, slice_find=None,
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pdf=False):
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r"""
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"""
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Plot the mean projected field, however can also plot a single slice.
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Parameters
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----------
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kind : str
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Field kind.
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nsim : int
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Simulation index.
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grid : int
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Grid size.
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in_rsp : bool
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Whether to load the field in redshift space.
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smooth_scale : float
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Smoothing scale in :math:`\mathrm{Mpc} / h`.
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MAS : str, optional
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Mass assignment scheme.
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vel_component : int, optional
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Which velocity field component to plot.
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highres_only : bool, optional
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Whether to only plot the high-resolution region.
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slice_find : float, optional
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Which slice to plot in fractional units (i.e. 1. is the last slice)
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pdf : bool, optional
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Whether to save the figure as a PDF.
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Returns
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-------
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None
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"""
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print(f"Plotting projected field `{kind}`. ", flush=True)
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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@ -383,7 +297,8 @@ def plot_projected_field(kind, nsim, grid, in_rsp, smooth_scale, MAS="PCS",
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field = File(paths.borg_mcmc(nsim), 'r')
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field = field["scalars"]["BORG_final_density"][...]
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else:
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field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=in_rsp)
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field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=in_rsp,
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smooth_scale=smooth_scale)
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if kind == "velocity":
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field = field[vel_component, ...]
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@ -487,7 +402,6 @@ def plot_projected_field(kind, nsim, grid, in_rsp, smooth_scale, MAS="PCS",
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else:
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fig.colorbar(im, cax=cbar_ax, label=clabel)
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fig.tight_layout(h_pad=0, w_pad=0)
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for ext in ["png"] if pdf is False else ["png", "pdf"]:
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fout = join(
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@ -506,20 +420,9 @@ def plot_projected_field(kind, nsim, grid, in_rsp, smooth_scale, MAS="PCS",
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###############################################################################
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def get_sky_label(kind, volume_weight):
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def get_sky_label(kind, volume_weight: bool):
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"""
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Get the sky label for a given field kind.
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Parameters
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----------
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kind : str
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Field kind.
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volume_weight : bool
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Whether to volume weight the field.
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Returns
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-------
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label : str
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"""
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if volume_weight:
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if kind == "density":
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@ -667,14 +570,16 @@ if __name__ == "__main__":
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plot_halos=5e13, volume_weight=True)
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if True:
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kind = "potential"
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kind = "environment"
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grid = 512
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smooth_scale = 0
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smooth_scale = 8.0
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# plot_projected_field("overdensity", 7444, grid, in_rsp=True,
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# highres_only=False)
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# for in_rsp in [True, False]:
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for in_rsp in [True, False]:
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plot_projected_field(kind, 7444, grid, in_rsp=in_rsp,
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# nsims = [7444 + n * 24 for n in range(101)]
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nsim = 7444
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for in_rsp in [False]:
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plot_projected_field(kind, nsim, grid, in_rsp=in_rsp,
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smooth_scale=smooth_scale, slice_find=0.5,
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MAS="PCS", highres_only=True)
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