powerspectrum
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0447279fcc
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1 changed files with 99 additions and 68 deletions
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@ -5,72 +5,73 @@ kmax = 2e0
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Nk = 50
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AliasingCorr=False
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def get_power_spectrum(field, kmin=kmin, kmax=kmax, Nk=Nk):
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def get_power_spectrum(field, kmin=kmin, kmax=kmax, Nk=Nk, G=None):
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from pysbmy.power import PowerSpectrum
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from pysbmy.fft import FourierGrid
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from pysbmy.correlations import get_autocorrelation
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G = FourierGrid(
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field.L0,
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field.L1,
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field.L2,
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field.N0,
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field.N1,
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field.N2,
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k_modes=np.concat([PowerSpectrum(field.L0,field.L1,field.L2,field.N0,field.N1,field.N2,).FourierGrid.k_modes[:10],np.logspace(
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np.log10(kmin),
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np.log10(kmax),
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Nk,
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)]),
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kmax=kmax,
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)
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if G is None:
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G = FourierGrid(
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field.L0,
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field.L1,
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field.L2,
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field.N0,
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field.N1,
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field.N2,
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k_modes=np.concat([PowerSpectrum(field.L0,field.L1,field.L2,field.N0,field.N1,field.N2,).FourierGrid.k_modes[:10],np.logspace(
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np.log10(kmin),
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np.log10(kmax),
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Nk,
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)]),
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kmax=kmax,
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)
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k = G.k_modes[1:]
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Pk, _ = get_autocorrelation(field, G, AliasingCorr)
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Pk = Pk[1:]
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return k, Pk
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return G, k, Pk
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def get_cross_correlations(field_A, field_B, kmin=kmin, kmax=kmax, Nk=Nk):
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def get_cross_correlations(field_A, field_B, kmin=kmin, kmax=kmax, Nk=Nk, G=None):
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from pysbmy.power import PowerSpectrum
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from pysbmy.fft import FourierGrid
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from pysbmy.correlations import get_crosscorrelation
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G = FourierGrid(
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field_A.L0,
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field_A.L1,
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field_A.L2,
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field_A.N0,
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field_A.N1,
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field_A.N2,
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k_modes=np.concat([PowerSpectrum(field_A.L0,field_A.L1,field_A.L2,field_A.N0,field_A.N1,field_A.N2,).FourierGrid.k_modes[:10],np.logspace(
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np.log10(kmin),
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np.log10(kmax),
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Nk,
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)]),
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kmax=kmax,
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)
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if G is None:
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G = FourierGrid(
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field_A.L0,
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field_A.L1,
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field_A.L2,
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field_A.N0,
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field_A.N1,
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field_A.N2,
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k_modes=np.concat([PowerSpectrum(field_A.L0,field_A.L1,field_A.L2,field_A.N0,field_A.N1,field_A.N2,).FourierGrid.k_modes[:10],np.logspace(
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np.log10(kmin),
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np.log10(kmax),
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Nk,
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)]),
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kmax=kmax,
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)
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k = G.k_modes[1:]
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_, _, Rks, _ = get_crosscorrelation(field_A, field_B, G, AliasingCorr)
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Rks = Rks[1:]
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return k, Rks
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return G, k, Rks
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def add_power_spectrum_to_plot(ax, field, Pk_ref=None, plot_args={}, power_args={}):
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k, Pk = get_power_spectrum(field, **power_args)
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def add_power_spectrum_to_plot(ax, field, Pk_ref=None, G=None, plot_args={}, power_args={}):
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G, k, Pk = get_power_spectrum(field, G=G, **power_args)
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if Pk_ref is not None:
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ax.plot(k, Pk/Pk_ref-1, **plot_args)
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ax.plot(k, Pk/Pk_ref, **plot_args)
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else:
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ax.plot(k, Pk, **plot_args)
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return ax
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return ax, G, k, Pk
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def add_cross_correlations_to_plot(ax, field_A, field_B, plot_args={}, power_args={}):
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k, Rks = get_cross_correlations(field_A, field_B, **power_args)
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def add_cross_correlations_to_plot(ax, field_A, field_B, G=None, plot_args={}, power_args={}):
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G, k, Rks = get_cross_correlations(field_A, field_B, G=G, **power_args)
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ax.plot(k, Rks, **plot_args)
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return ax
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return ax, G, k, Rks
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def plot_power_spectra(filenames,
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@ -79,6 +80,7 @@ def plot_power_spectra(filenames,
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linestyles=None,
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markers=None,
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Pk_ref=None,
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G=None,
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ylims=[0.9,1.1],
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yticks = np.linspace(0.9,1.1,11),
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bound1=0.01,
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@ -108,9 +110,10 @@ def plot_power_spectra(filenames,
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for i, filename in enumerate(filenames):
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field = read_field(filename)
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add_power_spectrum_to_plot(ax=ax,
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_, G, k, _ = add_power_spectrum_to_plot(ax=ax,
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field=field,
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Pk_ref=Pk_ref,
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G=G,
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plot_args=dict(label=labels[i],
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color=colors[i],
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linestyle=linestyles[i],
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@ -120,15 +123,15 @@ def plot_power_spectra(filenames,
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Nk=Nk),
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)
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ax.set_xscale('log')
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ax.set_xlim(kmin, kmax)
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ax.set_xlim(k.min(),k[-2])
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if ylims is not None:
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ax.set_ylim(ylims)
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if yticks is not None:
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ax.set_yticks(yticks)
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ax.set_xlabel(r'$k$ [$h/\mathrm{Mpc}$]')
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ax.set_xlabel(r'$k$ [$h/\mathrm{Mpc}$]', labelpad=-10)
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if Pk_ref is not None:
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ax.set_ylabel(r'$P(k)/P_\mathrm{ref}(k)-1$')
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ax.set_ylabel(r'$P(k)/P_\mathrm{ref}(k)$')
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else:
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ax.set_ylabel('$P(k)$')
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@ -145,6 +148,7 @@ def plot_power_spectra(filenames,
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def plot_cross_correlations(filenames_A,
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filename_B,
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G=None,
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labels=None,
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colors=None,
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linestyles=None,
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@ -180,9 +184,10 @@ def plot_cross_correlations(filenames_A,
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for i, filename_A in enumerate(filenames_A):
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field_A = read_field(filename_A)
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add_cross_correlations_to_plot(ax=ax,
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_, G, k, _ = add_cross_correlations_to_plot(ax=ax,
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field_A=field_A,
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field_B=field_B,
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G=G,
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plot_args=dict(label=labels[i],
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color=colors[i],
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linestyle=linestyles[i],
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@ -192,12 +197,12 @@ def plot_cross_correlations(filenames_A,
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Nk=Nk),
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)
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ax.set_xscale('log')
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ax.set_xlim(kmin, kmax)
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ax.set_xlim(k.min(), k[-2])
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if ylims is not None:
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ax.set_ylim(ylims)
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if yticks is not None:
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ax.set_yticks(yticks)
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ax.set_xlabel(r'$k$ [$h/\mathrm{Mpc}$]')
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ax.set_xlabel(r'$k$ [$h/\mathrm{Mpc}$]', labelpad=-10)
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ax.set_ylabel('$R(k)$')
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if bound1 is not None:
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@ -211,6 +216,28 @@ def plot_cross_correlations(filenames_A,
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return fig, ax
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def get_ylims_and_yticks(ylims):
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if ylims[0] == ylims[1]:
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ylims = None
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yticks = None
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else:
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diff_ylims = ylims[1] - ylims[0]
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factor = 1
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while diff_ylims<5.:
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diff_ylims *= 10
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factor *= 10
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if diff_ylims<12.:
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yticks = np.linspace(int(ylims[0]*factor)/factor,int(factor*ylims[1])/factor, int(diff_ylims)+1)
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else:
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while diff_ylims>12.:
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diff_ylims /= 2.
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factor /= 2.
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yticks = np.linspace(int(ylims[0]*factor)/factor,int(factor*ylims[1])/factor, int(diff_ylims)+1)
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return ylims, yticks
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if __name__ == "__main__":
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from argparse import ArgumentParser
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parser = ArgumentParser(description='Plot power spectra of fields')
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@ -226,6 +253,8 @@ if __name__ == "__main__":
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parser.add_argument('-ls', '--linestyles', type=str, nargs='+', default=None, help='Linestyles for each field.')
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parser.add_argument('-m', '--markers', type=str, nargs='+', default=None, help='Markers for each field.')
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parser.add_argument('-t','--title', type=str, default=None, help='Title of the plot.')
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parser.add_argument('-yrp', '--ylim_power', type=float, nargs=2, default=[0.9,1.1], help='Y-axis limits.')
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parser.add_argument('-yrc', '--ylim_corr', type=float, nargs=2, default=[0.99,1.001], help='Y-axis limits.')
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args = parser.parse_args()
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@ -235,12 +264,18 @@ if __name__ == "__main__":
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if args.reference is not None:
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from pysbmy.field import read_field
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Pk_ref = get_power_spectrum(read_field(args.directory+args.reference), kmin=kmin, kmax=kmax, Nk=Nk)[1]
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G, _, Pk_ref = get_power_spectrum(read_field(args.directory+args.reference), kmin=kmin, kmax=kmax, Nk=Nk)
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else:
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Pk_ref = None
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G = None
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filenames = [args.directory+f for f in args.filenames]
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ylims_power, yticks_power = get_ylims_and_yticks(args.ylim_power)
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ylims_corr, yticks_corr = get_ylims_and_yticks(args.ylim_corr)
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if args.power_spectrum and args.cross_correlation:
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import matplotlib.pyplot as plt
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fig, axes = plt.subplots(2, 1, figsize=(8,8))
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@ -250,14 +285,11 @@ if __name__ == "__main__":
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linestyles=args.linestyles,
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markers=args.markers,
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Pk_ref=Pk_ref,
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# ylims=[0.9,1.1],
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# yticks = np.linspace(0.9,1.1,11),
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# bound1=0.01,
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# bound2=0.02,
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ylims=None,
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yticks = None,
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bound1=None,
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bound2=None,
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G=G,
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ylims=ylims_power,
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yticks = yticks_power,
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bound1=0.01,
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bound2=0.02,
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kmin=kmin,
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kmax=kmax,
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Nk=Nk,
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@ -266,18 +298,15 @@ if __name__ == "__main__":
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plot_cross_correlations(filenames_A=filenames,
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filename_B=args.directory+args.reference,
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G=G,
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labels=args.labels,
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colors=args.colors,
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linestyles=args.linestyles,
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markers=args.markers,
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# ylims=[0.99, 1.001],
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# yticks = np.linspace(0.99,1.001,12),
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# bound1=0.001,
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# bound2=0.002,
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ylims=None,
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yticks = None,
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bound1=None,
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bound2=None,
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ylims=ylims_corr,
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yticks = yticks_corr,
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bound1=0.001,
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bound2=0.002,
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kmin=kmin,
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kmax=kmax,
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Nk=Nk,
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@ -298,8 +327,9 @@ if __name__ == "__main__":
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linestyles=args.linestyles,
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markers=args.markers,
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Pk_ref=Pk_ref,
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ylims=[0.9,1.1],
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yticks = np.linspace(0.9,1.1,11),
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G=G,
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ylims=ylims_power,
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yticks = yticks_power,
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bound1=0.01,
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bound2=0.02,
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kmin=kmin,
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@ -312,12 +342,13 @@ if __name__ == "__main__":
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elif args.cross_correlation:
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fig, ax = plot_cross_correlations(filenames_A=filenames,
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filename_B=args.reference,
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G=G,
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labels=args.labels,
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colors=args.colors,
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linestyles=args.linestyles,
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markers=args.markers,
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ylims=[0.99, 1.001],
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yticks = np.linspace(0.99,1.001,12),
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ylims=ylims_corr,
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yticks = yticks_corr,
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bound1=0.001,
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bound2=0.002,
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kmin=kmin,
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