size=11,
boundary='cut').distance_metric()
scf_val_noncon_bound = scf_distance_cut_bound.scf1.scf_surface
scf_fitlims = SCF(dataset1['cube'], size=11)
scf_fitlims.run(boundary='continuous', xlow=1.5 * u.pix, xhigh=4.5 * u.pix)
scf_slope_wlimits = scf_fitlims.slope
scf_slope_wlimits_2D = scf_fitlims.slope2D
# Cramer Statistic
from turbustat.statistics import Cramer_Distance
cramer_distance = Cramer_Distance(
dataset1["cube"], dataset2["cube"], noise_value1=0.1,
noise_value2=0.1).distance_metric(normalize=False)
cramer_val = cramer_distance.data_matrix1
# Dendrograms
from turbustat.statistics import DendroDistance, Dendrogram_Stats
min_deltas = np.logspace(-1.5, 0.5, 40)
dendro_distance = DendroDistance(dataset1["cube"],
dataset2["cube"],
min_deltas=min_deltas).distance_metric()
dendrogram_val = dendro_distance.dendro1.numfeatures
print(bispec.surface_distance)
print(bispec.mean_distance)
# Cramer Statistic
if run_cramer:
from turbustat.statistics import Cramer_Distance
cube = fits.open(
osjoin(data_path, "Design4_flatrho_0021_00_radmc.fits"))[0]
cube_fid = fits.open(
osjoin(data_path, "Fiducial0_flatrho_0021_00_radmc.fits"))[0]
cramer = Cramer_Distance(cube_fid,
cube,
noise_value1=-np.inf,
noise_value2=-np.inf)
cramer.distance_metric(normalize=True,
n_jobs=1,
verbose=True,
save_name=osjoin(fig_path,
"cramer_distmet.png"))
print(cramer.distance)
# Delta-Variance
if run_delvar:
from turbustat.statistics import DeltaVariance_Distance
moment0 = fits.open(
osjoin(data_path, "Design4_flatrho_0021_00_radmc_moment0.fits"))[0]
def stats_wrapper(dataset1,
dataset2,
fiducial_models=None,
statistics=None,
multicore=False,
vca_break=None,
vcs_break=None,
vcs_regrid=[None, None],
dendro_params=None,
periodic_bounds=[True, True],
noise_value=[-np.inf, -np.inf],
dendro_saves=[None, None],
scf_saves=[None, None],
inertial_range=[[None] * 2, [None] * 2],
spatial_range=[[None] * 2, [None] * 2]):
'''
Function to run all of the statistics on two datasets.
Each statistic is run with set inputs. This function needs to be altered
to change the inputs.
Parameters
----------
dataset1 : dict
Contains the cube and all of its property arrays.
dataset2 : dict
See dataset1
fiducial_models : dict, optional
Models for dataset1. Avoids recomputing when comparing
many sets to dataset1.
statistics : list, optional
List of all of the statistics to use. If None, all are run.
multicore : bool, optional
If the wrapper is being used in parallel, this disables
returning model values for dataset1.
vcs_break : float, optional
Pass an initial guess for the location of the VCS break.
vcs_regrid : list of bools, optional
The simulated cubes lack information on the smallest spectral scales.
When enabled, the cube is downsampled by a factor of 5 spectrally
before running VCS.
dendro_params : dict or list, optional
Provides parameters to use when computing the initial dendrogram.
If different parameters are required for each dataset, the
the input should be a list containing the two dictionaries.
periodic_bounds : list of bools
Set whether the boundaries should be handled as 'continuous' (True) or
not ('cut' or 'fill'; False).
cleanup : bool, optional
Delete distance classes after running.
'''
if statistics is None: # Run them all
statistics = statistics_list
distances = {}
# Calculate the fiducial case and return it for later use
if fiducial_models is None:
fiducial_models = {}
for statistic in statistics:
if "PDF" in statistic:
statistic = "PDF"
elif statistic == "Skewness" or statistic == "Kurtosis":
statistic = "stat_moments"
elif "Dendrogram" in statistic:
statistic = "Dendrogram"
elif "DeltaVariance_Centroid" in statistic:
statistic = "DeltaVariance_Centroid"
elif "DeltaVariance" in statistic and "Centroid" not in statistic:
statistic = "DeltaVariance"
fiducial_models[statistic] = None
if any("Wavelet" in s for s in statistics):
wavelet_distance = \
Wavelet_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models["Wavelet"],
xlow=spatial_range[0],
xhigh=spatial_range[1])
wavelet_distance.distance_metric()
distances["Wavelet"] = wavelet_distance.distance
if not multicore:
fiducial_models["Wavelet"] = copy(wavelet_distance.wt1)
del wavelet_distance
if any("MVC" in s for s in statistics):
mvc_distance = \
MVC_Distance(dataset1, dataset2,
fiducial_model=fiducial_models["MVC"],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
mvc_distance.distance_metric()
distances["MVC"] = mvc_distance.distance
if not multicore:
fiducial_models["MVC"] = copy(mvc_distance.mvc1)
del mvc_distance
if any("PSpec" in s for s in statistics):
pspec_distance = \
PSpec_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['PSpec'],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
pspec_distance.distance_metric()
distances["PSpec"] = pspec_distance.distance
if not multicore:
fiducial_models["PSpec"] = copy(pspec_distance.pspec1)
del pspec_distance
if any("Bispectrum" in s for s in statistics):
bispec_distance = \
BiSpectrum_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Bispectrum'])
bispec_distance.distance_metric()
distances["Bispectrum"] = bispec_distance.distance
if not multicore:
fiducial_models["Bispectrum"] = copy(bispec_distance.bispec1)
del bispec_distance
if any("DeltaVariance_Slope" in s for s in statistics) or \
any("DeltaVariance_Curve" in s for s in statistics):
# Check for how boundaries should be handled.
boundary1 = 'wrap' if periodic_bounds[0] else 'fill'
boundary2 = 'wrap' if periodic_bounds[1] else 'fill'
delvar_distance = \
DeltaVariance_Distance(dataset1["moment0"],
dataset2["moment0"],
weights1=dataset1["moment0_error"][0]**-2,
weights2=dataset2["moment0_error"][0]**-2,
fiducial_model=fiducial_models["DeltaVariance"],
xlow=spatial_range[0],
xhigh=spatial_range[1],
boundary=[boundary1, boundary2])
delvar_distance.distance_metric()
distances["DeltaVariance_Curve"] = delvar_distance.curve_distance
distances["DeltaVariance_Slope"] = delvar_distance.slope_distance
if not multicore:
fiducial_models["DeltaVariance"] = copy(delvar_distance.delvar1)
del delvar_distance
if any("DeltaVariance_Centroid_Slope" in s for s in statistics) or \
any("DeltaVariance_Centroid_Curve" in s for s in statistics):
# Check for how boundaries should be handled.
boundary1 = 'wrap' if periodic_bounds[0] else 'fill'
boundary2 = 'wrap' if periodic_bounds[1] else 'fill'
delvar_distance = \
DeltaVariance_Distance(dataset1["centroid"],
dataset2["centroid"],
weights1=dataset1["centroid_error"][0]**-2,
weights2=dataset2["centroid_error"][0]**-2,
fiducial_model=fiducial_models["DeltaVariance_Centroid"],
xlow=spatial_range[0],
xhigh=spatial_range[1],
boundary=[boundary1, boundary2])
delvar_distance.distance_metric()
distances["DeltaVariance_Centroid_Curve"] = \
delvar_distance.curve_distance
distances["DeltaVariance_Centroid_Slope"] = \
delvar_distance.slope_distance
if not multicore:
fiducial_models["DeltaVariance_Centroid"] = \
copy(delvar_distance.delvar1)
del delvar_distance
if any("Genus" in s for s in statistics):
genus_distance = \
GenusDistance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Genus'])
genus_distance.distance_metric()
distances["Genus"] = genus_distance.distance
if not multicore:
fiducial_models["Genus"] = copy(genus_distance.genus1)
del genus_distance
if any("VCS" in s for s in statistics):
# Regrid the cube to lower spectral resolution
if any(vcs_regrid):
from spectral_cube import SpectralCube
import astropy.io.fits as fits
from analysis_funcs import spectral_regrid_cube
if vcs_regrid[0] is not None:
cube1_hdu = fits.PrimaryHDU(dataset1["cube"][0],
header=dataset1["cube"][1])
cube1 = SpectralCube.read(cube1_hdu)
cube1 = spectral_regrid_cube(cube1, int(vcs_regrid[0]))
else:
cube1 = dataset1["cube"]
if vcs_regrid[1] is not None:
cube2_hdu = fits.PrimaryHDU(dataset2["cube"][0],
header=dataset2["cube"][1])
cube2 = SpectralCube.read(cube2_hdu)
cube2 = spectral_regrid_cube(cube2, int(vcs_regrid[1]))
else:
cube2 = dataset2["cube"]
else:
cube1 = dataset1["cube"]
cube2 = dataset2["cube"]
vcs_distance = VCS_Distance(cube1,
cube2,
breaks=vcs_break,
fiducial_model=fiducial_models['VCS'])
vcs_distance.distance_metric()
distances["VCS"] = vcs_distance.distance
distances["VCS_Small_Scale"] = vcs_distance.small_scale_distance
distances["VCS_Large_Scale"] = vcs_distance.large_scale_distance
distances["VCS_Break"] = vcs_distance.break_distance
if not multicore:
fiducial_models["VCS"] = copy(vcs_distance.vcs1)
del vcs_distance
if any("VCA" in s for s in statistics):
vca_distance = VCA_Distance(dataset1["cube"],
dataset2["cube"],
breaks=vca_break,
fiducial_model=fiducial_models['VCA'],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
vca_distance.distance_metric()
distances["VCA"] = vca_distance.distance
if not multicore:
fiducial_models["VCA"] = copy(vca_distance.vca1)
del vca_distance
if any("Tsallis" in s for s in statistics):
tsallis_distance = \
Tsallis_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Tsallis'])
tsallis_distance.distance_metric()
distances["Tsallis"] = tsallis_distance.distance
if not multicore:
fiducial_models["Tsallis"] = copy(tsallis_distance.tsallis1)
del tsallis_distance
if any("Skewness" in s for s in statistics) or\
any("Kurtosis" in s for s in statistics):
moment_distance = \
StatMoments_Distance(dataset1["moment0"],
dataset2["moment0"], radius=5,
weights1=dataset1["moment0_error"][0]**-2,
weights2=dataset2["moment0_error"][0]**-2,
fiducial_model=fiducial_models['stat_moments'])
moment_distance.distance_metric()
distances["Skewness"] = moment_distance.skewness_distance
distances["Kurtosis"] = moment_distance.kurtosis_distance
if not multicore:
fiducial_models["stat_moments"] = \
copy(moment_distance.moments1)
del moment_distance
if any("PCA" in s for s in statistics):
pca_distance = \
PCA_Distance(dataset1["cube"],
dataset2["cube"],
fiducial_model=fiducial_models['PCA'])
pca_distance.distance_metric()
distances["PCA"] = pca_distance.distance
if not multicore:
fiducial_models["PCA"] = pca_distance.pca1
del pca_distance
if any("SCF" in s for s in statistics):
# Switch the inputs such that the save file is the "fiducial"
# or first cube input below
# if scf_saves[0] is not None:
# fid_model = SCF.load_results(scf_saves[0])
# cube1 = dataset1["cube"]
# cube2 = dataset2["cube"]
# boundary1 = "continuous" if periodic_bounds[0] else 'cut'
# boundary2 = "continuous" if periodic_bounds[1] else 'cut'
# if scf_saves[1] is not None:
# fid_model = SCF.load_results(scf_saves[1])
# cube2 = dataset1["cube"]
# cube1 = dataset2["cube"]
# boundary1 = "continuous" if periodic_bounds[1] else 'cut'
# boundary2 = "continuous" if periodic_bounds[0] else 'cut'
boundary1 = "continuous" if periodic_bounds[0] else 'cut'
boundary2 = "continuous" if periodic_bounds[1] else 'cut'
scf_distance = \
SCF_Distance(cube1, cube2,
boundary=[boundary1, boundary2],
fiducial_model=fiducial_models["SCF"])
# fiducial_model=fid_model)
scf_distance.distance_metric()
distances["SCF"] = scf_distance.distance
if not multicore:
fiducial_models["SCF"] = copy(scf_distance.scf1)
del scf_distance
if any("Cramer" in s for s in statistics):
cramer_distance = \
Cramer_Distance(dataset1["cube"],
dataset2["cube"],
noise_value1=noise_value[0],
noise_value2=noise_value[1]).distance_metric()
distances["Cramer"] = cramer_distance.distance
del cramer_distance
if any("Dendrogram_Hist" in s for s in statistics) or \
any("Dendrogram_Num" in s for s in statistics):
if dendro_saves[0] is None:
input1 = dataset1["cube"]
elif isinstance(dendro_saves[0], str):
input1 = dendro_saves[0]
else:
raise UserWarning("dendro_saves must be the filename of the"
" saved file.")
if dendro_saves[1] is None:
input2 = dataset2["cube"]
elif isinstance(dendro_saves[1], str):
input2 = dendro_saves[1]
else:
raise UserWarning("dendro_saves must be the filename of the"
" saved file.")
dendro_distance = \
DendroDistance(input1, input2,
dendro_params=dendro_params,
fiducial_model=fiducial_models['Dendrogram'],
periodic_bounds=periodic_bounds,
min_features=40)
dendro_distance.distance_metric()
distances["Dendrogram_Hist"] = dendro_distance.histogram_distance
distances["Dendrogram_Num"] = dendro_distance.num_distance
if not multicore:
fiducial_models["Dendrogram"] = copy(dendro_distance.dendro1)
del dendro_distance
if any("PDF_Hellinger" in s for s in statistics) or \
any("PDF_KS" in s for s in statistics) or \
any("PDF_Lognormal" in s for s in statistics): # or \
# any("PDF_AD" in s for s in statistics):
pdf_distance = \
PDF_Distance(dataset1["moment0"],
dataset2["moment0"],
min_val1=2 * noise_value[0],
min_val2=2 * noise_value[1])
pdf_distance.distance_metric()
distances["PDF_Hellinger"] = pdf_distance.hellinger_distance
distances["PDF_KS"] = pdf_distance.ks_distance
distances["PDF_Lognormal"] = pdf_distance.lognormal_distance
# distances["PDF_AD"] = pdf_distance.ad_distance
if not multicore:
fiducial_models["PDF"] = copy(pdf_distance.PDF1)
del pdf_distance
if multicore:
return distances
else:
return distances, fiducial_models
stat_kwargs={'nsamples': 10000})
bispec.distance_metric(verbose=True,
save_name=osjoin(fig_path, "bispectrum_distmet.png"))
print(bispec.surface_distance)
print(bispec.mean_distance)
# Cramer Statistic
if run_cramer:
from turbustat.statistics import Cramer_Distance
cube = fits.open(osjoin(data_path, "Design4_flatrho_0021_00_radmc.fits"))[0]
cube_fid = fits.open(osjoin(data_path, "Fiducial0_flatrho_0021_00_radmc.fits"))[0]
cramer = Cramer_Distance(cube_fid, cube, noise_value1=-np.inf,
noise_value2=-np.inf)
cramer.distance_metric(normalize=True, n_jobs=1, verbose=True,
save_name=osjoin(fig_path, "cramer_distmet.png"))
print(cramer.distance)
# Delta-Variance
if run_delvar:
from turbustat.statistics import DeltaVariance_Distance
moment0 = fits.open(osjoin(data_path, "Design4_flatrho_0021_00_radmc_moment0.fits"))[0]
moment0_err = fits.open(osjoin(data_path, "Design4_flatrho_0021_00_radmc_moment0.fits"))[1]
moment0_fid = fits.open(osjoin(data_path, "Fiducial0_flatrho_0021_00_radmc_moment0.fits"))[0]
moment0_fid_err = fits.open(osjoin(data_path, "Fiducial0_flatrho_0021_00_radmc_moment0.fits"))[1]
dataset2["cube"], size=11).distance_metric()
scf_val = scf_distance.scf1.scf_surface
scf_spectrum = scf_distance.scf1.scf_spectrum
scf_slope = scf_distance.scf1.slope
# Now run the SCF when the boundaries aren't continuous
scf_distance_cut_bound = SCF_Distance(dataset1["cube"],
dataset2["cube"], size=11,
boundary='cut').distance_metric()
scf_val_noncon_bound = scf_distance_cut_bound.scf1.scf_surface
# Cramer Statistic
from turbustat.statistics import Cramer_Distance
cramer_distance = Cramer_Distance(dataset1["cube"],
dataset2["cube"]).distance_metric()
cramer_val = cramer_distance.data_matrix1
# Dendrograms
from turbustat.statistics import DendroDistance
min_deltas = np.logspace(-1.5, 0.5, 40)
dendro_distance = DendroDistance(dataset1["cube"],
dataset2["cube"],
min_deltas=min_deltas).distance_metric()
dendrogram_val = dendro_distance.dendro1.numfeatures
def generate_unitvals():
import numpy as np
import astropy.units as u
# The machine producing these values should have emcee installed!
try:
import emcee
except ImportError:
raise ImportError("Install emcee to generate unit test data.")
from turbustat.tests._testing_data import dataset1, dataset2
# Wavelet Transform
from turbustat.statistics import Wavelet_Distance, Wavelet
wavelet_distance = \
Wavelet_Distance(dataset1["moment0"],
dataset2["moment0"]).distance_metric()
wavelet_val = wavelet_distance.wt1.values
wavelet_slope = wavelet_distance.wt1.slope
# Wavelet with a break
wave_break = Wavelet(dataset1['moment0']).run(xhigh=7 * u.pix,
brk=5.5 * u.pix)
wavelet_slope_wbrk = wave_break.slope
wavelet_brk_wbrk = wave_break.brk.value
# MVC
from turbustat.statistics import MVC_Distance, MVC
mvc_distance = MVC_Distance(dataset1, dataset2).distance_metric()
mvc = MVC(dataset1["centroid"], dataset1["moment0"], dataset1["linewidth"])
mvc.run()
mvc_val = mvc.ps1D
mvc_slope = mvc.slope
mvc_slope2D = mvc.slope2D
# Spatial Power Spectrum/ Bispectrum
from turbustat.statistics import (PSpec_Distance, Bispectrum_Distance,
Bispectrum, PowerSpectrum)
pspec_distance = \
PSpec_Distance(dataset1["moment0"],
dataset2["moment0"]).distance_metric()
pspec = PowerSpectrum(dataset1['moment0'])
pspec.run()
pspec_val = pspec.ps1D
pspec_slope = pspec.slope
pspec_slope2D = pspec.slope2D
bispec_distance = \
Bispectrum_Distance(dataset1["moment0"],
dataset2["moment0"]).distance_metric()
bispec_val = bispec_distance.bispec1.bicoherence
azimuthal_slice = bispec_distance.bispec1.azimuthal_slice(
16, 10, value='bispectrum_logamp', bin_width=5 * u.deg)
bispec_azim_bins = azimuthal_slice[16][0]
bispec_azim_vals = azimuthal_slice[16][1]
bispec_azim_stds = azimuthal_slice[16][2]
bispec_meansub = Bispectrum(dataset1['moment0'])
bispec_meansub.run(mean_subtract=True)
bispec_val_meansub = bispec_meansub.bicoherence
# Genus
from turbustat.statistics import GenusDistance, Genus
smooth_scales = np.linspace(1.0, 0.1 * min(dataset1["moment0"][0].shape),
5)
genus_distance = \
GenusDistance(dataset1["moment0"],
dataset2["moment0"],
lowdens_percent=20,
genus_kwargs=dict(match_kernel=True)).distance_metric()
# The distance method requires standardizing the data. Make a
# separate version that isn't
genus = Genus(dataset1['moment0'], smoothing_radii=smooth_scales)
genus.run(match_kernel=True)
genus_val = genus.genus_stats
# Delta-Variance
from turbustat.statistics import DeltaVariance_Distance, DeltaVariance
delvar_distance = \
DeltaVariance_Distance(dataset1["moment0"],
dataset2["moment0"],
weights1=dataset1["moment0_error"][0],
weights2=dataset2["moment0_error"][0],
delvar_kwargs=dict(xhigh=11 * u.pix))
delvar_distance.distance_metric()
delvar = DeltaVariance(dataset1["moment0"],
weights=dataset1['moment0_error'][0]).run(xhigh=11 *
u.pix)
delvar_val = delvar.delta_var
delvar_slope = delvar.slope
# Test with a break point
delvar_wbrk = \
DeltaVariance(dataset1["moment0"],
weights=dataset1['moment0_error'][0]).run(xhigh=11 * u.pix,
brk=6 * u.pix)
delvar_slope_wbrk = delvar_wbrk.slope
delvar_brk = delvar_wbrk.brk.value
# Change boundary conditions
delvar_fill = \
DeltaVariance(dataset1["moment0"],
weights=dataset1['moment0_error'][0]).run(xhigh=11 * u.pix,
boundary='fill',
nan_treatment='interpolate')
delvar_fill_val = delvar_fill.delta_var
delvar_fill_slope = delvar_fill.slope
# VCA/VCS
from turbustat.statistics import VCA_Distance, VCS_Distance, VCA
vcs_distance = VCS_Distance(dataset1["cube"],
dataset2["cube"],
fit_kwargs=dict(high_cut=0.3 / u.pix,
low_cut=3e-2 / u.pix))
vcs_distance.distance_metric()
vcs_val = vcs_distance.vcs1.ps1D
vcs_slopes = vcs_distance.vcs1.slope
vca_distance = VCA_Distance(dataset1["cube"],
dataset2["cube"]).distance_metric()
vca = VCA(dataset1['cube'])
vca.run()
vca_val = vca.ps1D
vca_slope = vca.slope
vca_slope2D = vca.slope2D
# Tsallis
from turbustat.statistics import Tsallis
tsallis_kwargs = {"sigma_clip": 5, "num_bins": 100}
tsallis = Tsallis(dataset1['moment0'], lags=[1, 2, 4, 8, 16] * u.pix)
tsallis.run(periodic=True, **tsallis_kwargs)
tsallis_val = tsallis.tsallis_params
tsallis_stderrs = tsallis.tsallis_stderrs
tsallis_noper = Tsallis(dataset1['moment0'], lags=[1, 2, 4, 8, 16] * u.pix)
tsallis_noper.run(periodic=False, num_bins=100)
tsallis_val_noper = tsallis_noper.tsallis_params
# High-order stats
from turbustat.statistics import StatMoments_Distance, StatMoments
moment_distance = \
StatMoments_Distance(dataset1["moment0"],
dataset2["moment0"]).distance_metric()
kurtosis_val = moment_distance.moments1.kurtosis_hist[1]
skewness_val = moment_distance.moments1.skewness_hist[1]
# Save a few from the non-distance version
tester = StatMoments(dataset1["moment0"])
tester.run()
kurtosis_nondist_val = tester.kurtosis_hist[1]
skewness_nondist_val = tester.skewness_hist[1]
# Non-periodic
tester = StatMoments(dataset1["moment0"])
tester.run(periodic=False)
kurtosis_nonper_val = tester.kurtosis_hist[1]
skewness_nonper_val = tester.skewness_hist[1]
# PCA
from turbustat.statistics import PCA_Distance, PCA
pca_distance = PCA_Distance(dataset1["cube"],
dataset2["cube"]).distance_metric()
pca = PCA(dataset1["cube"], distance=250 * u.pc)
pca.run(mean_sub=True,
eigen_cut_method='proportion',
min_eigval=0.75,
spatial_method='contour',
spectral_method='walk-down',
fit_method='odr',
brunt_beamcorrect=False,
spectral_output_unit=u.m / u.s)
pca_val = pca.eigvals
pca_spectral_widths = pca.spectral_width().value
pca_spatial_widths = pca.spatial_width().value
pca_fit_vals = {
"index": pca.index,
"gamma": pca.gamma,
"intercept": pca.intercept().value,
"sonic_length": pca.sonic_length()[0].value
}
# Now get those values using mcmc
pca.run(mean_sub=True,
eigen_cut_method='proportion',
min_eigval=0.75,
spatial_method='contour',
spectral_method='walk-down',
fit_method='bayes',
brunt_beamcorrect=False,
spectral_output_unit=u.m / u.s)
pca_fit_vals["index_bayes"] = pca.index
pca_fit_vals["gamma_bayes"] = pca.gamma
pca_fit_vals["intercept_bayes"] = pca.intercept().value
pca_fit_vals["sonic_length_bayes"] = pca.sonic_length()[0].value
# Record the number of eigenvalues kept by the auto method
pca.run(mean_sub=True,
n_eigs='auto',
min_eigval=0.001,
eigen_cut_method='value',
decomp_only=True)
pca_fit_vals["n_eigs_value"] = pca.n_eigs
# Now w/ the proportion of variance cut
pca.run(mean_sub=True,
n_eigs='auto',
min_eigval=0.99,
eigen_cut_method='proportion',
decomp_only=True)
pca_fit_vals["n_eigs_proportion"] = pca.n_eigs
# SCF
from turbustat.statistics import SCF_Distance, SCF
scf_distance = SCF_Distance(dataset1["cube"], dataset2["cube"],
size=11).distance_metric()
scf = SCF(dataset1['cube'], size=11).run()
scf_val = scf.scf_surface
scf_spectrum = scf.scf_spectrum
scf_slope = scf.slope
scf_slope2D = scf.slope2D
# Now run the SCF when the boundaries aren't continuous
scf_distance_cut_bound = SCF_Distance(dataset1["cube"],
dataset2["cube"],
size=11,
boundary='cut').distance_metric()
scf_val_noncon_bound = scf_distance_cut_bound.scf1.scf_surface
scf_fitlims = SCF(dataset1['cube'], size=11)
scf_fitlims.run(boundary='continuous', xlow=1.5 * u.pix, xhigh=4.5 * u.pix)
scf_slope_wlimits = scf_fitlims.slope
scf_slope_wlimits_2D = scf_fitlims.slope2D
# Cramer Statistic
from turbustat.statistics import Cramer_Distance
cramer_distance = Cramer_Distance(
dataset1["cube"], dataset2["cube"], noise_value1=0.1,
noise_value2=0.1).distance_metric(normalize=False)
cramer_val = cramer_distance.data_matrix1
# Dendrograms
from turbustat.statistics import Dendrogram_Distance, Dendrogram_Stats
min_deltas = np.logspace(-1.5, 0.5, 40)
dendro_distance = Dendrogram_Distance(
dataset1["cube"], dataset2["cube"],
min_deltas=min_deltas).distance_metric()
dendrogram_val = dendro_distance.dendro1.numfeatures
# With periodic boundaries
dendro = Dendrogram_Stats(dataset1['cube'], min_deltas=min_deltas)
dendro.run(periodic_bounds=True)
dendrogram_periodic_val = dendro.numfeatures
# PDF
from turbustat.statistics import PDF_Distance
pdf_distance = \
PDF_Distance(dataset1["moment0"],
dataset2["moment0"],
min_val1=0.05,
min_val2=0.05,
weights1=dataset1["moment0_error"][0]**-2.,
weights2=dataset2["moment0_error"][0]**-2.,
do_fit=False,
normalization_type='standardize')
pdf_distance.distance_metric()
pdf_val = pdf_distance.PDF1.pdf
pdf_ecdf = pdf_distance.PDF1.ecdf
pdf_bins = pdf_distance.bins
# Do a fitted version of the PDF pca
pdf_fit_distance = \
PDF_Distance(dataset1["moment0"],
dataset2["moment0"],
min_val1=0.05,
min_val2=0.05,
do_fit=True,
normalization_type=None)
pdf_fit_distance.distance_metric()
np.savez_compressed('checkVals',
wavelet_val=wavelet_val,
wavelet_slope=wavelet_slope,
wavelet_slope_wbrk=wavelet_slope_wbrk,
wavelet_brk_wbrk=wavelet_brk_wbrk,
mvc_val=mvc_val,
mvc_slope=mvc_slope,
mvc_slope2D=mvc_slope2D,
pspec_val=pspec_val,
pspec_slope=pspec_slope,
pspec_slope2D=pspec_slope2D,
bispec_val=bispec_val,
bispec_azim_bins=bispec_azim_bins,
bispec_azim_vals=bispec_azim_vals,
bispec_azim_stds=bispec_azim_stds,
bispec_val_meansub=bispec_val_meansub,
genus_val=genus_val,
delvar_val=delvar_val,
delvar_slope=delvar_slope,
delvar_slope_wbrk=delvar_slope_wbrk,
delvar_brk=delvar_brk,
delvar_fill_val=delvar_fill_val,
delvar_fill_slope=delvar_fill_slope,
vcs_val=vcs_val,
vcs_slopes=vcs_slopes,
vca_val=vca_val,
vca_slope=vca_slope,
vca_slope2D=vca_slope2D,
tsallis_val=tsallis_val,
tsallis_stderrs=tsallis_stderrs,
tsallis_val_noper=tsallis_val_noper,
kurtosis_val=kurtosis_val,
skewness_val=skewness_val,
kurtosis_nondist_val=kurtosis_nondist_val,
skewness_nondist_val=skewness_nondist_val,
kurtosis_nonper_val=kurtosis_nonper_val,
skewness_nonper_val=skewness_nonper_val,
pca_val=pca_val,
pca_fit_vals=pca_fit_vals,
pca_spectral_widths=pca_spectral_widths,
pca_spatial_widths=pca_spatial_widths,
scf_val=scf_val,
scf_slope_wlimits=scf_slope_wlimits,
scf_slope_wlimits_2D=scf_slope_wlimits_2D,
scf_val_noncon_bound=scf_val_noncon_bound,
scf_spectrum=scf_spectrum,
scf_slope=scf_slope,
scf_slope2D=scf_slope2D,
cramer_val=cramer_val,
dendrogram_val=dendrogram_val,
dendrogram_periodic_val=dendrogram_periodic_val,
pdf_val=pdf_val,
pdf_bins=pdf_bins,
pdf_ecdf=pdf_ecdf)
np.savez_compressed(
'computed_distances',
mvc_distance=mvc_distance.distance,
pca_distance=pca_distance.distance,
vca_distance=vca_distance.distance,
pspec_distance=pspec_distance.distance,
scf_distance=scf_distance.distance,
wavelet_distance=wavelet_distance.distance,
delvar_curve_distance=delvar_distance.curve_distance,
delvar_slope_distance=delvar_distance.slope_distance,
# tsallis_distance=tsallis_distance.distance,
kurtosis_distance=moment_distance.kurtosis_distance,
skewness_distance=moment_distance.skewness_distance,
cramer_distance=cramer_distance.distance,
genus_distance=genus_distance.distance,
vcs_distance=vcs_distance.distance,
bispec_mean_distance=bispec_distance.mean_distance,
bispec_surface_distance=bispec_distance.surface_distance,
dendrohist_distance=dendro_distance.histogram_distance,
dendronum_distance=dendro_distance.num_distance,
pdf_hellinger_distance=pdf_distance.hellinger_distance,
pdf_ks_distance=pdf_distance.ks_distance,
pdf_lognorm_distance=pdf_fit_distance.lognormal_distance)