'''
Wrapper to run all of the statistics between two data sets.
For large-scale comparisons, this is the function that should be called.
'''
import numpy as np
from .wavelets import Wavelet_Distance
from .mvc import MVC_Distance
from .pspec_bispec import PSpec_Distance, BiSpectrum_Distance
from .genus import GenusDistance
from .delta_variance import DeltaVariance_Distance
from .vca_vcs import VCA_Distance, VCS_Distance
from .tsallis import Tsallis_Distance
from .stat_moments import StatMoments_Distance
from .pca import PCA_Distance
from .scf import SCF_Distance
from .cramer import Cramer_Distance
from .dendrograms import DendroDistance
from .pdf import PDF_Distance
from statistics_list import statistics_list
[docs]def stats_wrapper(dataset1, dataset2, fiducial_models=None,
statistics=None, multicore=False, vca_break=None,
vcs_break=None, dendro_params=None,
noise_value=[-np.inf, -np.inf],
dendro_saves=[None, None],
cleanup=True):
'''
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.
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.
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 = {}
if any("Wavelet" in s for s in statistics):
wavelet_distance = \
Wavelet_Distance(dataset1["moment0"],
dataset2["moment0"]).distance_metric()
distances["Wavelet"] = wavelet_distance.distance
if not multicore:
fiducial_models["Wavelet"] = wavelet_distance.wt1
if cleanup:
del wavelet_distance
if any("MVC" in s for s in statistics):
mvc_distance = MVC_Distance(dataset1, dataset2).distance_metric()
distances["MVC"] = mvc_distance.distance
if not multicore:
fiducial_models["MVC"] = mvc_distance.mvc1
if cleanup:
del mvc_distance
if any("PSpec" in s for s in statistics):
pspec_distance = \
PSpec_Distance(dataset1["moment0"],
dataset2["moment0"],
weights1=dataset1["moment0_error"][0]**2.,
weights2=dataset2["moment0_error"][0]**2.).distance_metric()
distances["PSpec"] = pspec_distance.distance
if not multicore:
fiducial_models["PSpec"] = pspec_distance.pspec1
if cleanup:
del pspec_distance
if any("Bispectrum" in s for s in statistics):
bispec_distance = \
BiSpectrum_Distance(dataset1["moment0"],
dataset2["moment0"]).distance_metric()
distances["Bispectrum"] = bispec_distance.distance
if not multicore:
fiducial_models["Bispectrum"] = bispec_distance.bispec1
if cleanup:
del bispec_distance
if any("DeltaVariance" in s for s in statistics):
delvar_distance = \
DeltaVariance_Distance(dataset1["moment0"],
dataset2["moment0"],
weights1=dataset1["moment0_error"][0],
weights2=dataset2["moment0_error"][0]).distance_metric()
distances["DeltaVariance_Curve"] = delvar_distance.curve_distance
distances["DeltaVariance_Slope"] = delvar_distance.slope_distance
if not multicore:
fiducial_models["DeltaVariance"] = delvar_distance.delvar1
if cleanup:
del delvar_distance
if any("Genus" in s for s in statistics):
genus_distance = \
GenusDistance(dataset1["moment0"],
dataset2["moment0"]).distance_metric()
distances["Genus"] = genus_distance.distance
if not multicore:
fiducial_models["Genus"] = genus_distance.genus1
if cleanup:
del genus_distance
if any("VCS" in s for s in statistics):
vcs_distance = VCS_Distance(dataset1["cube"],
dataset2["cube"],
breaks=vcs_break).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"] = vcs_distance.vcs1
if cleanup:
del vcs_distance
if any("VCA" in s for s in statistics):
vca_distance = VCA_Distance(dataset1["cube"],
dataset2["cube"],
breaks=vca_break).distance_metric()
distances["VCA"] = vca_distance.distance
if not multicore:
fiducial_models["VCA"] = vca_distance.vca1
if cleanup:
del vca_distance
if any("Tsallis" in s for s in statistics):
tsallis_distance = \
Tsallis_Distance(dataset1["moment0"],
dataset2["moment0"]).distance_metric()
distances["Tsallis"] = tsallis_distance.distance
if not multicore:
fiducial_models["Tsallis"] = tsallis_distance.tsallis1
if cleanup:
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"], 5).distance_metric()
distances["Skewness"] = moment_distance.skewness_distance
distances["Kurtosis"] = moment_distance.kurtosis_distance
if not multicore:
fiducial_models["stat_moments"] = moment_distance.moments1
if cleanup:
del moment_distance
if any("PCA" in s for s in statistics):
pca_distance = \
PCA_Distance(dataset1["cube"],
dataset2["cube"]).distance_metric()
distances["PCA"] = pca_distance.distance
if not multicore:
fiducial_models["PCA"] = pca_distance.pca1
if cleanup:
del pca_distance
if any("SCF" in s for s in statistics):
scf_distance = \
SCF_Distance(dataset1["cube"],
dataset2["cube"]).distance_metric()
distances["SCF"] = scf_distance.distance
if not multicore:
fiducial_models["SCF"] = scf_distance.scf1
if cleanup:
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
if cleanup:
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)
dendro_distance.distance_metric()
distances["Dendrogram_Hist"] = dendro_distance.histogram_distance
distances["Dendrogram_Num"] = dendro_distance.num_distance
if not multicore:
fiducial_models["Dendrogram"] = dendro_distance.dendro1
if cleanup:
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_AD" in s for s in statistics):
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.)
pdf_distance.distance_metric()
distances["PDF_Hellinger"] = pdf_distance.hellinger_distance
distances["PDF_KS"] = pdf_distance.ks_distance
# distances["PDF_AD"] = pdf_distance.ad_distance
if not multicore:
fiducial_models["PDF"] = pdf_distance.PDF1
if cleanup:
del pdf_distance
if multicore:
return distances
else:
return distances, fiducial_models
else:
if any("Wavelet" in s for s in statistics):
wavelet_distance = \
Wavelet_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models["Wavelet"]).distance_metric()
distances["Wavelet"] = wavelet_distance.distance
if cleanup:
del wavelet_distance
if any("MVC" in s for s in statistics):
mvc_distance = \
MVC_Distance(dataset1,
dataset2,
fiducial_model=fiducial_models["MVC"]).distance_metric()
distances["MVC"] = mvc_distance.distance
if cleanup:
del mvc_distance
if any("PSpec" in s for s in statistics):
pspec_distance = \
PSpec_Distance(dataset1["moment0"],
dataset2["moment0"],
weights1=dataset1["moment0_error"][0]**2.,
weights2=dataset2["moment0_error"][0]**2.,
fiducial_model=fiducial_models["PSpec"]).distance_metric()
distances["PSpec"] = pspec_distance.distance
if cleanup:
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"]).distance_metric()
distances["Bispectrum"] = bispec_distance.distance
if cleanup:
del bispec_distance
if any("DeltaVariance" in s for s in statistics):
delvar_distance = \
DeltaVariance_Distance(dataset1["moment0"],
dataset2["moment0"],
weights1=dataset1["moment0_error"][0],
weights2=dataset2["moment0_error"][0],
fiducial_model=fiducial_models["DeltaVariance"]).distance_metric()
distances["DeltaVariance_Curve"] = delvar_distance.curve_distance
distances["DeltaVariance_Slope"] = delvar_distance.slope_distance
if cleanup:
del delvar_distance
if any("Genus" in s for s in statistics):
genus_distance = \
GenusDistance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models["Genus"]).distance_metric()
distances["Genus"] = genus_distance.distance
if cleanup:
del genus_distance
if any("VCS" in s for s in statistics):
vcs_distance = \
VCS_Distance(dataset1["cube"],
dataset2["cube"],
fiducial_model=fiducial_models["VCS"],
breaks=vcs_break).distance_metric()
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
distances["VCS"] = vcs_distance.distance
if cleanup:
del vcs_distance
if any("VCA" in s for s in statistics):
vca_distance = \
VCA_Distance(dataset1["cube"],
dataset2["cube"],
fiducial_model=fiducial_models["VCA"],
breaks=vca_break).distance_metric()
distances["VCA"] = vca_distance.distance
if cleanup:
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"]).distance_metric()
distances["Tsallis"] = tsallis_distance.distance
if cleanup:
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"],
5,
fiducial_model=fiducial_models["stat_moments"]).distance_metric()
distances["Skewness"] = moment_distance.skewness_distance
distances["Kurtosis"] = moment_distance.kurtosis_distance
if cleanup:
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"]).distance_metric()
distances["PCA"] = pca_distance.distance
if cleanup:
del pca_distance
if any("SCF" in s for s in statistics):
scf_distance = \
SCF_Distance(dataset1["cube"],
dataset2["cube"],
fiducial_model=fiducial_models["SCF"]).distance_metric()
distances["SCF"] = scf_distance.distance
if cleanup:
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
if cleanup:
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[0] is None:
input2 = dataset2["cube"]
elif isinstance(dendro_saves[0], str):
input2 = dendro_saves[1]
else:
raise UserWarning("dendro_saves must be the filename of the"
" saved file.")
dendro_distance = \
DendroDistance(input1, input2,
fiducial_model=fiducial_models["Dendrogram"],
dendro_params=dendro_params)
dendro_distance.distance_metric()
distances["Dendrogram_Hist"] = dendro_distance.histogram_distance
distances["Dendrogram_Num"] = dendro_distance.num_distance
if cleanup:
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_AD" in s for s in statistics):
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.)
pdf_distance.distance_metric()
distances["PDF_Hellinger"] = pdf_distance.hellinger_distance
distances["PDF_KS"] = pdf_distance.ks_distance
# distances["PDF_AD"] = pdf_distance.ad_distance
if cleanup:
del pdf_distance
return distances