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import numpy as np | |
import glob | |
import fitsio | |
import GCRCatalogs | |
import GCR | |
from sklearn.neighbors import KDTree | |
import astropy.table | |
import healpy as hp | |
import os | |
import argparse |
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def compute_stats(x,y,z,bins,cut=[]): | |
b1, binx, biny, _ = binned_statistic_2d(x[cut],y[cut],z[cut],statistic='sum',bins=bins) | |
c1, binx, biny, _ = binned_statistic_2d(x[cut],y[cut],z[cut],statistic='count',bins=bins) | |
b2, binx, biny, _ = binned_statistic_2d(x[cut],y[cut],z[cut]**2,statistic='sum',bins=bins) | |
bin_center = 0.5*(bin_edges[1:]+bin_edges[:-1]) | |
return b1, c1, b2, bin_center |
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import numpy as np | |
import dask.dataframe as dd | |
import desc.pserv | |
import desc.pserv.utils as pserv_utils | |
import lsst.daf.persistence | |
def asPandas(cat, cls=None, copy=False, unviewable="copy"): | |
"""! | |
Return a pandas dataframe view into this catalog. |
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