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Merged
merged 6 commits into from
Jun 9, 2021
Merged

Replace np.float with np.float64 to fix numpy 1.20 deprecation #250

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d5ca5c2
Replace np.float with np.float64 to fix numpy 1.20 deprecation
lgarrison Jun 2, 2021
fc2be55
Merge branch 'master' into fix-np.float-deprecation
lgarrison Jun 2, 2021
a166f99
pep8
lgarrison Jun 2, 2021
e10599c
pep8
lgarrison Jun 2, 2021
2a3d792
Updated the docs as well
manodeep Jun 7, 2021
c957a7f
changelog [ci skip]
lgarrison Jun 8, 2021
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1 change: 1 addition & 0 deletions CHANGES.rst
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Expand Up @@ -18,6 +18,7 @@ Enhancements
- In the theoretical VPF calculation (``theory.vpf``), the total volume of the random spheres can now exceed the volume of the sample [#238]
- Gridlink (the binning of particles into cells) now uses a parallel algorithm for the theory module [#239]
- Add detection of known-bad Cray hugepages library at NERSC [#246]
- Replace ``np.float`` with ``np.float64`` to fix numpy 1.20 deprecation [#250]

Bug fixes
---------
Expand Down
16 changes: 8 additions & 8 deletions Corrfunc/io.py
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Expand Up @@ -30,7 +30,7 @@ def read_fastfood_catalog(filename, return_dtype=None, need_header=None):

return_dtype: numpy dtype for returned arrays. Default ``numpy.float``
Specifies the datatype for the returned arrays. Must be in
{np.float, np.float32}
{np.float64, np.float32}

need_header: boolean, default None.
Returns the header found in the fast-food file in addition to the
Expand Down Expand Up @@ -81,9 +81,9 @@ def read_fastfood_catalog(filename, return_dtype=None, need_header=None):

"""
if return_dtype is None:
return_dtype = np.float
return_dtype = np.float64

if return_dtype not in [np.float32, np.float]:
if return_dtype not in [np.float32, np.float64]:
msg = "Return data-type must be set and a valid numpy float"
raise ValueError(msg)

Expand Down Expand Up @@ -142,7 +142,7 @@ def read_fastfood_catalog(filename, return_dtype=None, need_header=None):
assert skip1 == ngal * 4 or skip1 == ngal * 8, \
"fast-food file seems to be corrupt (padding bytes a)"
# the next division must be the integer division
input_dtype = np.float32 if skip1 // ngal == 4 else np.float
input_dtype = np.float32 if skip1 // ngal == 4 else np.float64
array = np.fromfile(f, input_dtype, ngal)
skip2 = struct.unpack(bytes_to_native_str(b'@i'), f.read(4))[0]
if return_dtype == input_dtype:
Expand Down Expand Up @@ -171,7 +171,7 @@ def read_ascii_catalog(filename, return_dtype=None):

return_dtype: numpy dtype for returned arrays. Default ``numpy.float``
Specifies the datatype for the returned arrays. Must be in
{np.float, np.float32}
{np.float64, np.float32}

Returns
--------
Expand Down Expand Up @@ -216,7 +216,7 @@ def read_ascii_catalog(filename, return_dtype=None):
"""

if return_dtype is None:
return_dtype = np.float
return_dtype = np.float64

if not file_exists(filename):
msg = "Could not find file = {0}".format(filename)
Expand All @@ -241,7 +241,7 @@ def read_ascii_catalog(filename, return_dtype=None):
return x, y, z


def read_catalog(filebase=None, return_dtype=np.float):
def read_catalog(filebase=None, return_dtype=np.float64):
"""
Reads a galaxy/randoms catalog and returns 3 XYZ arrays.

Expand All @@ -254,7 +254,7 @@ def read_catalog(filebase=None, return_dtype=np.float):

return_dtype: numpy dtype for returned arrays. Default ``numpy.float``
Specifies the datatype for the returned arrays. Must be in
{np.float, np.float32}
{np.float64, np.float32}

Returns
--------
Expand Down
10 changes: 5 additions & 5 deletions Corrfunc/mocks/DDrppi_mocks.py
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Expand Up @@ -387,12 +387,12 @@ def DDrppi_mocks(autocorr, cosmology, nthreads, pimax, binfile,
import os
os.remove(rbinfile)

results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float),
(bytes_to_native_str(b'rmax'), np.float),
(bytes_to_native_str(b'rpavg'), np.float),
(bytes_to_native_str(b'pimax'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float64),
(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'rpavg'), np.float64),
(bytes_to_native_str(b'pimax'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float)])
(bytes_to_native_str(b'weightavg'), np.float64)])
results = np.array(extn_results, dtype=results_dtype)

if not c_api_timer:
Expand Down
10 changes: 5 additions & 5 deletions Corrfunc/mocks/DDsmu_mocks.py
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Expand Up @@ -322,12 +322,12 @@ def DDsmu_mocks(autocorr, cosmology, nthreads, mu_max, nmu_bins, binfile,
import os
os.remove(sbinfile)

results_dtype = np.dtype([(bytes_to_native_str(b'smin'), np.float),
(bytes_to_native_str(b'smax'), np.float),
(bytes_to_native_str(b'savg'), np.float),
(bytes_to_native_str(b'mumax'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'smin'), np.float64),
(bytes_to_native_str(b'smax'), np.float64),
(bytes_to_native_str(b'savg'), np.float64),
(bytes_to_native_str(b'mumax'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float)])
(bytes_to_native_str(b'weightavg'), np.float64)])

nbin = len(extn_results)
results = np.zeros(nbin, dtype=results_dtype)
Expand Down
12 changes: 6 additions & 6 deletions Corrfunc/mocks/DDtheta_mocks.py
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Expand Up @@ -338,11 +338,11 @@ def DDtheta_mocks(autocorr, nthreads, binfile,
import os
os.remove(rbinfile)

results_dtype = np.dtype([(bytes_to_native_str(b'thetamin'), np.float),
(bytes_to_native_str(b'thetamax'), np.float),
(bytes_to_native_str(b'thetaavg'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'thetamin'), np.float64),
(bytes_to_native_str(b'thetamax'), np.float64),
(bytes_to_native_str(b'thetaavg'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float)])
(bytes_to_native_str(b'weightavg'), np.float64)])
results = np.array(extn_results, dtype=results_dtype)

if not c_api_timer:
Expand Down Expand Up @@ -556,8 +556,8 @@ def find_fastest_DDtheta_mocks_bin_refs(autocorr, nthreads, binfile,

dtype = np.dtype([(bytes_to_native_str(b'nRA'), np.int),
(bytes_to_native_str(b'nDEC'), np.int),
(bytes_to_native_str(b'avg_time'), np.float),
(bytes_to_native_str(b'sigma_time'), np.float)])
(bytes_to_native_str(b'avg_time'), np.float64),
(bytes_to_native_str(b'sigma_time'), np.float64)])
all_runtimes = np.zeros(nperms, dtype=dtype)
all_runtimes[:] = np.inf

Expand Down
4 changes: 2 additions & 2 deletions Corrfunc/mocks/vpf_mocks.py
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Expand Up @@ -311,9 +311,9 @@ def vpf_mocks(rmax, nbins, nspheres, numpN,
else:
extn_results, api_time = extn_results

results_dtype = np.dtype([(bytes_to_native_str(b'rmax'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'pN'),
(np.float, numpN))])
(np.float64, numpN))])
nbin = len(extn_results)
results = np.zeros(nbin, dtype=results_dtype)

Expand Down
8 changes: 4 additions & 4 deletions Corrfunc/theory/DD.py
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Expand Up @@ -254,11 +254,11 @@ def DD(autocorr, nthreads, binfile, X1, Y1, Z1, weights1=None, periodic=True,
import os
os.remove(rbinfile)

results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float),
(bytes_to_native_str(b'rmax'), np.float),
(bytes_to_native_str(b'ravg'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float64),
(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'ravg'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float)])
(bytes_to_native_str(b'weightavg'), np.float64)])
results = np.array(extn_results, dtype=results_dtype)
if not c_api_timer:
return results
Expand Down
11 changes: 6 additions & 5 deletions Corrfunc/theory/DDrppi.py
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Expand Up @@ -305,12 +305,13 @@ def DDrppi(autocorr, nthreads, pimax, binfile, X1, Y1, Z1, weights1=None,
import os
os.remove(rbinfile)

results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float),
(bytes_to_native_str(b'rmax'), np.float),
(bytes_to_native_str(b'rpavg'), np.float),
(bytes_to_native_str(b'pimax'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float64),
(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'rpavg'), np.float64),
(bytes_to_native_str(b'pimax'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float),])
(bytes_to_native_str(b'weightavg'), np.float64),
])
results = np.array(extn_results, dtype=results_dtype)

if not c_api_timer:
Expand Down
11 changes: 6 additions & 5 deletions Corrfunc/theory/DDsmu.py
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Expand Up @@ -322,12 +322,13 @@ def DDsmu(autocorr, nthreads, binfile, mu_max, nmu_bins,
import os
os.remove(sbinfile)

results_dtype = np.dtype([(bytes_to_native_str(b'smin'), np.float),
(bytes_to_native_str(b'smax'), np.float),
(bytes_to_native_str(b'savg'), np.float),
(bytes_to_native_str(b'mu_max'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'smin'), np.float64),
(bytes_to_native_str(b'smax'), np.float64),
(bytes_to_native_str(b'savg'), np.float64),
(bytes_to_native_str(b'mu_max'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float),])
(bytes_to_native_str(b'weightavg'), np.float64),
])
results = np.array(extn_results, dtype=results_dtype)

if not c_api_timer:
Expand Down
4 changes: 2 additions & 2 deletions Corrfunc/theory/vpf.py
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Expand Up @@ -223,9 +223,9 @@ def vpf(rmax, nbins, nspheres, numpN, seed,
else:
extn_results, api_time = extn_results

results_dtype = np.dtype([(bytes_to_native_str(b'rmax'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'pN'),
(np.float, numpN))])
(np.float64, numpN))])
nbin = len(extn_results)
results = np.zeros(nbin, dtype=results_dtype)

Expand Down
14 changes: 7 additions & 7 deletions Corrfunc/theory/wp.py
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Expand Up @@ -174,8 +174,8 @@ def find_fastest_wp_bin_refs(boxsize, pimax, nthreads, binfile, X, Y, Z,
dtype = np.dtype([(bytes_to_native_str(b'nx'), np.int),
(bytes_to_native_str(b'ny'), np.int),
(bytes_to_native_str(b'nz'), np.int),
(bytes_to_native_str(b'avg_time'), np.float),
(bytes_to_native_str(b'sigma_time'), np.float)])
(bytes_to_native_str(b'avg_time'), np.float64),
(bytes_to_native_str(b'sigma_time'), np.float64)])
all_runtimes = np.zeros(maxbinref**3, dtype=dtype)
all_runtimes[:] = np.inf

Expand Down Expand Up @@ -525,12 +525,12 @@ def wp(boxsize, pimax, nthreads, binfile, X, Y, Z,
import os
os.remove(rbinfile)

results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float),
(bytes_to_native_str(b'rmax'), np.float),
(bytes_to_native_str(b'rpavg'), np.float),
(bytes_to_native_str(b'wp'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float64),
(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'rpavg'), np.float64),
(bytes_to_native_str(b'wp'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float)])
(bytes_to_native_str(b'weightavg'), np.float64)])
results = np.array(extn_results, dtype=results_dtype)

# A better solution for returning multiple values based on
Expand Down
10 changes: 5 additions & 5 deletions Corrfunc/theory/xi.py
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Expand Up @@ -234,12 +234,12 @@ def xi(boxsize, nthreads, binfile, X, Y, Z,
import os
os.remove(rbinfile)

results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float),
(bytes_to_native_str(b'rmax'), np.float),
(bytes_to_native_str(b'ravg'), np.float),
(bytes_to_native_str(b'xi'), np.float),
results_dtype = np.dtype([(bytes_to_native_str(b'rmin'), np.float64),
(bytes_to_native_str(b'rmax'), np.float64),
(bytes_to_native_str(b'ravg'), np.float64),
(bytes_to_native_str(b'xi'), np.float64),
(bytes_to_native_str(b'npairs'), np.uint64),
(bytes_to_native_str(b'weightavg'), np.float)])
(bytes_to_native_str(b'weightavg'), np.float64)])
results = np.array(extn_results, dtype=results_dtype)

if not c_api_timer:
Expand Down
8 changes: 4 additions & 4 deletions Corrfunc/utils.py
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Expand Up @@ -141,8 +141,8 @@ def convert_3d_counts_to_cf(ND1, ND2, NR1, NR2,

nonzero = pair_counts['R1R2'] > 0
if 'LS' in estimator or 'Landy' in estimator:
fN1 = np.float(NR1) / np.float(ND1)
fN2 = np.float(NR2) / np.float(ND2)
fN1 = np.float64(NR1) / np.float64(ND1)
fN2 = np.float64(NR2) / np.float64(ND2)
cf = np.zeros(nbins)
cf[:] = np.nan
cf[nonzero] = (fN1 * fN2 * pair_counts['D1D2'][nonzero] -
Expand Down Expand Up @@ -795,8 +795,8 @@ def gridlink_sphere(thetamax,
dec_binsize = dec_diff/ngrid_dec

# Upper and lower limits of the declination bands
grid_dtype= np.dtype({'names':['dec_limit','ra_limit'],
'formats':[(np.float, (2, )), (np.float, (2, ))]
grid_dtype = np.dtype({'names': ['dec_limit', 'ra_limit'],
'formats': [(np.float64, (2, )), (np.float64, (2, ))]
})
if not link_in_ra:
sphere_grid = np.zeros(ngrid_dec, dtype=grid_dtype)
Expand Down
16 changes: 6 additions & 10 deletions docs/source/modules/read_catalog.rst
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Expand Up @@ -12,7 +12,7 @@ then you can just pass them straight to ``Corrfunc``.
If you need to read the arrays in from disk, then read
on. For the command-line interface, the input files can only
be in ASCII or fast-food format (for description of fast-food
binaries, see :ref:`fast_food_binary`).
binaries, see :ref:`fast_food_binary`).

.. toctree::
:maxdepth: 1
Expand All @@ -24,7 +24,7 @@ Reading from ASCII files
========================

This is the most straight forward way -- you need an ASCII
file with columns X/Y/Z (white-space separated).
file with columns X/Y/Z (white-space separated).

Using ``numpy.genfromtxt``
---------------------------
Expand All @@ -35,12 +35,12 @@ Using ``numpy.genfromtxt``
fname = "myfile_containing_xyz_columns.dat"

# For double precision calculations
dtype = np.float ## change to np.float32 for single precision
dtype = np.float64 ## change to np.float32 for single precision

X, Y, Z = np.genfromtxt(fname, dtype=dtype, unpack=True)


.. note:: :py:mod:`Corrfunc.read_catalog` uses this exact code-snippet to read in ASCII files in python.
.. note:: :py:mod:`Corrfunc.read_catalog` uses this exact code-snippet to read in ASCII files in python.


Reading from fast-food files
Expand All @@ -50,14 +50,14 @@ If you are using the command-line interface, then the code will **have** to
read the arrays from files. While ``Corrfunc`` natively supports both
ASCII and fast-food formats (for description of fast-food binaries, see
:ref:`fast_food_binary`), the following python utility is intended to
read both these types of files.
read both these types of files.


Using utility: :py:mod:`Corrfunc.io.read_catalog`
-------------------------------------------------

:py:mod:`Corrfunc.io.read_catalog` can directly read ASCII files or fast-food binary
files.
files.

.. code:: python

Expand All @@ -71,7 +71,3 @@ files.
# filename
fname = "myfile_containing_xyz_columns.dat"
X, Y, Z = read_catalog(fname)




2 changes: 1 addition & 1 deletion mocks/python_bindings/call_correlation_functions_mocks.py
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Expand Up @@ -48,7 +48,7 @@ def main():
# Double-precision calculations
# (if you want single-prec, just change the following line
# to dtype = np.float32)
dtype = np.float
dtype = np.float64

# Check if pandas is available - much faster to read in the
# data through pandas
Expand Down
4 changes: 2 additions & 2 deletions theory/python_bindings/call_correlation_functions.py
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Expand Up @@ -142,7 +142,7 @@ def read_fastfood_catalog(filename, return_dtype=None, need_header=None):
assert skip1 == ngal * 4 or skip1 == ngal * 8, \
"fast-food file seems to be corrupt (padding bytes a)"
# the next division must be the integer division
input_dtype = np.float32 if skip1 // ngal == 4 else np.float
input_dtype = np.float32 if skip1 // ngal == 4 else np.float64
array = np.fromfile(f, input_dtype, ngal)
skip2 = struct.unpack(bytes_to_native_str(b'@i'), f.read(4))[0]
pos[field] = array if return_dtype == input_dtype \
Expand Down Expand Up @@ -181,7 +181,7 @@ def read_fastfood_catalog(filename, return_dtype=None, need_header=None):
f = read_fastfood_catalog if '.ff' in extension else read_ascii_catalog

# default return is double
x, y, z = f(filebase, np.float)
x, y, z = f(filebase, np.float64)
return x, y, z

raise IOError("Could not locate file {0}", filebase)
Expand Down