allow numpy 2.0 to run

bin/all_sky_search/pycbc_coinc_statmap

@@ -508,9 +508,9 @@ f.attrs['hierarchical_removal_iterations'] = h_iterations
 
 # Write whether hierarchical removals were removed against the
 # inclusive background or the exclusive background. Have to use
-# numpy.string_ datatype.
+# numpy.bytes_ datatype.
 if h_iterations != 0:
     hrm_method = args.hierarchical_removal_against
-    f.attrs['hierarchical_removal_method'] = numpy.string_(hrm_method)
+    f.attrs['hierarchical_removal_method'] = numpy.bytes_(hrm_method)
 
 logging.info("Done")

bin/all_sky_search/pycbc_sngls_statmap

@@ -436,10 +436,10 @@ f.attrs['hierarchical_removal_iterations'] = h_iterations
 
 # Write whether hierarchical removals were removed against the
 # inclusive background or the exclusive background. Have to use
-# numpy.string_ datatype.
+# numpy.bytes_ datatype.
 if h_iterations != 0:
     hrm_method = args.hierarchical_removal_against
-    f.attrs['hierarchical_removal_method'] = numpy.string_(hrm_method)
+    f.attrs['hierarchical_removal_method'] = numpy.bytes_(hrm_method)
 
 logging.info("Done")
 

pycbc/conversions.py

@@ -77,9 +77,9 @@ def ensurearray(*args):
         inputs was an array.
     """
     input_is_array = any(isinstance(arg, numpy.ndarray) for arg in args)
-    args = numpy.broadcast_arrays(*args)
+    args = list(numpy.broadcast_arrays(*args))
     args.append(input_is_array)
-    return args
+    return tuple(args)
 
 
 def formatreturn(arg, input_is_array=False):

pycbc/events/eventmgr.py

@@ -149,8 +149,8 @@ def findchirp_cluster_over_window(times, values, window_length):
 
     indices = numpy.zeros(len(times), dtype=numpy.int32)
     tlen = len(times)
-    absvalues = numpy.array(abs(values), copy=False)
-    times = numpy.array(times, dtype=numpy.int32, copy=False)
+    absvalues = numpy.asarray(abs(values))
+    times = numpy.asarray(times, dtype=numpy.int32)
     k = findchirp_cluster_over_window_cython(times, absvalues, window_length,
                                              indices, tlen)
 

pycbc/events/stat.py

@@ -506,7 +506,8 @@ def get_hist(self, ifos=None):
 
             # renormalise to PDF
             self.weights[ifo] = \
-                weights[ifo] / (weights[ifo].sum() * bin_volume)
+                (weights[ifo] / (weights[ifo].sum() * bin_volume))
+            self.weights[ifo] = self.weights[ifo].astype(numpy.float32)
 
             if param[ifo].dtype == numpy.int8:
                 # Older style, incorrectly sorted histogram file
@@ -641,13 +642,17 @@ def logsignalrate(self, stats, shift, to_shift):
 
         # Get reference ifo information
         rate = numpy.zeros(len(shift), dtype=numpy.float32)
-        ps = {ifo: numpy.array(stats[ifo]['coa_phase'], ndmin=1)
+        ps = {ifo: numpy.array(stats[ifo]['coa_phase'],
+                               dtype=numpy.float32, ndmin=1)
               for ifo in self.ifos}
-        ts = {ifo: numpy.array(stats[ifo]['end_time'], ndmin=1)
+        ts = {ifo: numpy.array(stats[ifo]['end_time'],
+                               dtype=numpy.float64, ndmin=1)
               for ifo in self.ifos}
-        ss = {ifo: numpy.array(stats[ifo]['snr'], ndmin=1)
+        ss = {ifo: numpy.array(stats[ifo]['snr'],
+                               dtype=numpy.float32, ndmin=1)
               for ifo in self.ifos}
-        sigs = {ifo: numpy.array(stats[ifo]['sigmasq'], ndmin=1)
+        sigs = {ifo: numpy.array(stats[ifo]['sigmasq'],
+                                 dtype=numpy.float32, ndmin=1)
                 for ifo in self.ifos}
         for ref_ifo in self.ifos:
             rtype = rtypes[ref_ifo]

pycbc/io/record.py

@@ -1507,7 +1507,7 @@ def _isstring(dtype):
     """Given a numpy dtype, determines whether it is a string. Returns True
     if the dtype is string or unicode.
     """
-    return dtype.type == numpy.unicode_ or dtype.type == numpy.string_
+    return dtype.type == numpy.unicode_ or dtype.type == numpy.bytes_
 
 
 def aliases_from_fields(fields):

pycbc/tmpltbank/coord_utils.py

@@ -440,7 +440,7 @@ def get_mu_params(lambdas, metricParams, fUpper):
     mus : list of floats or numpy.arrays
         Position of the system(s) in the mu coordinate system
     """
-    lambdas = numpy.array(lambdas, copy=False)
+    lambdas = numpy.asarray(lambdas)
     # If original inputs were floats we need to make this a 2D array
     if len(lambdas.shape) == 1:
         resize_needed = True
@@ -451,7 +451,7 @@ def get_mu_params(lambdas, metricParams, fUpper):
     evecs = metricParams.evecs[fUpper]
     evals = metricParams.evals[fUpper]
 
-    evecs = numpy.array(evecs, copy=False)
+    evecs = numpy.asarray(evecs)
 
     mus = ((lambdas.T).dot(evecs)).T
     mus = mus * numpy.sqrt(evals)[:,None]
@@ -479,7 +479,7 @@ def get_covaried_params(mus, evecsCV):
     xis : list of floats or numpy.arrays
         Position of the system(s) in the xi coordinate system
     """
-    mus = numpy.array(mus, copy=False)
+    mus = numpy.asarray(mus)
     # If original inputs were floats we need to make this a 2D array
     if len(mus.shape) == 1:
         resize_needed = True

requirements.txt

@@ -3,7 +3,7 @@ astropy>=2.0.3,!=4.2.1,!=4.0.5
 Mako>=1.0.1
 scipy>=0.16.0
 matplotlib>=2.0.0
-numpy>=1.16.0,!=1.19.0,<2.0.0
+numpy>=1.16.0,!=1.19.0
 pillow
 h5py>=3.0.0,!=3.7.0
 jinja2

setup.py

@@ -32,7 +32,7 @@
 setup_requires = ['numpy>=1.16.0']
 install_requires = setup_requires + [
     'cython>=0.29',
-    'numpy>=1.16.0,!=1.19.0,<2.0.0',
+    'numpy>=1.16.0,!=1.19.0',
     'scipy>=0.16.0',
     'astropy>=2.0.3,!=4.2.1,!=4.0.5',
     'matplotlib>=1.5.1',