update np.float and np.int

LBL-EESA · Dec 16, 2022 · 8110300 · 8110300
1 parent 030bf72
commit 8110300
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Showing 2 changed files with 13 additions and 13 deletions.
diff --git a/fastkde/fastKDE.py b/fastkde/fastKDE.py
@@ -158,9 +158,9 @@ def vprint(msg):
       dataRank = len(shape(data))
       #If the data are a vector, promote the data to a rank-1 array with only 1 column
       if(dataRank == 1):
-          data = array(originalData[newaxis,:],dtype=npy.float)
+          data = array(originalData[newaxis,:],dtype=npy.float_)
       else:
-          data = array(originalData,dtype=npy.float)
+          data = array(originalData,dtype=npy.float_)
       if(dataRank > 2):
           raise ValueError("data must be a rank-2 array of shape [numVariables,numDataPoints]")
 

diff --git a/fastkde/nufft.pyx b/fastkde/nufft.pyx
@@ -114,7 +114,7 @@ cpdef np.ndarray[double complex] nuifft( \
     #********************************************
     vprint("Getting the size of the frequency spaces",beVerbose)
     cdef int n,t,iNotMissing
-    cdef np.int_t [:] frequencySizes = np.zeros([numDimensions],dtype=np.int)
+    cdef np.int_t [:] frequencySizes = np.zeros([numDimensions],dtype=np.int_)
 
     for n in range(numDimensions):
         iNotMissing = 0
@@ -163,8 +163,8 @@ cpdef np.ndarray[double complex] nuifft( \
     hyperSlabSize = nspread**numDimensions
 
     #get the shape of a hyperslab
-    #cdef np.ndarray[np.int_t,ndim=1] hyperSlabShape = nspread*np.ones([numDimensions],dtype=np.int)
-    cdef np.int_t [:] hyperSlabShape = nspread*np.ones([numDimensions],dtype=np.int)
+    #cdef np.ndarray[np.int_t,ndim=1] hyperSlabShape = nspread*np.ones([numDimensions],dtype=np.int_)
+    cdef np.int_t [:] hyperSlabShape = nspread*np.ones([numDimensions],dtype=np.int_)
     vprint("\tconvolution hyperslab shape: {}".format(hyperSlabShape),beVerbose)
 
     #Calculate the quantities necessary for estimating x-indices
@@ -174,9 +174,9 @@ cpdef np.ndarray[double complex] nuifft( \
             deltaxs = np.array([ abscissaGrids[n,1] - abscissaGrids[n,0] for n in range(numDimensions) ])
 
     #Inialize worker terms for the convolution
-    #cdef np.ndarray[long,ndim=1] mvec = np.zeros([numDimensions],dtype=np.int)
-    cdef np.int_t [:] mvec = np.zeros([numDimensions],dtype=np.int)
-    cdef np.int_t [:] m0vec = np.zeros([numDimensions],dtype=np.int)
+    #cdef np.ndarray[long,ndim=1] mvec = np.zeros([numDimensions],dtype=np.int_)
+    cdef np.int_t [:] mvec = np.zeros([numDimensions],dtype=np.int_)
+    cdef np.int_t [:] m0vec = np.zeros([numDimensions],dtype=np.int_)
     cdef np.float_t [:] mprimevec = np.zeros([numDimensions])
     cdef np.float_t mprime = 0.0
     cdef double complex gaussTerm = 0.0
@@ -247,7 +247,7 @@ cpdef np.ndarray[double complex] nuifft( \
     #Pre-declare and allocate a raveled form of the DFT
     cdef double complex [:] DFT = np.zeros([freqSpaceSize],dtype=np.complex128)
     #Pre declare a dimension index vector
-    cdef np.int_t [:] dimInds = np.zeros([numDimensions],dtype=np.int)
+    cdef np.int_t [:] dimInds = np.zeros([numDimensions],dtype=np.int_)
 
     #Deconvolve the FFT (divide by the FFT of the gaussian) to obtain the DFT estimate
     vprint("Deconvolving the Fourier transformed data",beVerbose)
@@ -354,7 +354,7 @@ cpdef np.ndarray[double complex] idft( \
     #********************************************
     vprint("Getting the size of the frequency spaces",beVerbose)
     cdef int n,t,iNotMissing
-    cdef np.int_t [:] frequencySizes = np.zeros([numDimensions],dtype=np.int)
+    cdef np.int_t [:] frequencySizes = np.zeros([numDimensions],dtype=np.int_)
 
     for n in range(numDimensions):
         iNotMissing = 0
@@ -375,7 +375,7 @@ cpdef np.ndarray[double complex] idft( \
     cdef int i,k
 
     #Pre declare a dimension index vector
-    cdef np.int_t [:] dimInds = np.zeros([numDimensions],dtype=np.int)
+    cdef np.int_t [:] dimInds = np.zeros([numDimensions],dtype=np.int_)
 
     cdef double complex myDFT
     cdef double expArg
@@ -577,7 +577,7 @@ cpdef np.ndarray[double complex] dft_points( \
     #********************************************
     vprint("Getting the size of the frequency spaces",beVerbose)
     cdef int t,iNotMissing
-    cdef np.int_t [:] frequencySizes = np.zeros([numDimensions],dtype=np.int)
+    cdef np.int_t [:] frequencySizes = np.zeros([numDimensions],dtype=np.int_)
 
     for n in range(numDimensions):
         iNotMissing = 0
@@ -596,7 +596,7 @@ cpdef np.ndarray[double complex] dft_points( \
     cdef double complex [:] DFT = np.zeros([freqSpaceSize],dtype=np.complex128)
 
     #Pre declare a dimension index vector
-    cdef np.int_t [:] dimInds = np.zeros([numDimensions],dtype=np.int)
+    cdef np.int_t [:] dimInds = np.zeros([numDimensions],dtype=np.int_)
 
     vprint("Calculating the DFT",beVerbose)
     with nogil: