have updated numpy and pandas to specify correct float and int to sat…

…isfy deprication of .int and .float

cgat/BamTools/bamtools.pyx

@@ -666,12 +666,12 @@ def bam2stats_count(AlignmentFile samfile,
         counter.total_read2_is_unmapped = total_read2_is_unmapped
         counter.total_read2_is_missing = total_read2_is_missing
 
-        substitution_rates = numpy.zeros(fastq_nreads, dtype=numpy.float)
-        insertion_rates = numpy.zeros(fastq_nreads, dtype=numpy.float)
-        deletion_rates = numpy.zeros(fastq_nreads, dtype=numpy.float)
-        error_rates = numpy.zeros(fastq_nreads, dtype=numpy.float)
-        coverages = numpy.zeros(fastq_nreads, dtype=numpy.float)
-        mask = numpy.ones(fastq_nreads, dtype=numpy.int)
+        substitution_rates = numpy.zeros(fastq_nreads, dtype=numpy.float64)
+        insertion_rates = numpy.zeros(fastq_nreads, dtype=numpy.float64)
+        deletion_rates = numpy.zeros(fastq_nreads, dtype=numpy.float64)
+        error_rates = numpy.zeros(fastq_nreads, dtype=numpy.float64)
+        coverages = numpy.zeros(fastq_nreads, dtype=numpy.float64)
+        mask = numpy.ones(fastq_nreads, dtype=numpy.int64)
 
         if outfile_details:
             header = ["read_md5",
@@ -1558,7 +1558,7 @@ def bam2stats_window_count(AlignmentFile samfile,
     cdef uint32_t ncontigs = len(contigs)
     cdef int ncolumns = len(columns)
     cdef numpy.ndarray offsets = numpy.zeros(
-        ncontigs + 1, dtype=numpy.int32)
+        ncontigs + 1, dtype=numpy.int64)
 
     for idx, length in enumerate(lengths):
         offsets[idx] = offset
@@ -1570,7 +1570,7 @@ def bam2stats_window_count(AlignmentFile samfile,
 
     cdef numpy.ndarray window_counts = numpy.zeros(
         (total_windows, ncolumns),
-        dtype=numpy.int32)
+        dtype=numpy.int64)
 
     cdef int32_t [:, :] window_counts_view = window_counts
 

cgat/BamTools/peakshape.pyx

@@ -51,7 +51,7 @@ cdef class Counter:
                                                  **kwargs )
 
         nbins = len(bins) - 1
-        hist = numpy.zeros(nbins, dtype=numpy.int)
+        hist = numpy.zeros(nbins, dtype=numpy.int64)
         cdef int lcounts = len(counts)
 
         if self.smooth_method is not None:
@@ -196,7 +196,7 @@ cdef class Counter:
         #################################################
         # compute histogram
         nbins = len(bins) - 1
-        hist = numpy.zeros(nbins, dtype = numpy.int)
+        hist = numpy.zeros(nbins, dtype = numpy.int64)
 
         # decide in which region to count - interval or window
         if use_interval:
@@ -337,7 +337,7 @@ cdef class CounterBam(Counter):
                 for i from rstart <= i < rend: ccounts[i] += 1
 
         # transfer to numpy count object
-        counts = numpy.zeros( interval_width, dtype = numpy.int )
+        counts = numpy.zeros( interval_width, dtype = numpy.int64)
         for i from 0 <= i < interval_width: counts[i] = ccounts[i]
 
         free( ccounts )

cgat/GeneModelAnalysis.pyx

@@ -307,9 +307,9 @@ class Counter:
         return utr5, utr3
 
 cimport numpy
-DTYPE_INT = numpy.int
+DTYPE_INT = numpy.int64
 ctypedef numpy.int_t DTYPE_INT_t
-DTYPE_FLOAT = numpy.float
+DTYPE_FLOAT = numpy.float64
 ctypedef numpy.float_t DTYPE_FLOAT_t
 
 
@@ -360,8 +360,8 @@ class CounterReadCoverage(Counter):
             return
 
         cdef int length = sum( [x[1] - x[0] for x in segments ] )
-        cdef numpy.ndarray[DTYPE_INT_t, ndim=1] counts_sense = numpy.zeros( length, dtype = numpy.int )
-        cdef numpy.ndarray[DTYPE_INT_t, ndim=1] counts_antisense = numpy.zeros( length, dtype = numpy.int )
+        cdef numpy.ndarray[DTYPE_INT_t, ndim=1] counts_sense = numpy.zeros( length, dtype = numpy.int64)
+        cdef numpy.ndarray[DTYPE_INT_t, ndim=1] counts_antisense = numpy.zeros( length, dtype = numpy.int64)
         cdef int p, pos, offset
 
         reads_sense, reads_antisense = set(), set()
@@ -893,11 +893,11 @@ class CounterReadCountsFull(CounterBAM):
                          x=ndirection_status, 
                          y=nexons_status,
                          z=nspliced_status):
-            counters = numpy.zeros(n, dtype=numpy.float)
+            counters = numpy.zeros(n, dtype=numpy.float64)
             counters.shape = (x,y,z)
             return counters
 
-        # counters = numpy.zeros(ncounters, dtype=numpy.float)
+        # counters = numpy.zeros(ncounters, dtype=numpy.float64)
         # counters.shape = (ndirection_status,
         #                   nexons_status,
         #                   nspliced_status)
@@ -1072,7 +1072,7 @@ class CounterReadCountsFull(CounterBAM):
 
         if not weight_multi_mapping:
             # convert to full counts
-            counters = numpy.array(counters, dtype=numpy.int)
+            counters = numpy.array(counters, dtype=numpy.int64)
 
         self.counters = counters
         self.reads_below_quality = quality_read_status
@@ -1415,7 +1415,7 @@ class CounterReadPairCountsFull(CounterBAM):
         cdef AlignedSegment read1
 
         # define counters, add 1 for quality filtered reads
-        counters = numpy.zeros(ncounters, dtype=numpy.float)
+        counters = numpy.zeros(ncounters, dtype=numpy.float64)
         counters.shape = (npair_status,
                           ndirection_status,
                           nexons_status,
@@ -1667,7 +1667,7 @@ class CounterReadPairCountsFull(CounterBAM):
 
         if not weight_multi_mapping:
             # convert to full counts
-            counters = numpy.array(counters, dtype=numpy.int)
+            counters = numpy.array(counters, dtype=numpy.int64)
 
         self.counters = counters
 
@@ -2308,7 +2308,7 @@ class CounterCoverage(CounterOverlap):
 
         l = sum([x[1] - x[0] for x in segments])
 
-        counts = numpy.zeros(l, numpy.int)
+        counts = numpy.zeros(l, numpy.int64)
 
         intervals = set()
         if contig in self.mIntersectors:
@@ -4864,8 +4864,8 @@ class CounterReadExtension(Counter):
                 # create dummy counts vector
                 start, end = 0, 1
 
-            counts.append([numpy.zeros(end - start, numpy.int),
-                           numpy.zeros(end - start, numpy.int)])
+            counts.append([numpy.zeros(end - start, numpy.int64),
+                           numpy.zeros(end - start, numpy.int64)])
 
         def __add(counts, positions, offset):
             l = len(counts)

cgat/Genomics.py

@@ -1475,7 +1475,7 @@ def AlignedPair2SubstitutionMatrix(seq1, seq2, alphabet):
         raise ValueError("two sequences of unequal length submitted")
 
     nchars = len(alphabet)
-    matrix = numpy.zeros((nchars, nchars), numpy.int)
+    matrix = numpy.zeros((nchars, nchars), numpy.int64)
 
     map_char2pos = {}
     for x in alphabet:

cgat/MatrixTools.py

@@ -210,7 +210,7 @@ def getMatrixFromEdges(lines, options,
     return matrix
 
 
-def buildMatrixFromLists(lists, dtype=numpy.float, default=None):
+def buildMatrixFromLists(lists, dtype=numpy.float64, default=None):
     '''build a matrix from a list of lists.
 
     Each list is a list of tuples (row, value).
@@ -235,7 +235,7 @@ def buildMatrixFromLists(lists, dtype=numpy.float, default=None):
     return matrix, list(all_rows.keys())
 
 
-def buildMatrixFromTables(infiles, column, column_header=0, dtype=numpy.float,
+def buildMatrixFromTables(infiles, column, column_header=0, dtype=numpy.float64,
                           default=None):
     '''build a matrix from a column called *column* in a series of input files.
 
@@ -260,7 +260,7 @@ def buildMatrixFromEdges(edges,
                          is_symmetric=False,
                          missing_value=0,
                          diagonal_value=0,
-                         dtype=numpy.int):
+                         dtype=numpy.int64):
     """build a matrix from an edge-list representation.
 
     For example, the following list of tuples::

cgat/SetTools.py

@@ -97,7 +97,7 @@ def unionIntersectionMatrix(list_of_sets):
     '''
 
     l = len(list_of_sets)
-    matrix = numpy.zeros((l, l), dtype=numpy.int)
+    matrix = numpy.zeros((l, l), dtype=numpy.int64)
     for x in range(l):
         xx = list_of_sets[x]
         for y in range(x):

cgat/Stats.py

@@ -594,7 +594,7 @@ def doFDRPython(pvalues,
     # v[i] = number of observations less than or equal to pvalue[i]
     # could this be done more elegantly?
     val2bin = len(bins) - numpy.digitize(pvalues, bins)
-    v = numpy.zeros(m, dtype=numpy.int)
+    v = numpy.zeros(m, dtype=numpy.int64)
     lastbin = None
     for x in range(m - 1, -1, -1):
         bin = val2bin[idx[x]]
@@ -1155,8 +1155,8 @@ def savitzky_golay(y, window_size, order, deriv=0, rate=1):
     """
 
     try:
-        window_size = numpy.abs(numpy.int(window_size))
-        order = numpy.abs(numpy.int(order))
+        window_size = numpy.abs(numpy.int64(window_size))
+        order = numpy.abs(numpy.int64(order))
     except ValueError:
         raise ValueError("window_size and order have to be of type int")
     if window_size % 2 != 1 or window_size < 1:

cgat/tools/diff_bed.py

@@ -117,7 +117,7 @@ def _count(self, filename, idx):
 
             nexons_overlapping += 1
             start, end = this.start, this.end
-            counts = numpy.zeros(end - start, numpy.int)
+            counts = numpy.zeros(end - start, numpy.int64)
             for other_start, other_end, other_value in intervals:
                 for x in range(max(start, other_start) - start, min(end, other_end) - start):
                     counts[x] += 1
@@ -203,7 +203,7 @@ def _countIndices(self, idx_in, idx):
                     continue
 
                 nexons_overlapping += 1
-                counts = numpy.zeros(end - start, numpy.int)
+                counts = numpy.zeros(end - start, numpy.int64)
                 for other_start, other_end, other_value in intervals:
                     for x in range(max(start, other_start) - start, min(end, other_end) - start):
                         counts[x] += 1

cgat/tools/diff_gtf.py

@@ -194,7 +194,7 @@ def _count(self, filename, idx):
             overlapping_genes.add(this.gene_id)
             nexons_overlapping += 1
             start, end = this.start, this.end
-            counts = numpy.zeros(end - start, numpy.int)
+            counts = numpy.zeros(end - start, numpy.int64)
             for other_start, other_end, other_value in intervals:
                 for x in range(max(start, other_start) - start, min(end, other_end) - start):
                     counts[x] += 1