Remove numpy asfarray and float_ usage to support v2.0 deprecation

cnvlib/bintest.py

@@ -88,7 +88,7 @@ def z_prob(cnarr):
 
 def p_adjust_bh(p):
     """Benjamini-Hochberg p-value correction for multiple hypothesis testing."""
-    p = np.asfarray(p)
+    p = np.asarray(p, dtype=float)
     by_descend = p.argsort()[::-1]
     by_orig = by_descend.argsort()
     steps = float(len(p)) / np.arange(len(p), 0, -1)

cnvlib/call.py

@@ -130,7 +130,7 @@ def absolute_threshold(cnarr, ploidy, thresholds, is_haploid_x_reference):
         GAIN(3) >=  +0.3
 
     """
-    absolutes = np.zeros(len(cnarr), dtype=np.float_)
+    absolutes = np.zeros(len(cnarr), dtype=np.float64)
     for idx, row in enumerate(cnarr):
         ref_copies = _reference_copies_pure(row.chromosome, ploidy, is_haploid_x_reference)
         if np.isnan(row.log2):
@@ -161,7 +161,7 @@ def absolute_clonal(cnarr, ploidy, purity, is_haploid_x_reference, diploid_parx_
 
 def absolute_pure(cnarr, ploidy, is_haploid_x_reference):
     """Calculate absolute copy number values from segment or bin log2 ratios."""
-    absolutes = np.zeros(len(cnarr), dtype=np.float_)
+    absolutes = np.zeros(len(cnarr), dtype=np.float64)
     for i, row in enumerate(cnarr):
         ref_copies = _reference_copies_pure(row.chromosome, ploidy, is_haploid_x_reference)
         absolutes[i] = _log2_ratio_to_absolute_pure(row.log2, ref_copies)

cnvlib/cnary.py

@@ -492,7 +492,7 @@ def expect_flat_log2(self, is_haploid_x_reference=None, diploid_parx_genome=None
         """
         if is_haploid_x_reference is None:
             is_haploid_x_reference = not self.guess_xx(diploid_parx_genome=diploid_parx_genome, verbose=False)
-        cvg = np.zeros(len(self), dtype=np.float_)
+        cvg = np.zeros(len(self), dtype=np.float64)
         if is_haploid_x_reference:
             # Single-copy X, Y
             idx = self.chr_x_filter(diploid_parx_genome).values | (self.chr_y_filter(diploid_parx_genome)).values

cnvlib/descriptives.py

@@ -21,7 +21,7 @@ def on_array(default=None):
     def outer(f):
         @wraps(f)
         def wrapper(a, **kwargs):
-            a = np.asfarray(a)
+            a = np.asarray(a, dtype=float)
             a = a[~np.isnan(a)]
             if not len(a):
                 return np.nan
@@ -52,8 +52,8 @@ def wrapper(a, w, **kwargs):
                 raise ValueError(f"Unequal array lengths: a={len(a)}, w={len(w)}")
             if not len(a):
                 return np.nan
-            a = np.asfarray(a)
-            w = np.asfarray(w)
+            a = np.asarray(a, dtype=float)
+            w = np.asarray(w, dtype=float)
             # Drop a's NaN indices from both arrays
             a_nan = np.isnan(a)
             if a_nan.any():

cnvlib/importers.py

@@ -69,7 +69,7 @@ def do_import_theta(segarr, theta_results_fname, ploidy=2):
         # Drop any segments where the C value is None
         mask_drop = np.array([c is None for c in copies], dtype="bool")
         segarr = segarr[~mask_drop].copy()
-        ok_copies = np.asfarray([c for c in copies if c is not None])
+        ok_copies = np.asarray([c for c in copies if c is not None], dtype=float)
         # Replace remaining segment values with these integers
         segarr["cn"] = ok_copies.astype("int")
         ok_copies[ok_copies == 0] = 0.5

cnvlib/reference.py

@@ -575,7 +575,7 @@ def get_fasta_stats(cnarr, fa_fname):
         calculate_gc_lo(subseq) for subseq in fasta_extract_regions(fa_fname, cnarr)
     ]
     gc_vals, rm_vals = zip(*gc_rm_vals)
-    return np.asfarray(gc_vals), np.asfarray(rm_vals)
+    return np.asarray(gc_vals, dtype=float), np.asarray(rm_vals, dtype=float)
 
 
 def calculate_gc_lo(subseq):

cnvlib/segmentation/haar.py

@@ -305,9 +305,9 @@ def HaarConv(
         logging.debug(
             "Error?: stepHalfSize (%s) > signalSize (%s)", stepHalfSize, signalSize
         )
-        return np.zeros(signalSize, dtype=np.float_)
+        return np.zeros(signalSize, dtype=np.float64)
 
-    result = np.zeros(signalSize, dtype=np.float_)
+    result = np.zeros(signalSize, dtype=np.float64)
     if weight is not None:
         # Init weight sums
         highWeightSum = weight[:stepHalfSize].sum()
@@ -490,7 +490,7 @@ def PulseConv(
     pulseHeight = 1.0 / pulseSize
 
     # Circular padding init
-    result = np.zeros(signalSize, dtype=np.float_)
+    result = np.zeros(signalSize, dtype=np.float64)
     for k in range((pulseSize + 1) // 2):
         result[0] += signal[k]
     for k in range(pulseSize // 2):

cnvlib/segmetrics.py

@@ -52,7 +52,7 @@ def do_segmetrics(
         for statname in location_stats:
             func = stat_funcs[statname]
             segarr[statname] = np.fromiter(
-                map(func, bins_log2s), np.float_, len(segarr)
+                map(func, bins_log2s), np.float64, len(segarr)
             )
     # Measures of spread
     if spread_stats:
@@ -62,7 +62,7 @@ def do_segmetrics(
         for statname in spread_stats:
             func = stat_funcs[statname]
             segarr[statname] = np.fromiter(
-                map(func, deviations), np.float_, len(segarr)
+                map(func, deviations), np.float64, len(segarr)
             )
     # Interval calculations
     weights = cnarr["weight"]
@@ -137,7 +137,7 @@ def confidence_interval_bootstrap(
         samples = _smooth_samples_by_weight(values, samples)
     # Recalculate segment means
     seg_means = (np.average(val, weights=wt) for val, wt in samples)
-    bootstrap_dist = np.fromiter(seg_means, np.float_, bootstraps)
+    bootstrap_dist = np.fromiter(seg_means, np.float64, bootstraps)
     alphas = np.array([alpha / 2, 1 - alpha / 2])
     if not smoothed:
         # alphas = _bca_correct_alpha(values, weights, bootstrap_dist, alphas)

cnvlib/smoothing.py

@@ -16,7 +16,7 @@ def check_inputs(x, width, as_series=True, weights=None):
     whole window. The output half-window size is truncated to the length of `x`
     if needed.
     """
-    x = np.asfarray(x)
+    x = np.asarray(x, dtype=float)
     wing = _width2wing(width, x)
     signal = _pad_array(x, wing)
     if as_series:
@@ -63,21 +63,21 @@ def rolling_median(x, width):
     rolled = signal.rolling(2 * wing + 1, 1, center=True).median()
     # if rolled.hasnans:
     #     rolled = rolled.interpolate()
-    return np.asfarray(rolled[wing:-wing])
+    return np.asarray(rolled[wing:-wing], dtype=float)
 
 
 def rolling_quantile(x, width, quantile):
     """Rolling quantile (0--1) with mirrored edges."""
     x, wing, signal = check_inputs(x, width)
     rolled = signal.rolling(2 * wing + 1, 2, center=True).quantile(quantile)
-    return np.asfarray(rolled[wing:-wing])
+    return np.asarray(rolled[wing:-wing], dtype=float)
 
 
 def rolling_std(x, width):
     """Rolling quantile (0--1) with mirrored edges."""
     x, wing, signal = check_inputs(x, width)
     rolled = signal.rolling(2 * wing + 1, 2, center=True).std()
-    return np.asfarray(rolled[wing:-wing])
+    return np.asarray(rolled[wing:-wing], dtype=float)
 
 
 def convolve_weighted(window, signal, weights, n_iter=1):

scripts/guess_baits.py

@@ -44,7 +44,7 @@ def filter_targets(target_bed, sample_bams, procs, fasta):
         raise RuntimeError("Targets must be in BED format; try skg_convert.py")
     logging.info("Loaded %d candidate regions from %s", len(baits), target_bed)
     # Loop over BAMs to calculate weighted averages of bin coverage depths
-    total_depths = np.zeros(len(baits), dtype=np.float_)
+    total_depths = np.zeros(len(baits), dtype=np.float64)
     for bam_fname in sample_bams:
         logging.info("Evaluating targets in %s", bam_fname)
         sample = cnvlib.do_coverage(target_bed, bam_fname, processes=procs, fasta=fasta)

skgenome/intersect.py

@@ -60,7 +60,7 @@ def into_ranges(
         elem = source[src_col].iat[0]
         if isinstance(elem, (str, np.string_)):
             summary_func = join_strings
-        elif isinstance(elem, (float, np.float_)):
+        elif isinstance(elem, (float, np.float64)):
             summary_func = np.nanmedian
         else:
             summary_func = first_of