Adopt petrolib.data_qc_io in 2015-2019 articles (PR-B1)

src2015_04/article4_microresistivity_extraction.py

@@ -37,9 +37,7 @@
 
 def histogram_scale(image, ref_min, ref_max):
     """Linear min-max histogram scaling of an image array onto [ref_min, ref_max]."""
-    img = np.asarray(image, float)
-    lo, hi = np.min(img), np.max(img)
-    return ref_min + (img - lo) * (ref_max - ref_min) / (hi - lo)
+    return petrolib.data_qc_io.scale.normalize_to_reference(image, ref_min, ref_max, pct=None)
 
 
 def median_curve(image_array):

src2016_02/article3_low_permeability_measurements.py

@@ -112,7 +112,7 @@ def deviation_indicator(x1, x2):
     the fractional discrepancy used to compare permeability/porosity from
     different techniques.
     """
-    return 2.0 * abs(x1 - x2) / (x1 + x2)
+    return float(petrolib.data_qc_io.clean.relative_discrepancy(x1, x2))
 
 
 # ---------------------------------------------- tests --------------

src2016_12/article6_gr_normalization_haynesville.py

@@ -26,13 +26,20 @@
 
 import numpy as np
 
+try:
+    import petrolib
+except ImportError:  # bare clone, not installed
+    import sys, pathlib
+    sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1]))
+    import petrolib
+
 
 # ---------------------------------------------- normalization --------------
 
 def histogram_normalize(gr, gr_min, gr_max, ref_min, ref_max):
     """Linear histogram normalization of a GR log onto a reference [ref_min, ref_max]."""
-    gr = np.asarray(gr, float)
-    return ref_min + (gr - gr_min) * (ref_max - ref_min) / (gr_max - gr_min)
+    return petrolib.data_qc_io.scale.normalize_to_reference(
+        gr, ref_min, ref_max, in_lo=gr_min, in_hi=gr_max)
 
 
 def tst_projection(gr_vertical, tst_vertical, tst_query):
@@ -47,8 +54,7 @@ def affine_normalize(gr_raw, target_mean, target_std):
 
         GR_norm = (GR_raw - mean)/std * target_std + target_mean.
     """
-    gr = np.asarray(gr_raw, float)
-    return (gr - gr.mean()) / gr.std() * target_std + target_mean
+    return petrolib.data_qc_io.scale.match_moments(gr_raw, target_mean, target_std)
 
 
 def max_percent_shift(gr_raw, gr_norm):

src2017_10/article5_permian_core_analysis.py

@@ -65,7 +65,7 @@ def hydrocarbon_pore_volume(phi, sw):
 
 def relative_discrepancy(value_a, value_b):
     """Relative discrepancy between two method results  |a - b|/mean(a, b)."""
-    return abs(value_a - value_b) / (0.5 * (value_a + value_b))
+    return float(petrolib.data_qc_io.clean.relative_discrepancy(value_a, value_b))
 
 
 # ---------------------------------------------- tests --------------

src2018_12/article11_data_preconditioning.py

@@ -50,17 +50,12 @@ def zscore_outliers(x, thresh=3.0):
 
 def iqr_outliers(x, k=1.5):
     """Boolean mask of IQR outliers (outside Q1 - k*IQR, Q3 + k*IQR)."""
-    x = np.asarray(x, float)
-    q1, q3 = np.percentile(x, [25, 75]); iqr = q3 - q1
-    return (x < q1 - k * iqr) | (x > q3 + k * iqr)
+    return petrolib.data_qc_io.clean.outlier_mask(x, "iqr", k=k)
 
 
 def impute_gaps(x):
     """Fill NaN gaps by linear interpolation over the index."""
-    x = np.asarray(x, float).copy()
-    idx = np.arange(len(x)); good = ~np.isnan(x)
-    x[~good] = np.interp(idx[~good], idx[good], x[good])
-    return x
+    return petrolib.data_qc_io.clean.impute_gaps(x)
 
 
 # ---------------------------------------------- tests --------------

src2019_02/article4_insitu_saturation_core_comparison.py

@@ -60,7 +60,7 @@ def reconstruct_insitu(so_measured, sg_measured, retention):
 
 def closure(sw, so, sg):
     """Saturation closure residual  Sw + So + Sg - 1 (should be ~0)."""
-    return sw + so + sg - 1.0
+    return float(petrolib.data_qc_io.clean.closure_residual(sw, so, sg))
 
 
 # ---------------------------------------------- tests --------------