Use asarray

vasole · May 7, 2024 · 118f495 · 118f495
1 parent 259a6bf
commit 118f495
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Showing 6 changed files with 20 additions and 20 deletions.
diff --git a/PyMca5/PyMcaIO/JcampReader.py b/PyMca5/PyMcaIO/JcampReader.py
@@ -2,7 +2,7 @@
 #
 # The PyMca X-Ray Fluorescence Toolkit
 #
-# Copyright (c) 2004-2023 European Synchrotron Radiation Facility
+# Copyright (c) 2004-2024 European Synchrotron Radiation Facility
 #
 # This file is part of the PyMca X-ray Fluorescence Toolkit developed at
 # the ESRF.
@@ -231,7 +231,7 @@ def parseData(self, dataLines):
             yValues = values[1::2]
         xFactor = float(self.info.get("XFACTOR", 1.0))
         yFactor = float(self.info.get("YFACTOR", 1.0))
-        return x * xFactor, numpy.array(yValues, copy=False) * yFactor
+        return x * xFactor, numpy.asarray(yValues) * yFactor
 
     def parseDataOld(self, dataLines):
         if self.info['XYDATA'].upper().strip() not in ["(X++(Y..Y))", "(XY..XY)"]:

diff --git a/PyMca5/PyMcaMath/ImageListStats.py b/PyMca5/PyMcaMath/ImageListStats.py
@@ -1,5 +1,5 @@
 # /*##########################################################################
-# Copyright (C) 2023 European Synchrotron Radiation Facility
+# Copyright (C) 2023-2024 European Synchrotron Radiation Facility
 #
 # This file is part of the PyMca X-ray Fluorescence Toolkit developed at
 # the ESRF.
@@ -53,7 +53,7 @@ def arrayListMeanRatioAndMedianRatio(imageList, mask=None):
             goodIndex = numpy.isfinite(image0) & numpy.isfinite(image1)
             image0 = image0[goodIndex]
             image1 = image1[goodIndex]
-            mean_ratio = image0 / numpy.array(image1, copy=False, dtype=numpy.float64)
+            mean_ratio = image0 / numpy.asarray(image1, dtype=numpy.float64)
             goodIndex = numpy.isfinite(mean_ratio)
             mean_ratio = mean_ratio[goodIndex]
             median_ratio = numpy.median(mean_ratio)

diff --git a/PyMca5/PyMcaMath/SimpleMath.py b/PyMca5/PyMcaMath/SimpleMath.py
@@ -2,7 +2,7 @@
 #
 # The PyMca X-Ray Fluorescence Toolkit
 #
-# Copyright (c) 2004-2022 European Synchrotron Radiation Facility
+# Copyright (c) 2004-2024 European Synchrotron Radiation Facility
 #
 # This file is part of the PyMca X-ray Fluorescence Toolkit developed at
 # the ESRF.
@@ -44,8 +44,8 @@ class SimpleMath(object):
                        "SG smoothed 5 point"]
 
     def derivate(self,xdata,ydata, xlimits=None, option=None):
-        x=numpy.array(xdata, copy=False, dtype=numpy.float64)
-        y=numpy.array(ydata, copy=False, dtype=numpy.float64)
+        x=numpy.asarray(xdata, dtype=numpy.float64)
+        y=numpy.asarray(ydata, dtype=numpy.float64)
         if xlimits is not None:
             i1=numpy.nonzero((xdata>=xlimits[0])&\
                                (xdata<=xlimits[1]))[0]
@@ -211,7 +211,7 @@ def smooth(self, *var, **kw):
         else:
             ydata=self.y
         f=[0.25,0.5,0.25]
-        result=numpy.array(ydata, copy=False, dtype=numpy.float64)
+        result=numpy.asarray(ydata, dtype=numpy.float64)
         if len(result) > 1:
             result[1:-1]=numpy.convolve(result,f,mode=0)
             result[0]=0.5*(result[0]+result[1])

diff --git a/PyMca5/PyMcaMath/fitting/Gefit.py b/PyMca5/PyMcaMath/fitting/Gefit.py
@@ -2,7 +2,7 @@
 #
 # The PyMca X-Ray Fluorescence Toolkit
 #
-# Copyright (c) 2004-2016 European Synchrotron Radiation Facility
+# Copyright (c) 2004-2024 European Synchrotron Radiation Facility
 #
 # This file is part of the PyMca X-ray Fluorescence Toolkit developed at
 # the ESRF by the Software group.
@@ -107,7 +107,7 @@ def LeastSquaresFit(model, parameters0, data=None, maxiter = 100,constrains=None
     """
     if constrains is None:
         constrains = []
-    parameters = numpy.array(parameters0, dtype=numpy.float64, copy=False)
+    parameters = numpy.asarray(parameters0, dtype=numpy.float64)
     if linear is None:linear=0
     if deltachi is None:
         deltachi = 0.01
@@ -212,7 +212,7 @@ def LinearLeastSquaresFit(model0,parameters0,data0,maxiter,
             raise ValueError("Linear fit cannot handle quoted constraint")
     # make a local copy of the function for an easy speed up ...
     model = model0
-    parameters = numpy.array(parameters0, dtype=numpy.float64, copy=False)
+    parameters = numpy.asarray(parameters0, dtype=numpy.float64)
     if data0 is not None:
         selfx = numpy.array([x[0] for x in data0])
         selfy = numpy.array([x[1] for x in data0])
@@ -326,7 +326,7 @@ def RestreinedLeastSquaresFit(model0,parameters0,data0,maxiter,
                 raise ValueError("Unknown constraint %s" % constrains[0][i])
     # make a local copy of the function for an easy speed up ...
     model = model0
-    parameters = numpy.array(parameters0, dtype=numpy.float64, copy=False)
+    parameters = numpy.asarray(parameters0, dtype=numpy.float64)
     if ONED:
         data = numpy.array(data0)
         x = data[1:2,0]

diff --git a/PyMca5/PyMcaMath/linalg.py b/PyMca5/PyMcaMath/linalg.py
@@ -2,7 +2,7 @@
 #
 # The PyMca X-Ray Fluorescence Toolkit
 #
-# Copyright (c) 2004-2023 European Synchrotron Radiation Facility
+# Copyright (c) 2004-2024 European Synchrotron Radiation Facility
 #
 # This file is part of the PyMca X-ray Fluorescence Toolkit developed at
 # the ESRF.
@@ -229,8 +229,8 @@ def lstsq(a, b, rcond=None, sigma_b=None, weight=False,
     >>> plt.show()
 
     """
-    a = numpy.array(a, dtype=numpy.float64, copy=False)
-    b = numpy.array(b, dtype=numpy.float64, copy=False)
+    a = numpy.asarray(a, dtype=numpy.float64)
+    b = numpy.asarray(b, dtype=numpy.float64)
     a_shape = a.shape
     b_shape = b.shape
     original = b_shape
@@ -250,7 +250,7 @@ def lstsq(a, b, rcond=None, sigma_b=None, weight=False,
     if weight:
         if sigma_b is not None:
             # experimental uncertainties provided these are the ones to use (if any)
-            w = numpy.abs(numpy.array(sigma_b, dtype=numpy.float64, copy=False))
+            w = numpy.abs(numpy.asarray(sigma_b, dtype=numpy.float64))
             w = w + numpy.equal(w, 0)
             if w.size == b_shape[0]:
                 # same uncertainty for every spectrum

diff --git a/PyMca5/PyMcaMath/mva/KMeansModule.py b/PyMca5/PyMcaMath/mva/KMeansModule.py
@@ -2,7 +2,7 @@
 #
 # The PyMca X-Ray Fluorescence Toolkit
 #
-# Copyright (c) 2020-2023 European Synchrotron Radiation Facility
+# Copyright (c) 2020-2024 European Synchrotron Radiation Facility
 #
 # This file is part of the PyMca X-ray Fluorescence Toolkit developed at
 # the ESRF.
@@ -64,7 +64,7 @@
 def _labelCythonKMeans(x, k):
     labels, means, iterations, converged = _kmeans.kmeans(x, k)
     return {
-        "labels": numpy.array(labels, dtype=numpy.int32, copy=False),
+        "labels": numpy.asarray(labels, dtype=numpy.int32),
         "means": means,
         "iterations": iterations,
         "converged": converged,
@@ -79,7 +79,7 @@ def _labelMdp(x, k):
         classifier.train(x[i : i + 1])
     # classifier.train(x)
     labels = classifier.label(x)
-    return {"labels": numpy.array(labels, dtype=numpy.int32, copy=False)}
+    return {"labels": numpy.asarray(labels, dtype=numpy.int32)}
 
 
 def _labelScikitLearn(x, k):
@@ -91,7 +91,7 @@ def _labelScikitLearn(x, k):
     # labels = km.predict(x)
     converged = len(km.cluster_centers_) == len(labels)
     return {
-        "labels": numpy.array(labels, dtype=numpy.int32, copy=False),
+        "labels": numpy.asarray(labels, dtype=numpy.int32),
         "means": km.cluster_centers_,
         "iterations": km.n_iter_,
         "converged": converged,