Move some PyAna submodules to PyAnaMisc pkg with empty __init__.py

src/simplebuild_dgcode/data/pkgs/PyAnalysis/PyAna/pkg.info

@@ -1,4 +1,4 @@
-package( USEPKG Units USEEXT Numpy )
+package( USEPKG Units PyAnaMisc USEEXT Numpy )
 
 ######################################################################
 

src/simplebuild_dgcode/data/pkgs/PyAnalysis/PyAna/python/__init__.py

@@ -71,7 +71,7 @@ def kh(evt):
 
 #3) disable FPE's certain matplotlib function calls (we don't care too
 #   much about FPE's in other peoples gui/plotting code):
-from PyAna.fpe import standardMPLFixes as _standardMPLFixes
+from PyAnaMisc.fpe import standardMPLFixes as _standardMPLFixes
 _standardMPLFixes()
 
 #4) Make all legends draggable by default (unless new kw notdraggable=True) and set default numpoints=1 :

src/simplebuild_dgcode/data/pkgs/PyAnalysis/PyAna/python/_fix_backend_gtk.py

@@ -1,28 +1 @@
-#To avoid confusing users with useless(?) warnings such as the following:
-#
-#/usr/lib64/python2.7/site-packages/matplotlib/backends/backend_gtk.py:250: Warning: Source ID 2 was not found when attempting to remove it
-#  gobject.source_remove(self._idle_event_id)
-#
-#we monkey-patch FigureCanvasGTK.destroy and block stderr while it is invoked.
-#
-#Hopefully in some future version of matplotlib this warning will disappear by itself.
-import sys,os
-try:
-    from matplotlib.backends.backend_gtk import FigureCanvasGTK
-    if hasattr(FigureCanvasGTK,'destroy'):
-        _FigureCanvasGTK_destroy = FigureCanvasGTK.destroy
-        def patched_FigureCanvasGTK_destroy(self):
-            _stderr = sys.stderr
-            null = open(os.devnull,'wb')
-            try:
-                sys.stderr.flush()
-                sys.stderr = null
-                _FigureCanvasGTK_destroy(self)
-            finally:
-                sys.stderr.flush()
-                sys.stderr = _stderr
-                null.close()
-        FigureCanvasGTK.destroy=patched_FigureCanvasGTK_destroy
-except ImportError:
-    #Something unexpected, but let us not break stuff for users because of it!
-    pass
+import PyAnaMisc._fix_backend_gtk

src/simplebuild_dgcode/data/pkgs/PyAnalysis/PyAna/python/dyncmap.py

@@ -1,118 +1 @@
-from __future__ import division
-__all__=['dynamic_2d_colormap']
-
-import numpy as np
-from matplotlib.colors import LinearSegmentedColormap
-
-def dynamic_2d_colormap(data,imgobj=None,ncols=65536):
-    #attempt to use high-precision floating point for intermediate calculations:
-    try:
-        import decimal
-        _=decimal.Context(prec=999)
-        hpfloat,hpabs,hpmin,hpmax=_.create_decimal,_.abs,_.min,_.max
-        hpceil = lambda x : x.to_integral_value(rounding=decimal.ROUND_CEILING)
-    except ImportError:
-        import math
-        hpfloat,hpabs,hpmin,hpmax,hpceil=float,abs,min,max,math.ceil
-
-    hpncols=hpfloat(ncols)
-    zero,one=hpfloat(0.0),hpfloat(1.0)
-    #figure out limits (remember that all values in data might be masked):
-    rawmin = np.min(data)
-    rawmax = np.max(data)
-    if rawmin is np.ma.masked and rawmax is np.ma.masked:
-        rawmin,rawmax = -one, one
-    datamin = hpfloat(rawmin)
-    datamax = hpfloat(rawmax)
-    ddata=datamax-datamin
-    if ddata:
-        datamin -= hpfloat(0.005)*hpabs(ddata)
-        datamax += hpfloat(0.005)*hpabs(ddata)
-    else:
-        datamin -= hpfloat(0.5);
-        datamax += hpfloat(0.5);
-
-    #NB: In principle there is a bug here, ispos+isneg should be based on
-    #rawmin/rawmax not datamin/datamax. However, at least the code in
-    #NCSABVal/scripts/sabstudio is relying on the present behaviour, so we can't
-    #just fix it right away:
-    ispos=datamin>=zero
-    isneg=datamax<=zero
-    #ispos=rawmin>=zero
-    #isneg=rawmax<=zero
-
-    if ispos and datamin<zero:
-        datamin=zero
-    if isneg and datamax>zero:
-        datamax=zero
-    if not ispos and not isneg:
-        #make sure that 0.0 falls on a "bin-edge" in the colormap, so as to
-        #not let small positive values accidentally acquire blue values or
-        #vice versa:
-        assert datamax>zero and datamin<zero
-        d0=(datamax-datamin)/hpncols
-        m=hpmin(datamax,-datamin)
-        k=hpmax(hpceil(m/d0),4)#at least 4 bins on the smallest side
-        assert k<hpncols
-        eps=(k*(datamax-datamin)-hpncols*m)/(hpncols-k)
-        if datamax > -datamin: datamin -= eps
-        else: datamax += eps
-        # d=(datamax-datamin)/hpncols
-
-    #relative root-mean-square clipped to [0.0001,0.3]:
-    npmean=np.mean(np.square(data))
-    if npmean is np.ma.masked:
-        rms = hpfloat(0.1)
-    else:
-        rms=hpmax(hpfloat(0.0001),
-                  hpmin(hpfloat(0.3),
-                        hpfloat(np.sqrt(npmean))/(datamax-datamin)))
-    if datamin>=0.0:
-        cdict = {'red':   [(0.0,  0.10, 0.10),
-                           (float(rms),  0.7, 0.7),
-                           (float(3*rms),  1.0, 1.0),
-                           (1.0,  1.0, 1.0)],
-                 'green': [(0.0,  0.0, 0.0),
-                           (1.0,  1.0, 1.0)],
-                 'blue':  [(0.0,  0.0, 0.0),
-                           (1.0,  0.1, 0.1)]}
-    elif datamax<=0.0:
-        cdict = {'red':   [(0.0,  0.1, 0.1),
-                           (1.0,  0.0, 0.0)],
-                 'green': [(0.0,  1.0, 1.0),
-                           (1.0,  0.0, 0.0)],
-                 'blue':  [(0.0,  1.0, 1.0),
-                           (float(one-3*rms),  1.0, 1.0),
-                           (float(one-rms),  0.7, 0.7),
-                           (1.0,  0.1, 0.1)]}
-    else:
-        rneg = - datamin / ( datamax - datamin )
-        dbin = one/hpncols
-        #In rneg-dbin to rneg+dbin, we keep equal mix of red and blue, so
-        #numerical errors won't show e.g. very small positive values as blue or
-        #vice versa:
-        cdict = {'red':   [(0.0,  0.1, 0.1),
-                           (float(rneg-dbin),  0.0, 0.1),
-                           (float(rneg+dbin),  0.1, 0.1),
-                           (float(rneg+hpmax(hpfloat(2)*dbin,(one-rneg)*rms)),  0.7, 0.7),
-                           (float(rneg+hpmax(hpfloat(3)*dbin,(one-rneg)*rms*hpfloat(3))),  1.0, 1.0),
-                           (1.0,  1.0, 1.0)],
-                 'green': [(0.0,  1.0, 1.0),
-                           (float(rneg-dbin),  0.0, 0.0),
-                           (float(rneg+dbin),  0.0, 0.0),
-                           (1.0,  1.0, 1.0)],
-                 'blue':  [(0.0,  1.0, 1.0),
-                           (float(rneg-hpmax(hpfloat(3)*dbin,rneg*hpfloat(3)*rms)),  1.0, 1.0),
-                           (float(rneg-hpmax(hpfloat(2)*dbin,rneg*rms)),  0.7, 0.7),
-                           (float(rneg-dbin),  0.1, 0.1),
-                           (float(rneg+dbin),  0.1, 0.0),
-                           (1.0,  0.1, 0.1)]}
-        #for k,v in cdict.items():
-        #    print k,v
-    import matplotlib.colors
-    cmap=LinearSegmentedColormap('dynamic_rbcmap', cdict, N=ncols, gamma=1.0)
-    cmap.set_bad('black')#color of masked values (if any)
-    if imgobj:
-        imgobj.set_clim(float(datamin),float(datamax))
-        imgobj.set_cmap(cmap)
-    return float(datamin),float(datamax),cmap
+from PyAnaMisc.dyncmap import dynamic_2d_colormap

src/simplebuild_dgcode/data/pkgs/PyAnalysis/PyAnaMisc/pkg.info

@@ -0,0 +1,7 @@
+package()
+
+######################################################################
+
+Package providing standalone modules (empty __init__.py!) related to PyAna.
+
+primary author: thomas.kittelmann@ess.eu

src/simplebuild_dgcode/data/pkgs/PyAnalysis/PyAnaMisc/python/_fix_backend_gtk.py

@@ -0,0 +1,29 @@
+#To avoid confusing users with useless(?) warnings such as the following:
+#
+#/usr/lib64/python2.7/site-packages/matplotlib/backends/backend_gtk.py:250: Warning: Source ID 2 was not found when attempting to remove it
+#  gobject.source_remove(self._idle_event_id)
+#
+#we monkey-patch FigureCanvasGTK.destroy and block stderr while it is invoked.
+#
+#Hopefully in some future version of matplotlib this warning will disappear by itself.
+import sys
+import os
+try:
+    from matplotlib.backends.backend_gtk import FigureCanvasGTK
+    if hasattr(FigureCanvasGTK,'destroy'):
+        _FigureCanvasGTK_destroy = FigureCanvasGTK.destroy
+        def patched_FigureCanvasGTK_destroy(self):
+            _stderr = sys.stderr
+            null = open(os.devnull,'wb')
+            try:
+                sys.stderr.flush()
+                sys.stderr = null
+                _FigureCanvasGTK_destroy(self)
+            finally:
+                sys.stderr.flush()
+                sys.stderr = _stderr
+                null.close()
+        FigureCanvasGTK.destroy=patched_FigureCanvasGTK_destroy
+except ImportError:
+    #Something unexpected, but let us not break stuff for users because of it!
+    pass

src/simplebuild_dgcode/data/pkgs/PyAnalysis/PyAnaMisc/python/dyncmap.py

@@ -0,0 +1,117 @@
+__all__=['dynamic_2d_colormap']
+
+import numpy as np
+from matplotlib.colors import LinearSegmentedColormap
+
+def dynamic_2d_colormap(data,imgobj=None,ncols=65536):
+    #attempt to use high-precision floating point for intermediate calculations:
+    try:
+        import decimal
+        _=decimal.Context(prec=999)
+        hpfloat,hpabs,hpmin,hpmax=_.create_decimal,_.abs,_.min,_.max
+        hpceil = lambda x : x.to_integral_value(rounding=decimal.ROUND_CEILING)
+    except ImportError:
+        import math
+        hpfloat,hpabs,hpmin,hpmax,hpceil=float,abs,min,max,math.ceil
+
+    hpncols=hpfloat(ncols)
+    zero,one=hpfloat(0.0),hpfloat(1.0)
+    #figure out limits (remember that all values in data might be masked):
+    rawmin = np.min(data)
+    rawmax = np.max(data)
+    if rawmin is np.ma.masked and rawmax is np.ma.masked:
+        rawmin,rawmax = -one, one
+    datamin = hpfloat(rawmin)
+    datamax = hpfloat(rawmax)
+    ddata=datamax-datamin
+    if ddata:
+        datamin -= hpfloat(0.005)*hpabs(ddata)
+        datamax += hpfloat(0.005)*hpabs(ddata)
+    else:
+        datamin -= hpfloat(0.5);
+        datamax += hpfloat(0.5);
+
+    #NB: In principle there is a bug here, ispos+isneg should be based on
+    #rawmin/rawmax not datamin/datamax. However, at least the code in
+    #NCSABVal/scripts/sabstudio is relying on the present behaviour, so we can't
+    #just fix it right away:
+    ispos=datamin>=zero
+    isneg=datamax<=zero
+    #ispos=rawmin>=zero
+    #isneg=rawmax<=zero
+
+    if ispos and datamin<zero:
+        datamin=zero
+    if isneg and datamax>zero:
+        datamax=zero
+    if not ispos and not isneg:
+        #make sure that 0.0 falls on a "bin-edge" in the colormap, so as to
+        #not let small positive values accidentally acquire blue values or
+        #vice versa:
+        assert datamax>zero and datamin<zero
+        d0=(datamax-datamin)/hpncols
+        m=hpmin(datamax,-datamin)
+        k=hpmax(hpceil(m/d0),4)#at least 4 bins on the smallest side
+        assert k<hpncols
+        eps=(k*(datamax-datamin)-hpncols*m)/(hpncols-k)
+        if datamax > -datamin: datamin -= eps
+        else: datamax += eps
+        # d=(datamax-datamin)/hpncols
+
+    #relative root-mean-square clipped to [0.0001,0.3]:
+    npmean=np.mean(np.square(data))
+    if npmean is np.ma.masked:
+        rms = hpfloat(0.1)
+    else:
+        rms=hpmax(hpfloat(0.0001),
+                  hpmin(hpfloat(0.3),
+                        hpfloat(np.sqrt(npmean))/(datamax-datamin)))
+    if datamin>=0.0:
+        cdict = {'red':   [(0.0,  0.10, 0.10),
+                           (float(rms),  0.7, 0.7),
+                           (float(3*rms),  1.0, 1.0),
+                           (1.0,  1.0, 1.0)],
+                 'green': [(0.0,  0.0, 0.0),
+                           (1.0,  1.0, 1.0)],
+                 'blue':  [(0.0,  0.0, 0.0),
+                           (1.0,  0.1, 0.1)]}
+    elif datamax<=0.0:
+        cdict = {'red':   [(0.0,  0.1, 0.1),
+                           (1.0,  0.0, 0.0)],
+                 'green': [(0.0,  1.0, 1.0),
+                           (1.0,  0.0, 0.0)],
+                 'blue':  [(0.0,  1.0, 1.0),
+                           (float(one-3*rms),  1.0, 1.0),
+                           (float(one-rms),  0.7, 0.7),
+                           (1.0,  0.1, 0.1)]}
+    else:
+        rneg = - datamin / ( datamax - datamin )
+        dbin = one/hpncols
+        #In rneg-dbin to rneg+dbin, we keep equal mix of red and blue, so
+        #numerical errors won't show e.g. very small positive values as blue or
+        #vice versa:
+        cdict = {'red':   [(0.0,  0.1, 0.1),
+                           (float(rneg-dbin),  0.0, 0.1),
+                           (float(rneg+dbin),  0.1, 0.1),
+                           (float(rneg+hpmax(hpfloat(2)*dbin,(one-rneg)*rms)),  0.7, 0.7),
+                           (float(rneg+hpmax(hpfloat(3)*dbin,(one-rneg)*rms*hpfloat(3))),  1.0, 1.0),
+                           (1.0,  1.0, 1.0)],
+                 'green': [(0.0,  1.0, 1.0),
+                           (float(rneg-dbin),  0.0, 0.0),
+                           (float(rneg+dbin),  0.0, 0.0),
+                           (1.0,  1.0, 1.0)],
+                 'blue':  [(0.0,  1.0, 1.0),
+                           (float(rneg-hpmax(hpfloat(3)*dbin,rneg*hpfloat(3)*rms)),  1.0, 1.0),
+                           (float(rneg-hpmax(hpfloat(2)*dbin,rneg*rms)),  0.7, 0.7),
+                           (float(rneg-dbin),  0.1, 0.1),
+                           (float(rneg+dbin),  0.1, 0.0),
+                           (1.0,  0.1, 0.1)]}
+        #for k,v in cdict.items():
+        #    print k,v
+    import matplotlib.colors
+    cmap=LinearSegmentedColormap('dynamic_rbcmap', cdict, N=ncols, gamma=1.0)
+    cmap.set_bad('black')#color of masked values (if any)
+    if imgobj:
+        imgobj.set_clim(float(datamin),float(datamax))
+        imgobj.set_cmap(cmap)
+    return float(datamin),float(datamax),cmap

src/simplebuild_dgcode/data/pkgs/PyAnalysis/PyAnaMisc/python/fpe.py

@@ -0,0 +1,43 @@
+def disableFPEDuringCall(obj,fctname):
+    import Core.misc
+    if not Core.misc.is_debug_build():
+        return
+    orig = getattr(obj,fctname)
+    import Core.FPE
+    nofpectxmgr = Core.FPE.DisableFPEContextManager
+    def safefct(*args,**kwargs):
+        with nofpectxmgr():
+            return orig(*args,**kwargs)
+    setattr(obj,fctname,safefct)
+
+__standardFixesDone = [False]
+def standardMPLFixes():
+    global __standardFixesDone
+    if __standardFixesDone[0]:
+        return
+    __standardFixesDone[0] = True
+    import Core.misc
+    if not Core.misc.is_debug_build():
+        return
+
+    try:
+        import matplotlib.pyplot
+    except ImportError:
+        return#do nothing
+
+    import matplotlib.figure
+    import matplotlib.scale
+    try:
+        import matplotlib.backends.backend_pdf
+    except ImportError:
+        pass
+    disableFPEDuringCall(matplotlib.pyplot,'tight_layout')
+    disableFPEDuringCall(matplotlib.figure.Figure,'tight_layout')
+    disableFPEDuringCall(matplotlib.figure.Figure,'savefig')
+    disableFPEDuringCall(matplotlib.pyplot,'show')
+    disableFPEDuringCall(matplotlib.pyplot,'plot')
+    disableFPEDuringCall(matplotlib.pyplot,'savefig')
+    if hasattr(matplotlib.scale,'LogTransformBase'):
+        disableFPEDuringCall(matplotlib.scale.LogTransformBase,'transform_non_affine')
+    if hasattr(matplotlib.scale,'LogTransform'):
+        disableFPEDuringCall(matplotlib.scale.LogTransform,'transform_non_affine')

src/simplebuild_dgcode/data/pkgs/SimpleHists/SimpleHists/pkg.info

@@ -1,4 +1,4 @@
-package(USEPKG Utils USEEXT ZLib)
+package(USEPKG Utils PyAnaMisc USEEXT ZLib)
 
 ######################################################################
 

src/simplebuild_dgcode/data/pkgs/SimpleHists/SimpleHists/python/_backend_test.py

@@ -7,9 +7,9 @@
 if _backend_ok and _backend=='gtkagg':
     #attempt to silence some useless/confusing warnings
     try:
-        import PyAna._fix_backend_gtk # noqa F401
+        import PyAnaMisc._fix_backend_gtk # noqa F401
     except ImportError:
-        #perhaps PyAna package was not built, no biggie, users might get warnings.
+        #perhaps PyAnaMisc package was not built, no biggie, users might get warnings.
         pass
 
 if _backend_ok and _backend=='tkagg':
@@ -21,11 +21,7 @@
                                 message=('Treat the new Tool classes introduced in v1.5 as experimental'+
                                          ' for now, the API will likely change in version 2.1'))
 
-try:
-    from PyAna.fpe import standardMPLFixes as _standardMPLFixes # noqa E402
-except ModuleNotFoundError:
-    #fail silently, the PyAna pkg might be disabled
-    _standardMPLFixes = lambda : None
+from PyAnaMisc.fpe import standardMPLFixes as _standardMPLFixes # noqa E402
 _standardMPLFixes()
 
 def _ensure_backend_ok():