Merge pull request #60 from MJOLNIRPackage/develop

Merged from Develop

.travis.yml

@@ -11,6 +11,7 @@ python:
   - 3.6
   - 3.7
   - 3.8
+  - 3.9
 matrix:
   include:      
     - name: "Python 3.7.5 on macOS 10.14"
@@ -26,9 +27,22 @@ matrix:
         - wget -UseBasicParsing 'https://www.psi.ch/en/media/60041/download'
         - unzip download
       script: py.test test/ -vv
-    - name: "Python 3.8.0 on macOS 10.14"
+    - name: "Python 3.8 on macOS 10.15.5"
       os: osx
-      osx_image: xcode11.3  # Python 3.8.0 running on macOS 10.14.6
+      osx_image: xcode11.6  # Python 3.8 running on macOS 10.15.5
+      language: shell       # 'language: python' is an error on Travis CI macOS
+      before_install:
+        - python3 --version
+        - sleep 3 # give xvfb some time to start
+        - pip3 install -U pip
+        - pip install pytest>=3.6 pycodestyle python-coveralls pytest-cov>=2.5 coveralls
+        - pip install .
+        - wget -UseBasicParsing 'https://www.psi.ch/en/media/60041/download'
+        - unzip download
+      script: py.test test/ -vv
+    - name: "Python 3.9 on macOS 10.15.7"
+      os: osx
+      osx_image: xcode12.2  # Python 3.9 running on macOS 10.15.7
       language: shell       # 'language: python' is an error on Travis CI macOS
       before_install:
         - python3 --version
@@ -96,6 +110,20 @@ matrix:
         - curl -o download.zip https://www.psi.ch/en/media/60041/download
         - 7z e download.zip "-osamlpedata"
       env: PATH=/c/Python38:/c/Python38/Scripts:$PATH
+    - name: "Python 3.9.0 on Windows"
+      os: windows           # Windows 10.0.17134 N/A Build 17134
+      language: shell       # 'language: python' is an error on Travis CI Windows
+      before_install:
+        - choco install python --version 3.9.0
+        - python --version
+        - sleep 3 # give xvfb some time to start
+        - python -m pip install --upgrade pip
+        - pip3 install pytest>=3.6 pycodestyle python-coveralls pytest-cov>=2.5 coveralls
+        - python setup.py sdist
+        - pip3 install .
+        - curl -o download.zip https://www.psi.ch/en/media/60041/download
+        - 7z e download.zip "-osamlpedata"
+      env: PATH=/c/Python39:/c/Python39/Scripts:$PATH
 before_install:
   - pip install pytest>=3.6 pycodestyle python-coveralls pytest-cov>=2.5 coveralls
   - pip install --upgrade pip

MJOLNIR/Data/DataFile.py

@@ -11,7 +11,7 @@
 import MJOLNIR
 import datetime
 import math
-import shapely
+#import shapely
 # from shapely.geometry import Polygon as PolygonS, Point as PointS
 from MJOLNIR import TasUBlibDEG as TasUBlib
 from MJOLNIR._tools import Marray

MJOLNIR/Data/DataSet.py

@@ -3128,20 +3128,18 @@ def undoAbsolutNormalize(self):
 
 
 
-    def absolutNormalize(self,sampleMass,sampleChemicalFormula,formulaUnitsPerUnitCell=1.0,
+    def absolutNormalize(self,sampleMass=None,sampleMolarMass=None,sampleChemicalFormula=None,formulaUnitsPerUnitCell=1.0,
                          sampleGFactor=2.0, correctVanadium=False,vanadiumMass=15.25,
                          vanadiumMonitor=100000,vanadiumSigmaIncoherent=5.08,vanadiumChemicalFormula='V',vanadiumGFactor=2.0,
-                         vanadiumUnitsPerUnitCell=1.0):
+                         vanadiumUnitsPerUnitCell=1.0,vanadiumMolarMass=None):
         """Normaliza dataset to absolut units () by 
 
-        Args:
+        Kwargs:
 
             - sampleMass (float): Mass of sample in gram
 
             - sampleChemicalFormula (string): Chemical formula of sample
 
-        Kwargs:
-
             - formulaUnitsPerUnitCell (float): Number of formula units per unit cell (default 1.0)
 
             - sampleDebyeWaller (float): Debye Waller factor (default 1.0)
@@ -3166,10 +3164,10 @@ def absolutNormalize(self,sampleMass,sampleChemicalFormula,formulaUnitsPerUnitCe
 
 
         normFactor = \
-        _tools.calculateAbsolutNormalization(sampleChemicalFormula=sampleChemicalFormula,sampleMass=sampleMass,
+        _tools.calculateAbsolutNormalization(sampleChemicalFormula=sampleChemicalFormula,sampleMolarMass=sampleMolarMass,sampleMass=sampleMass,
                                              formulaUnitsPerUnitCell=formulaUnitsPerUnitCell,sampleGFactor=sampleGFactor,
-                                             correctVanadium=correctVanadium,vanadiumMass=vanadiumMass,
-                                             vanadiumMonitor=vanadiumMonitor,vanadiumSigmaIncoherent=vanadiumSigmaIncoherent,
+                                             correctVanadium=correctVanadium,vanadiumMass=vanadiumMass,vanadiumChemicalFormula=vanadiumChemicalFormula,
+                                             vanadiumMonitor=vanadiumMonitor,vanadiumSigmaIncoherent=vanadiumSigmaIncoherent,vanadiumMolarMass=vanadiumMolarMass,
                                              vanadiumGFactor=vanadiumGFactor,vanadiumUnitsPerUnitCell=vanadiumUnitsPerUnitCell)
 
         if self.absolutNormalized != 0:

MJOLNIR/Data/Sample.py

@@ -14,7 +14,6 @@
 from MJOLNIR._tools import Marray
 import MJOLNIR.Data.DataFile
 
-factorsqrtEK = 0.694692
 
 def cosd(x):
     return np.cos(np.deg2rad(x))

MJOLNIR/Geometry/Instrument.py

@@ -7,6 +7,7 @@
 from MJOLNIR import _tools
 from MJOLNIR import TasUBlibDEG as TasUBlib
 from MJOLNIR.TasUBlibDEG import cosd,sind
+from MJOLNIR.Data import Sample,RLUAxes
 import warnings
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
@@ -1304,85 +1305,119 @@ def makeMonitor(entry,Numpoints):
 
 
 def prediction(A3Start,A3Stop,A3Steps,A4Positions,Ei,Cell,r1,r2,points=False):
-
-    cell = TasUBlib.calcCell(Cell)
-
-    UB = TasUBlib.calcTasUBFromTwoReflections(cell, r1, r2)
+    s = Sample.Sample(*Cell,projectionVector1=r1,projectionVector2=r2)
+
+    class simpleDataSet():
+        def __init__(self,sample):
+            self.sample = [sample]
 
-    pV1 = _tools.LengthOrder(np.array(r2[:3],dtype=float))
-    pV2 = _tools.LengthOrder(np.array(r1[:3],dtype=float))
+
+    def converterToQxQy(A3,A4,Ei,Ef):
 
-    convertionMatrix = np.dot(np.dot([[1,0,0],[0,1,0]],UB),np.array([pV1,pV2]).T)
+        ki = np.sqrt(Ei)*_tools.factorsqrtEK
+        kf = np.sqrt(Ef)*_tools.factorsqrtEK
 
-
-    factorsqrtEK = 0.694692
-
-    def converterToP1P2(A3,A4,Ei,Ef, convertionMatrix = convertionMatrix):
-
-        ki = np.sqrt(Ei)*factorsqrtEK
-        kf = np.sqrt(Ef)*factorsqrtEK
-
         r = [0,0,0,A3,A4,0.0,0.0,Ei,Ef]
         QV = TasUBlib.calcTasUVectorFromAngles(r)
         q = np.sqrt(ki**2 +kf**2-
-               2. *ki *kf * np.cos(np.deg2rad(A4)))
-    
+            2. *ki *kf * np.cos(np.deg2rad(A4)))
+
 
         Qx,Qy = (QV*q.reshape(-1,1))[:,:2].T
-        convertionMatrixINV = np.linalg.inv(convertionMatrix)
-        proj1,proj2 = np.einsum('ij,j...->i...',convertionMatrixINV,[Qx,Qy])
-        return (proj1,proj2)
-
-    fig,Ax = plt.subplots(3,3,figsize=(26,13))
-    Ax = Ax.flatten()
+        return (Qx,Qy)
+
+    def converterToA3A4(Qx,Qy, Ei,Ef,A3Off=0.0):
+        Qx = np.asarray(Qx)
+        Qy = np.asarray(Qy)
+
+        QC = np.array([Qx,Qy])
+        q = np.linalg.norm(QC)
+
+        U1V = np.array([Qx.flatten(),Qy.flatten(),0.0],dtype=float)
+
+        U1V/=np.linalg.norm(U1V)
+        U2V = np.array([0.0,0.0,1.0],dtype=float)
+
+
+        TV = TasUBlib.buildTVMatrix(U1V, U2V)
+        R = np.linalg.inv(TV)
+
+        ss = 1.0
+
+        cossgl = np.sqrt(R[0,0]*R[0,0]+R[1,0]*R[1,0])
+        om = TasUBlib.arctan2d(R[1,0]/cossgl, R[0,0]/cossgl)
+
+        ki = np.sqrt(Ei)*_tools.factorsqrtEK
+        kf = np.sqrt(Ef)*_tools.factorsqrtEK
+
+        cos2t =(ki**2 + kf**2 - q**2) / (2. * np.abs(ki) * np.abs(kf))
+
+        A4 = ss*TasUBlib.arccosd(cos2t)
+        theta = TasUBlib.calcTheta(ki, kf, A4)
+        A3 = -om + -ss*theta + A3Off
+        return A3,-A4
+
+
+    t = simpleDataSet(s)
+    fig = plt.figure(figsize=(26,13))
+    Ax = [RLUAxes.createRLUAxes(t,figure=fig,ids=(3,3,i+1)) for i in range(9)]
 
     MJOLNIRDir = os.path.dirname(_tools.__file__)
     calibPath = os.path.join(MJOLNIRDir,'Normalization_1.calib')
-    
+
     calib = np.loadtxt(calibPath,delimiter=',',skiprows=3)
-    
+
     A4Instrument = calib[:,-1].reshape(104,8)
     EfInstrument = calib[:,4].reshape(104,8)
     EfInstrument[np.isclose(EfInstrument,0.0)]=np.nan
-    A3PointValues = np.linspace(A3Start,A3Stop,A3Steps)
+
+    A3PointValues = np.linspace(A3Start,A3Stop,A3Steps)-s.theta
     steps = A3Steps
     if steps>30:
         steps = 30
-    A3Values = np.linspace(A3Start,A3Stop,steps)
-
-
-    startColor = np.array([0.12156863, 0.46666667, 0.70588235, 0.5])
-    endColor = np.array([0.83921569, 0.15294118, 0.15686275, 0.5])
+    A3Values = np.linspace(A3Start,A3Stop,steps)-s.theta
+
+    endColor = np.array([0.12156863, 0.46666667, 0.70588235, 0.5])
+    startColor = np.array([0.83921569, 0.15294118, 0.15686275, 0.5])
     diff = (endColor-startColor)/7.0
-
 
-    def format_axes(x,y,pV1,pV2):
-        return '['+', '.join(['{:.3f}'.format(x) for x in np.asarray(x)*pV1+np.asarray(y)*pV2])+'] RLU'
+    def format_axes_func(self,x,y,Ei,Ef,A3Off=s.theta):
+        A3,A4 = converterToA3A4(x,y,Ei,Ef,-A3Off)
+        return ax.sample.format_coord(x,y)+', A3 = {:.2f} deg, A4 = {:.2f} deg, Ef = {:.2f}'.format(A3,A4,Ef)
 
     for i,(ax,Ef,A4) in enumerate(zip(Ax,EfInstrument.reshape(104,8).T,A4Instrument.reshape(104,8).T)):
+        ax.Ef = np.nanmean(Ef)
         for A4Position in A4Positions:
             if points:
                 for A3 in A3PointValues:
-                    H,L = converterToP1P2(A3=A3, A4=A4+A4Position,Ei = Ei, Ef = Ef)
+                    H,L = converterToQxQy(A3=A3, A4=A4+A4Position,Ei = Ei, Ef = Ef)
                     ax.scatter(H,L,color=startColor[:-1]+diff[:-1]*i)
+
             A4Edge = np.array([np.concatenate([a4,[a4[-1]]*steps,a4[::-1],[a4[0]]*steps]) for a4 in A4.reshape(8,13)])
             EfEdge = np.array([np.concatenate([ef,[ef[-1]]*steps,ef[::-1],[ef[0]]*steps]) for ef in Ef.reshape(8,13)])
             A3Edge = np.array(list(np.concatenate([[A3Values[0]]*13,A3Values,[A3Values[-1]]*13,A3Values[::-1]]))*8).reshape(8,-1)
-            Hedge,Ledge = np.array([converterToP1P2(A3=a3, A4=a4+A4Position,Ei = Ei, Ef = ef) for a3,a4,ef in zip(A3Edge,A4Edge,EfEdge)]).transpose([1,0,2])
+            Hedge,Ledge = np.array([converterToQxQy(A3=a3, A4=a4+A4Position,Ei = Ei, Ef = ef) for a3,a4,ef in zip(A3Edge,A4Edge,EfEdge)]).transpose([1,0,2])
             [a.plot(hedge,ledge,color=startColor+diff*i) for hedge,ledge in zip(Hedge,Ledge) for a in [Ax[-1],ax]]
-    
+
 
-        ax.set_xlabel(_tools.generateLabelDirection(pV1) + ' RLU')
-        ax.set_ylabel(_tools.generateLabelDirection(pV2) + ' RLU')
-        ax.set_title(r'$\Delta $E {:.2f}'.format(Ei-np.nanmean(Ef))+' meV')
-        ax.invert_yaxis()
-        ax.format_coord = lambda x,y: format_axes(x, y, pV1,  pV2)
-
-    Ax[-1].set_xlabel(_tools.generateLabelDirection(pV1) + ' RLU')
-    Ax[-1].set_ylabel(_tools.generateLabelDirection(pV2) + ' RLU')
+        ax.set_xlabel(_tools.generateLabelDirection(s.projectionVector1) + ' RLU')
+        ax.set_ylabel(_tools.generateLabelDirection(s.projectionVector2) + ' RLU')
+        ax.set_title(r'$\Delta $E {:.2f}'.format(Ei-np.nanmean(Ef))+' meV, E$_f$ {:.2f} meV'.format(np.nanmean(Ef)))
+
+
+    Ax[-1].set_xlabel(_tools.generateLabelDirection(s.projectionVector1) + ' RLU')
+    Ax[-1].set_ylabel(_tools.generateLabelDirection(s.projectionVector2) + ' RLU')
     Ax[-1].set_title('All Planes')
-    Ax[-1].invert_yaxis()
-    Ax[-1].format_coord = lambda x,y: format_axes(x, y, pV1,  pV2)
-
+
     fig.tight_layout()
+    Ax[0].format_coord = lambda x,y: format_axes_func(Ax[0],x,y,Ei,np.nanmean(EfInstrument,axis=0)[0],-s.theta)
+    Ax[1].format_coord = lambda x,y: format_axes_func(Ax[1],x,y,Ei,np.nanmean(EfInstrument,axis=0)[1],-s.theta)
+    Ax[2].format_coord = lambda x,y: format_axes_func(Ax[2],x,y,Ei,np.nanmean(EfInstrument,axis=0)[2],-s.theta)
+    Ax[3].format_coord = lambda x,y: format_axes_func(Ax[3],x,y,Ei,np.nanmean(EfInstrument,axis=0)[3],-s.theta)
+    Ax[4].format_coord = lambda x,y: format_axes_func(Ax[4],x,y,Ei,np.nanmean(EfInstrument,axis=0)[4],-s.theta)
+    Ax[5].format_coord = lambda x,y: format_axes_func(Ax[5],x,y,Ei,np.nanmean(EfInstrument,axis=0)[5],-s.theta)
+    Ax[6].format_coord = lambda x,y: format_axes_func(Ax[6],x,y,Ei,np.nanmean(EfInstrument,axis=0)[6],-s.theta)
+    Ax[7].format_coord = lambda x,y: format_axes_func(Ax[7],x,y,Ei,np.nanmean(EfInstrument,axis=0)[7],-s.theta)
+    fig.tight_layout()
+
     return Ax

MJOLNIR/__init__.py

@@ -8,8 +8,10 @@
 import sys,os
 sys.path.append('.')
 
-__version__ = '1.1.16'
+__version__ = '1.1.17'
 __author__ = 'Jakob Lass'
 
 __multiFLEXXNormalization__ = os.path.join(os.path.dirname(__file__),'CalibrationMultiFLEXX.csv') 
 __flatConeNormalization__ = os.path.join(os.path.dirname(__file__),'CalibrationFlatCone.csv')
+__CAMEANormalization__ = os.path.join(os.path.dirname(__file__),'Normalization_1.calib')
+