Initial commit of PySSD. One working example in main.py using octupoles.

Author: like2000

Detuning.py

@@ -0,0 +1,33 @@
+import numpy as np
+
+class Detuning:
+    def __call__(self,jx,jy):
+        raise NotImplemented;
+
+class LinearDetuning(Detuning):
+    _startTune = 0.31;
+    _slopex = 0.0;
+    _slopey = 0.0;
+
+    def __init__(self,startTune,slopex,slopey):
+        self._startTune = startTune
+        self._slopex = slopex
+        self._slopey = slopey
+
+    def __call__(self,jx,jy):
+        return self._startTune + self._slopex*jx+self._slopey*jy
+
+
+class FootprintDetuning(Detuning):
+    _footprint = None;
+    _plane = None;
+
+    #0 for H and 1 for V
+    def __init__(self,footprint,plane=0):
+        self._footprint = footprint
+        self._plane = plane
+
+    def __call__(self,jx,jy):
+        sigx = np.sqrt(2.0*jx)
+        sigy = np.sqrt(2.0*jy)
+        return self._footprint.getTunesForAmpl(sigx,sigy)[self._plane]

Dispersion.py

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+class Dispersion:
+    _detuning = None;
+    _distribution = None;
+    _Q = 0.0;
+    _epsilon = 1E-6;
+
+    def __init__(self, distribution, detuning, Q, epsilon=1E-6):
+        self._distribution = distribution
+        self._detuning = detuning
+        self._Q = Q
+        self._epsilon = epsilon
+
+    def setEpsilon(self,epsilon):
+        self._epsilon = epsilon
+
+    def getEpsilon(self):
+        return self._epsilon
+
+    def getValue(self, jx, jy):
+
+        # DI = (
+        #      (jx * self._distribution.getDJx(jx,jy)) /
+        #      (complex(self._Q - self._detuning(jx,jy), self._epsilon))
+        #      )
+        DI = (
+            (jx * self._distribution.getDJx(jx, jy)) /
+            (self._Q - self._detuning(jx, jy) + 1j*self._epsilon)
+             )
+
+        return DI
+
+
+class RealDispersion(Dispersion):
+    def __call__(self,jx,jy):
+        return self.getValue(jx,jy).real
+
+
+class ImaginaryDispersion(Dispersion):
+    def __call__(self,jx,jy):
+        return self.getValue(jx,jy).imag

Distribution.py

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+import numpy as np
+from abc import ABCMeta, abstractmethod
+
+
+class Distribution(object):
+    __metaclass__ = ABCMeta
+
+    @abstractmethod
+    def getValue(self, jx, jy):
+        '''Return local pdf'''
+        pass
+
+    @abstractmethod
+    def getDJx(self, jx, jy):
+        '''Return derivation by Jx'''
+        pass
+
+    @abstractmethod
+    def getDJy(self, jx, jy):
+        '''Return derivation by Jy'''
+        pass
+
+
+class BiGaussian(Distribution):
+
+    def getValue(self, jx, jy):
+        '''Return pdf of normal bi-Gaussian'''
+        return np.exp(-(jx + jy));
+
+    def getDJx(self, jx, jy):
+        return -self.getValue(jx, jy);
+
+    def getDJy(self, jx, jy):
+        return -self.getValue(jx, jy);