From 1b66cf20d0f40741d89d39b901716341beeabeca Mon Sep 17 00:00:00 2001
From: Blaise Thompson <blaise@untzag.com>
Date: Mon, 16 Oct 2017 21:05:20 -0500
Subject: structure

---
 .../src/inhom.py                                   | 85 ----------------------
 1 file changed, 85 deletions(-)
 delete mode 100644 figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/inhom.py

(limited to 'figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/inhom.py')

diff --git a/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/inhom.py b/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/inhom.py
deleted file mode 100644
index 1438846..0000000
--- a/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/inhom.py	
+++ /dev/null
@@ -1,85 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Sat Jun 21 14:07:53 2014
-
-@author: Dan
-"""
-
-from NISE.lib.misc import *
-
-class Inhom():
-    # class contains the list of weights and sampling values to use
-    #--------------------------Recorded attributes--------------------------
-    out_vars = ['inhom_sampling', 'dist_params']
-    #--------------------------Methods--------------------------
-    def __init__(self, inhom_sampling=None, **dist_params):
-        """
-        generates the list of sampling points in the distribution and their weights
-        inhom dists should be normalized (int(f, dzeta) = 1.)
-        """
-        # inherit all class attributes unless kwargs has them; then use those 
-        # values.  if kwargs is not an Omega attribute, it gets ignored
-        for key, value in dist_params.items():
-            setattr(self, key, value)
-        #print self.__dict__.items()
-        # eliminating other quadrature methods; linear works best anyways
-        if inhom_sampling == 'linear':
-            # currently the only inhomogeneity parameter that can normalize well 
-            # in relation to the case of no inhomogeneity
-            if isinstance(dist_params.get('num'), int):
-                num = dist_params.get('num')
-            else:
-                try:
-                    num = int(num)
-                except TypeError:
-                    print 'no distribution sampling number specified; using 10 points as default'
-                    num = 10
-            if 'zeta_bound' in dist_params.keys():
-                zeta_bound = dist_params.get('zeta_bound')
-            else:
-                zeta_bound = 3
-            zeta = np.linspace(-zeta_bound, zeta_bound, num=num)
-            # need parameter 'sigma'
-            sigma = dist_params.get('sigma')
-            # scale our sampling intervals according to sigma
-            zeta = zeta * sigma
-            self.zweight = 1 / (np.sqrt(2*np.pi)*sigma) * np.exp(- 0.5 * ((zeta / sigma)**2))
-            self.dzeta = np.abs(zeta[1] - zeta[0])
-            self.zeta = zeta
-        elif inhom_sampling == 'rect':
-            w = dist_params.get('w')
-            if isinstance(dist_params.get('num'), int):
-                num = dist_params['num']
-            else:
-                try:
-                    num = int(num)
-                except TypeError:
-                    print 'no distribution sampling number specified; using 10 points as default'
-                    num = 10
-            self.zeta = np.linspace(-w,w,num=num)
-            self.dzeta = np.abs(self.zeta[1] - self.zeta[0])
-            self.zweight = np.ones(self.zeta.shape)
-        elif inhom_sampling == 'gh':
-            import NISE.hamiltonians.params.gauss_hermite as gh
-            # gaussian-hermite quadrature
-            # see http://en.wikipedia.org/wiki/Gauss%E2%80%93Hermite_quadrature
-            # for details
-            n = dist_params.get('n')
-            try:
-                gh.quad[n]
-            except KeyError:
-                print 'no table for quadrature of number {0} is available'.format(n)
-                print 'available quadrature numbers:  {0}'.format(str(gh.quad.keys()))
-            sigma = dist_params.get('sigma')
-            self.zeta = np.array(gh.quad[n])[0]
-            self.zweight = np.array(gh.quad[n])[1]
-            self.dzeta = 1.
-            # substitution to inhom variables yields the following scaling:
-            self.zeta*= np.sqrt(2) * sigma
-            self.zweight*= np.pi**-0.5
-        else:
-            self.zeta = np.array([0])
-            self.zweight = [1.0]
-            self.dzeta = 1.0
-        self.inhom_sampling = inhom_sampling
-        self.dist_params = dist_params
-- 
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