From 6538c18a80142aacc8f42fae7e4e6fad40876c5d Mon Sep 17 00:00:00 2001
From: Yngve Levinsen <yngve@pm.me>
Date: Thu, 22 Nov 2018 17:04:54 +0100
Subject: [PATCH] removing * imports

---
 ess/lib_tw.py | 37 ++++++++++++++++++-------------------
 1 file changed, 18 insertions(+), 19 deletions(-)

diff --git a/ess/lib_tw.py b/ess/lib_tw.py
index f19eb63..b58db76 100644
--- a/ess/lib_tw.py
+++ b/ess/lib_tw.py
@@ -11,8 +11,7 @@ from __future__ import print_function
 
 #---- Lib
 
-from numpy        import *
-from numpy.linalg import det
+import numpy
 
 from struct    import pack
 from itertools import chain
@@ -431,7 +430,7 @@ class PARTRAN:
                 lin=lin.split()
                 if flag  ==1     : data.append(map(float,lin))
                 if '##' in lin[0]: flag=1
-            data=array(data).transpose()
+            data=numpy.array(data).transpose()
 
         # Instances
         self.s    =data[idx_s    ]
@@ -450,7 +449,7 @@ class PARTRAN:
         
         # Additional instances
         self.gamma= data[idx_gamma]+1.0
-        self.beta = sqrt(1.0-1.0/self.gamma**2)
+        self.beta = numpy.sqrt(1.0-1.0/self.gamma**2)
         self.z    =-self.phs*self.beta*(c/freq*1e5)/360.0
         self.betx = self.sigx**2/self.epsx*self.beta*self.gamma
         self.bety = self.sigy**2/self.epsy*self.beta*self.gamma
@@ -507,7 +506,7 @@ class DST:
             mass =fromfile(file,dtype=float64,count=1      )[0]
 
         # Adjust units
-        gamma=1.0+x[5]/mass; beta=sqrt(1-1/gamma**2)
+        gamma=1.0+x[5]/mass; beta=numpy.sqrt(1-1/gamma**2)
         if unit_x =='mm'  : x[0]= x[0]*1e1; x[2]=x[2]*1e1
         if unit_px=='mrad': x[1]= x[1]*1e3; x[3]=x[3]*1e3
         if unit_z =='deg' : x[4]= x[4]*180/pi
@@ -632,12 +631,12 @@ class DENSITY:
 
         #-- Take care ave and rms
 
-        cent_ave=cent_ave/Nrun; cent_rms=sqrt(cent_rms/Nrun)
-        sig_ave = sig_ave/Nrun; sig_rms =sqrt( sig_rms/Nrun)
-        eps_ave = eps_ave/Nrun; eps_rms =sqrt( eps_rms/Nrun)
+        cent_ave=cent_ave/Nrun; cent_rms=numpy.sqrt(cent_rms/Nrun)
+        sig_ave = sig_ave/Nrun; sig_rms =numpy.sqrt( sig_rms/Nrun)
+        eps_ave = eps_ave/Nrun; eps_rms =numpy.sqrt( eps_rms/Nrun)
         if Nptcl[0]>0:
-            loss_num_ave=1.0*array(loss_num_ave)/Nrun; loss_num_rms=sqrt(1.0*array(loss_num_rms)/Nrun)
-            loss_pow_ave=    array(loss_pow_ave)/Nrun; loss_pow_rms=sqrt(    array(loss_pow_rms)/Nrun)
+            loss_num_ave=1.0*numpy.array(loss_num_ave)/Nrun; loss_num_rms=numpy.sqrt(1.0*numpy.array(loss_num_rms)/Nrun)
+            loss_pow_ave=    numpy.array(loss_pow_ave)/Nrun; loss_pow_rms=numpy.sqrt(    numpy.array(loss_pow_rms)/Nrun)
 
         #-- Change units, m => mm, pi-m-rad => pi-mm-mrad
 
@@ -652,15 +651,15 @@ class DENSITY:
         #-- Define std (around to avoid sqrt(-eps))
 
         if Nrun>1:
-            cent_std=sqrt(around(cent_rms**2-cent_ave**2,12))
-            sig_std =sqrt(around( sig_rms**2- sig_ave**2,12))
-            eps_std =sqrt(around( eps_rms**2- eps_ave**2,12))
+            cent_std=numpy.sqrt(around(cent_rms**2-cent_ave**2,12))
+            sig_std =numpy.sqrt(around( sig_rms**2- sig_ave**2,12))
+            eps_std =numpy.sqrt(around( eps_rms**2- eps_ave**2,12))
             cent_std=nan_to_num(cent_std)  # Replace nan with 0
             sig_std =nan_to_num( sig_std)  # Replace nan with 0
             eps_std =nan_to_num( eps_std)  # Replace nan with 0
             if Nptcl[0]>0:
-                loss_num_std=sqrt(around(loss_num_rms**2-loss_num_ave**2,16))
-                loss_pow_std=sqrt(around(loss_pow_rms**2-loss_pow_ave**2,16))
+                loss_num_std=numpy.sqrt(around(loss_num_rms**2-loss_num_ave**2,16))
+                loss_pow_std=numpy.sqrt(around(loss_pow_rms**2-loss_pow_ave**2,16))
                 loss_num_std=nan_to_num(loss_num_std)  # Replace nan with 0
                 loss_pow_std=nan_to_num(loss_pow_std)  # Replace nan with 0
 
@@ -836,9 +835,9 @@ def x2twiss(x):
         2015.07.30
     '''
 
-    x  =[x_i-mean(x_i) for x_i in array(x).transpose()]
+    x  =[x_i-mean(x_i) for x_i in numpy.array(x).transpose()]
     sig=[[mean(x_i*x_k) for x_k in x] for x_i in x]
-    eps=[ sqrt(det(array(sig)[i:i+2][:,i:i+2])) for i in (0,2,4)]
+    eps=[ numpy.sqrt(numpy.linalg.det(numpy.array(sig)[i:i+2][:,i:i+2])) for i in (0,2,4)]
     bet=[ sig[i  ][i  ]/eps[i/2]                for i in (0,2,4)]
     alf=[-sig[i  ][i+1]/eps[i/2]                for i in (0,2,4)]
     gam=[ sig[i+1][i+1]/eps[i/2]                for i in (0,2,4)]
@@ -886,7 +885,7 @@ def loss_elem2den(s,loss,file_name_dt='',dlt_dt=5e-6):
     try:
         # DTL cell and DT lengths
         with open(file_name_dt) as file:
-            L_cell,L_dt=array([map(float,lin.split()) for lin in file.readlines()]).transpose()[:2]
+            L_cell,L_dt=numpy.array([map(float,lin.split()) for lin in file.readlines()]).transpose()[:2]
         # Replace cell lengths with DT lengths
         Ndt=0
         for i in range(len(L)):
@@ -923,7 +922,7 @@ def loss_elem2den(s,loss,file_name_dt='',dlt_dt=5e-6):
     try:
         # Read DTL cell and DT lengths
         with open(file_name_dt) as file:
-            l_cell,l_dt=array([map(float,lin.split()) for lin in file.readlines()]).transpose()[:2]
+            l_cell,l_dt=numpy.array([map(float,lin.split()) for lin in file.readlines()]).transpose()[:2]
         # Replace cell lengths with DT lengths
         Ndt=0
         for i in range(len(l)):
-- 
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