From 7fd76e5d9916a51c4015a560de4935fdb9d12df0 Mon Sep 17 00:00:00 2001
From: Yngve Inntjore Levinsen <Yngve.Levinsen@esss.se>
Date: Mon, 12 Mar 2018 17:03:39 +0100
Subject: [PATCH] flake8 corrections
---
ess/TraceWin.py | 818 ++++++++++++++++++++++++------------------------
1 file changed, 407 insertions(+), 411 deletions(-)
diff --git a/ess/TraceWin.py b/ess/TraceWin.py
index 45ddf39..d3bd2ca 100644
--- a/ess/TraceWin.py
+++ b/ess/TraceWin.py
@@ -1,8 +1,9 @@
from __future__ import print_function
+
class dst:
'''
- Simple class to read in a
+ Simple class to read in a
TraceWin distribution file
Class afterwards hold the following
@@ -16,22 +17,21 @@ class dst:
'''
def __init__(self, filename=None):
# easy storage..
- self.filename=filename
+ self.filename = filename
# used to create dict behaviour..
- self._columns=['x','xp','y','yp','phi','E']
+ self._columns = ['x', 'xp', 'y', 'yp', 'phi', 'E']
if filename:
# read in the file..
self._readBinaryFile()
else:
import numpy
- self.Np=0
- self.Ib=0.0
- self.freq=352.21
- self._data=numpy.zeros((self.Np,6))
- self.mass=0.0
-
+ self.Np = 0
+ self.Ib = 0.0
+ self.freq = 352.21
+ self._data = numpy.zeros((self.Np, 6))
+ self.mass = 0.0
- def append(self,x=0.0,xp=0.0,y=0.0,yp=0.0,E=0.0,phi=0.0):
+ def append(self, x=0.0, xp=0.0, y=0.0, yp=0.0, E=0.0, phi=0.0):
'''
Append one particle to the distribution
@@ -42,9 +42,8 @@ class dst:
'''
import numpy
- self._data=numpy.append(self._data, [[x,xp,y,yp,phi,E]], 0)
- self.Np+=1
-
+ self._data = numpy.append(self._data, [[x, xp, y, yp, phi, E]], 0)
+ self.Np += 1
def remove(self, i=None):
'''
@@ -52,11 +51,11 @@ class dst:
'''
import numpy
- if i==None:
- self._data=numpy.delete(self._data,numpy.s_[:],0)
+ if i is None:
+ self._data=numpy.delete(self._data,numpy.s_[:], 0)
self.Np=0
else:
- self._data=numpy.delete(self._data,i,0)
+ self._data=numpy.delete(self._data, i, 0)
self.Np-=1
def _readBinaryFile(self):
@@ -74,25 +73,25 @@ class dst:
('freq', numpy.float64),
('dummy3', numpy.int8)
])
- Header=numpy.fromfile(fin, dtype=Header_type, count=1)
- self.Np=Header['Np'][0]
- self.Ib=Header['Ib'][0]
- self.freq=Header['freq'][0]
+ Header = numpy.fromfile(fin, dtype=Header_type, count=1)
+ self.Np = Header['Np'][0]
+ self.Ib = Header['Ib'][0]
+ self.freq = Header['freq'][0]
# Some toutatis distributions has an undocumented 7th line of 0's
- Table=numpy.fromfile(fin, dtype=numpy.float64, count=self.Np*7+1)
+ Table = numpy.fromfile(fin, dtype=numpy.float64, count=self.Np*7+1)
if len(Table)==self.Np*7+1:
- self._data=Table[:-1].reshape(self.Np,7)
+ self._data = Table[:-1].reshape(self.Np, 7)
elif len(Table)==self.Np*6+1: # this is true in most cases
- self._data=Table[:-1].reshape(self.Np,6)
+ self._data = Table[:-1].reshape(self.Np, 6)
else:
- raise ValueError("Incorrect table dimensions found:",len(Table))
+ raise ValueError("Incorrect table dimensions found:", len(Table))
# convert x,y from cm to m:
- self._data[:,0]*=1e-2
- self._data[:,2]*=1e-2
+ self._data[:,0] *= 1e-2
+ self._data[:,2] *= 1e-2
- self.mass=Table[-1]
+ self.mass = Table[-1]
def keys(self):
return self._columns[:]
@@ -102,14 +101,14 @@ class dst:
# e.g. dst['x'] returns the x array..
try:
i=self._columns.index(key)
- return self._data[:,i]
+ return self._data[:, i]
except:
raise ValueError("Available keys: "+str(self._columns))
def __setitem__(self, key, value):
try:
i=self._columns.index(key)
- self._data[:,i]=value
+ self._data[:,i] = value
except:
raise ValueError("Available keys: "+str(self._columns))
@@ -127,34 +126,34 @@ class dst:
from struct import pack
import numpy
- fout=open(filename,'wb')
- fout.write(pack('b',125))
- fout.write(pack('b',100))
- fout.write(pack('i',self.Np))
- fout.write(pack('d',self.Ib))
- fout.write(pack('d',self.freq))
- fout.write(pack('b',125))
+ fout=open(filename, 'wb')
+ fout.write(pack('b', 125))
+ fout.write(pack('b', 100))
+ fout.write(pack('i', self.Np))
+ fout.write(pack('d', self.Ib))
+ fout.write(pack('d', self.freq))
+ fout.write(pack('b', 125))
- data=self._data.copy()
+ data = self._data.copy()
if toutatis and data.shape[1]==6:
- data=numpy.append(data,numpy.zeros((len(data),1)),1)
+ data = numpy.append(data,numpy.zeros((len(data),1)),1)
elif not toutatis and data.shape[1]==7:
- data=data[:,:-1]
+ data = data[:,:-1]
# convert x,y from m to cm:
- data[:,0]*=1e2
- data[:,2]*=1e2
+ data[:,0] *= 1e2
+ data[:,2] *= 1e2
if toutatis:
- data=data.reshape(self.Np*7,1)
+ data = data.reshape(self.Np*7, 1)
else:
- data=data.reshape(self.Np*6,1)
+ data = data.reshape(self.Np*6, 1)
for x in data:
- fout.write(pack('d',x))
+ fout.write(pack('d', x))
- fout.write(pack('d',self.mass))
+ fout.write(pack('d', self.mass))
fout.close()
def subplot(self, index, x, y=None, nb=100, mask=None):
@@ -185,9 +184,9 @@ class dst:
# get X and Y data
dx=np.array(self[x])
if mask!=None:
- dx=dx[mask]
+ dx = dx[mask]
if y!=None:
- dy=np.array(self[y])
+ dy = np.array(self[y])
if mask!=None:
dy=dy[mask]
@@ -384,16 +383,16 @@ class plt:
if not hasattr(self,'avg'):
self.calc_avg()
- self.gamma=[]
- self.beta=[]
+ self.gamma = []
+ self.beta = []
for i,j in zip(self.Nelp,range(len(self.Nelp))):
- Eavg=self.avg['E'][j]
- self.gamma.append((self.mc2+Eavg)/self.mc2)
- self.beta.append(numpy.sqrt(1.-1./self.gamma[-1]**2))
- self.gamma=numpy.array(self.gamma)
- self.beta=numpy.array(self.beta)
+ Eavg = self.avg['E'][j]
+ self.gamma.append((self.mc2 + Eavg) / self.mc2)
+ self.beta.append(numpy.sqrt(1. - 1. / self.gamma[-1]**2))
+ self.gamma = numpy.array(self.gamma)
+ self.beta = numpy.array(self.beta)
- def calc_minmax(self,pmin=5,pmax=95):
+ def calc_minmax(self, pmin=5, pmax=95):
'''
Calculates min/max values of beam coordinates
in percentile, pmin is lower and pmax upper.
@@ -402,14 +401,14 @@ class plt:
'''
import numpy
- self.min=dict(x=[], xp=[], y=[], yp=[], E=[])
- self.max=dict(x=[], xp=[], y=[], yp=[], E=[])
+ self.min = dict(x=[], xp=[], y=[], yp=[], E=[])
+ self.max = dict(x=[], xp=[], y=[], yp=[], E=[])
for i in self.Nelp:
- data=self[i]
+ data = self[i]
for v in self.min.keys():
- self.min[v].append(numpy.percentile(data[v],pmin))
- self.max[v].append(numpy.percentile(data[v],pmax))
+ self.min[v].append(numpy.percentile(data[v], pmin))
+ self.max[v].append(numpy.percentile(data[v], pmax))
for v in self.min.keys():
self.min[v]=numpy.array(self.min[v])
@@ -434,18 +433,18 @@ class plt:
import numpy
if not hasattr(self,'avg'):
- self.calc_avg()
+ self.calc_avg()
- vals=self._columns[:-1]
+ vals = self._columns[:-1]
- self.sigma=[]
+ self.sigma = []
for j in range(len(self.Nelp)):
- i=self.Nelp[j]
- data=self[i]
+ i = self.Nelp[j]
+ data = self[i]
- self.sigma.append([[numpy.mean( (data[n]-self.avg[n][j]) * (data[m] - self.avg[m][j]) ) for n in vals] for m in vals])
+ self.sigma.append([[numpy.mean((data[n] - self.avg[n][j]) * (data[m] - self.avg[m][j])) for n in vals] for m in vals])
- self.sigma=numpy.array(self.sigma)
+ self.sigma = numpy.array(self.sigma)
def calc_std(self):
'''
@@ -455,16 +454,16 @@ class plt:
import numpy
- if not hasattr(self,'sigma'):
- self.calc_sigma()
+ if not hasattr(self, 'sigma'):
+ self.calc_sigma()
- vals=self._columns[:-1]
+ vals = self._columns[:-1]
- self.std={}
+ self.std = {}
for j in range(len(vals)):
- v=vals[j]
- self.std[v]=numpy.sqrt(self.sigma[:,j,j])
+ v = vals[j]
+ self.std[v] = numpy.sqrt(self.sigma[:, j, j])
def calc_twiss(self):
'''
@@ -474,29 +473,29 @@ class plt:
import numpy
- if not hasattr(self,'sigma'):
- self.calc_sigma()
- if not hasattr(self,'gamma'):
- self.calc_rel()
+ if not hasattr(self, 'sigma'):
+ self.calc_sigma()
+ if not hasattr(self, 'gamma'):
+ self.calc_rel()
- self.twiss_eps=[]
+ self.twiss_eps = []
for j in range(len(self.Nelp)):
- self.twiss_eps.append([numpy.sqrt(numpy.linalg.det(self.sigma[j][i:i+2][:,i:i+2])) for i in (0,2,4)])
- self.twiss_eps=numpy.array(self.twiss_eps)
+ self.twiss_eps.append([numpy.sqrt(numpy.linalg.det(self.sigma[j][i:i + 2][:, i:i + 2])) for i in (0, 2, 4)])
+ self.twiss_eps = numpy.array(self.twiss_eps)
# Calculate normalized emittance:
# TODO: this is NOT correct normalization for longitudinal
- self.twiss_eps_normed=self.twiss_eps.copy()
+ self.twiss_eps_normed = self.twiss_eps.copy()
for i in range(3):
- self.twiss_eps_normed[:,i]*=self.gamma*self.beta
+ self.twiss_eps_normed[:, i] *= self.gamma * self.beta
# Calculate beta:
# This is a factor 10 different from what TraceWin plots
- self.twiss_beta = [[self.sigma[j][i][i]/self.twiss_eps[j,i/2] for i in (0,2,4)] for j in range(len(self.Nelp))]
+ self.twiss_beta = [[self.sigma[j][i][i] / self.twiss_eps[j, i / 2] for i in (0, 2, 4)] for j in range(len(self.Nelp))]
self.twiss_beta = numpy.array(self.twiss_beta)
# Calculate alpha:
- self.twiss_alpha = [[-self.sigma[j][i][i+1]/self.twiss_eps[j,i/2] for i in (0,2,4)] for j in range(len(self.Nelp))]
+ self.twiss_alpha = [[-self.sigma[j][i][i + 1] / self.twiss_eps[j, i / 2] for i in (0, 2, 4)] for j in range(len(self.Nelp))]
self.twiss_alpha = numpy.array(self.twiss_alpha)
def get_dst(self, index):
@@ -504,34 +503,33 @@ class plt:
Returns the dst corresponding to the given index
'''
import numpy
-
- dset=self[index]
-
- _dst=dst()
- _dst.freq=self.freq
- _dst.Ib=self.Ib*1000
- _dst.Np=len(dset['x'])
- _dst.mass=self.mc2
- _dst._data=numpy.array([dset['x'],
- dset['xp'],
- dset['y'],
- dset['yp'],
- dset['phi'],
- dset['E']]).transpose()
+
+ dset = self[index]
+
+ _dst = dst()
+ _dst.freq = self.freq
+ _dst.Ib = self.Ib * 1000
+ _dst.Np = len(dset['x'])
+ _dst.mass = self.mc2
+ _dst._data = numpy.array([dset['x'],
+ dset['xp'],
+ dset['y'],
+ dset['yp'],
+ dset['phi'],
+ dset['E']]).transpose()
return _dst
- def save_dst(self, index,filename):
+ def save_dst(self, index, filename):
'''
Saves the dst at the specified index to file
Returns the same dst object.
'''
- _dst=self.get_dst(index)
+ _dst = self.get_dst(index)
_dst.save(filename)
return _dst
-
class density_file:
'''
Simple class to read a TraceWin density file
@@ -539,152 +537,148 @@ class density_file:
'''
def __init__(self, filename, envelope=None):
- import numpy, sys
-
+ import numpy
+ import sys
- self.filename=filename
- self.fin=open(self.filename, 'r')
+ self.filename = filename
+ self.fin = open(self.filename, 'r')
- if envelope==None: # try to guess
- if filename.split('/')[-1].split('.')[0]=='Density_Env':
- self.envelope=True
+ if envelope is None: # try to guess
+ if filename.split('/')[-1].split('.')[0] == 'Density_Env':
+ self.envelope = True
else:
- self.envelope=False
+ self.envelope = False
else:
- self.envelope=envelope
+ self.envelope = envelope
# currently unknown:
- self.version=0
+ self.version = 0
# first we simply count how many elements we have:
- counter=0
+ counter = 0
while True:
try:
self._skipAndCount()
- counter+=1
- except IndexError: # EOF reached..
+ counter += 1
+ except IndexError: # EOF reached..
break
if sys.flags.debug:
print("Number of steps found:", counter)
self.fin.seek(0)
# set up the arrays..
- self.i=0
+ self.i = 0
# z position [m] :
- self.z=numpy.zeros(counter)
+ self.z = numpy.zeros(counter)
# element index number
- self.nelp=numpy.zeros(counter)
+ self.nelp = numpy.zeros(counter)
# current [mA] :
- self.ib=numpy.zeros(counter)
+ self.ib = numpy.zeros(counter)
# number of lost particles:
- self.Np=numpy.zeros(counter)
+ self.Np = numpy.zeros(counter)
- self.Xouv=numpy.zeros(counter)
- self.Youv=numpy.zeros(counter)
+ self.Xouv = numpy.zeros(counter)
+ self.Youv = numpy.zeros(counter)
- if self.version>=9:
- self.dXouv=numpy.zeros(counter)
- self.dYouv=numpy.zeros(counter)
+ if self.version >= 9:
+ self.dXouv = numpy.zeros(counter)
+ self.dYouv = numpy.zeros(counter)
- self.moy=numpy.zeros((counter,7))
- self.moy2=numpy.zeros((counter,7))
+ self.moy = numpy.zeros((counter, 7))
+ self.moy2 = numpy.zeros((counter, 7))
- self._max=numpy.zeros((counter,7))
- self._min=numpy.zeros((counter,7))
+ self._max = numpy.zeros((counter, 7))
+ self._min = numpy.zeros((counter, 7))
- if self.version>=10:
- self.maxR=numpy.zeros((counter,7))
- self.minR=numpy.zeros((counter,7))
+ if self.version >= 10:
+ self.maxR = numpy.zeros((counter, 7))
+ self.minR = numpy.zeros((counter, 7))
- if self.version>=5:
- self.rms_size=numpy.zeros((counter,7))
- self.rms_size2=numpy.zeros((counter,7))
+ if self.version >= 5:
+ self.rms_size = numpy.zeros((counter, 7))
+ self.rms_size2 = numpy.zeros((counter, 7))
- if self.version>=6:
- self.min_pos_moy=numpy.zeros((counter,7))
- self.max_pos_moy=numpy.zeros((counter,7))
+ if self.version >= 6:
+ self.min_pos_moy = numpy.zeros((counter, 7))
+ self.max_pos_moy = numpy.zeros((counter, 7))
- if self.version>=7:
- self.rms_emit=numpy.zeros((counter,3))
- self.rms_emit2=numpy.zeros((counter,3))
+ if self.version >= 7:
+ self.rms_emit = numpy.zeros((counter, 3))
+ self.rms_emit2 = numpy.zeros((counter, 3))
- if self.version>=8:
- self.energy_accept=numpy.zeros(counter)
- self.phase_ouv_pos=numpy.zeros(counter)
- self.phase_ouv_neg=numpy.zeros(counter)
+ if self.version >= 8:
+ self.energy_accept = numpy.zeros(counter)
+ self.phase_ouv_pos = numpy.zeros(counter)
+ self.phase_ouv_neg = numpy.zeros(counter)
- self.lost=numpy.zeros((counter,self.Nrun))
- self.powlost=numpy.zeros((counter,self.Nrun))
+ self.lost = numpy.zeros((counter, self.Nrun))
+ self.powlost = numpy.zeros((counter, self.Nrun))
- self.lost2=numpy.zeros(counter)
- self.Minlost=numpy.zeros(counter)
- self.Maxlost=numpy.zeros(counter)
+ self.lost2 = numpy.zeros(counter)
+ self.Minlost = numpy.zeros(counter)
+ self.Maxlost = numpy.zeros(counter)
- self.powlost2=numpy.zeros(counter)
- self.Minpowlost=numpy.zeros(counter)
- self.Maxpowlost=numpy.zeros(counter)
+ self.powlost2 = numpy.zeros(counter)
+ self.Minpowlost = numpy.zeros(counter)
+ self.Maxpowlost = numpy.zeros(counter)
-
- while self.i<counter:
+ while self.i < counter:
self._getFullContent()
- self.i+=1
- if sys.flags.debug and self.i%100 == 0:
- print('Read status',self.i)
-
+ self.i += 1
+ if sys.flags.debug and self.i % 100 == 0:
+ print('Read status', self.i)
def _getHeader(self):
import numpy
-
# header..
- version=numpy.fromfile(self.fin, dtype=numpy.int16, count=1)[0]
- year=numpy.fromfile(self.fin, dtype=numpy.int16, count=1)[0]
+ version = numpy.fromfile(self.fin, dtype=numpy.int16, count=1)[0]
+ year = numpy.fromfile(self.fin, dtype=numpy.int16, count=1)[0]
# in case we did not read all data, this will detect our mistake:
- shift=0
- while year!=2011 or version not in [8,9,10,11,12]:
- shift+=1
- version=year
- year=numpy.fromfile(self.fin, dtype=numpy.int16, count=1)[0]
+ shift = 0
+ while year != 2011 or version not in [8, 9, 10, 11, 12]:
+ shift += 1
+ version = year
+ year = numpy.fromfile(self.fin, dtype=numpy.int16, count=1)[0]
if shift:
- print(year,version)
- raise ValueError("ERROR, shifted "+str(shift*2)+" bytes")
+ print(year, version)
+ raise ValueError("ERROR, shifted " + str(shift * 2) + " bytes")
- self.vlong=numpy.fromfile(self.fin, dtype=numpy.int16, count=1)[0]
- self.Nrun=numpy.fromfile(self.fin, dtype=numpy.int32, count=1)[0]
+ self.vlong = numpy.fromfile(self.fin, dtype=numpy.int16, count=1)[0]
+ self.Nrun = numpy.fromfile(self.fin, dtype=numpy.int32, count=1)[0]
- self.version=version
- self.year=year
+ self.version = version
+ self.year = year
def _skipAndCount(self):
-
import numpy
self._getHeader()
if self.envelope:
- if self.version==8:
- numpy.fromfile(self.fin, dtype=numpy.int16, count=292//2)
- elif self.version==9:
- numpy.fromfile(self.fin, dtype=numpy.int16, count=300//2)
- elif self.version==10:
- numpy.fromfile(self.fin, dtype=numpy.int16, count=356//2)
+ if self.version == 8:
+ numpy.fromfile(self.fin, dtype=numpy.int16, count=292 // 2)
+ elif self.version == 9:
+ numpy.fromfile(self.fin, dtype=numpy.int16, count=300 // 2)
+ elif self.version == 10:
+ numpy.fromfile(self.fin, dtype=numpy.int16, count=356 // 2)
else:
raise TypeError("It is not possible to read this format..")
- elif self.Nrun>1:
- #WARN not 100% sure if this is correct..
- if self.version<=9:
- numpy.fromfile(self.fin, dtype=numpy.int16, count=((5588+self.Nrun*12)//2))
- elif self.version==10:
- numpy.fromfile(self.fin, dtype=numpy.int16, count=((20796+self.Nrun*12)//2))
+ elif self.Nrun > 1:
+ # WARN not 100% sure if this is correct..
+ if self.version <= 9:
+ numpy.fromfile(self.fin, dtype=numpy.int16, count=((5588 + self.Nrun * 12) // 2))
+ elif self.version == 10:
+ numpy.fromfile(self.fin, dtype=numpy.int16, count=((20796 + self.Nrun * 12) // 2))
else:
raise TypeError("It is not possible to read this format..")
- elif self.version==8:
- numpy.fromfile(self.fin, dtype=numpy.int16, count=12344//2)
- elif self.version==9:
- numpy.fromfile(self.fin, dtype=numpy.int16, count=12352//2)
- elif self.version==10:
- numpy.fromfile(self.fin, dtype=numpy.int16, count=12408//2)
+ elif self.version == 8:
+ numpy.fromfile(self.fin, dtype=numpy.int16, count=12344 // 2)
+ elif self.version == 9:
+ numpy.fromfile(self.fin, dtype=numpy.int16, count=12352 // 2)
+ elif self.version == 10:
+ numpy.fromfile(self.fin, dtype=numpy.int16, count=12408 // 2)
else:
raise TypeError("It is not possible to read this format..")
@@ -705,107 +699,107 @@ class density_file:
import numpy
- #self._getHeader()
+ # self._getHeader()
# no need to read the header again:
# (though only if we are SURE about content!)
numpy.fromfile(self.fin, dtype=numpy.int16, count=5)
- self.nelp[self.i]=numpy.fromfile(self.fin, dtype=numpy.int32, count=1)[0]
- self.ib[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
- self.z[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+ self.nelp[self.i] = numpy.fromfile(self.fin, dtype=numpy.int32, count=1)[0]
+ self.ib[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+ self.z[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
# Aperture
- self.Xouv[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
- self.Youv[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
- if self.version>=9:
- dXouv=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
- dYouv=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
- step=numpy.fromfile(self.fin, dtype=numpy.int32, count=1)[0]
-
- n=7 # x [m], y[m], Phase [deg], Energy [MeV], R[m], Z[m], dp/p
-
- self.moy[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
- self.moy2[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
-
- self._max[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
- self._min[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
-
- if self.version>=10:
- self.maxR[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
- self.minR[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
-
- if self.version>=5:
- self.rms_size[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)
- self.rms_size2[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)
- if self.version>=6:
- self.min_pos_moy[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)
- self.max_pos_moy[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=n)
- if self.version>=7:
- self.rms_emit[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=3)[:]
- self.rms_emit2[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=3)[:]
- if self.version>=8:
- self.energy_accept[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)
- self.phase_ouv_pos[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)
- self.phase_ouv_neg[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)
-
- self.Np[self.i]=numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
+ self.Xouv[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+ self.Youv[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+ if self.version >= 9:
+ dXouv = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+ dYouv = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+ step = numpy.fromfile(self.fin, dtype=numpy.int32, count=1)[0]
+
+ n = 7 # x [m], y[m], Phase [deg], Energy [MeV], R[m], Z[m], dp/p
+
+ self.moy[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
+ self.moy2[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
+
+ self._max[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
+ self._min[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
+
+ if self.version >= 10:
+ self.maxR[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
+ self.minR[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)[:]
+
+ if self.version >= 5:
+ self.rms_size[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)
+ self.rms_size2[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)
+ if self.version >= 6:
+ self.min_pos_moy[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)
+ self.max_pos_moy[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=n)
+ if self.version >= 7:
+ self.rms_emit[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=3)[:]
+ self.rms_emit2[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=3)[:]
+ if self.version >= 8:
+ self.energy_accept[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)
+ self.phase_ouv_pos[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)
+ self.phase_ouv_neg[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)
+
+ self.Np[self.i] = numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
if self.Np[self.i]:
for i in range(self.Nrun):
- self.lost[self.i,i]=numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
- self.powlost[self.i,i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
- self.lost2[self.i]=numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
- self.Minlost[self.i]=numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
- self.Maxlost[self.i]=numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
- self.powlost2[self.i]=numpy.fromfile(self.fin, dtype=numpy.float64, count=1)[0]
- self.Minpowlost[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
- self.Maxpowlost[self.i]=numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
-
- if self.vlong==1:
- tab=numpy.fromfile(self.fin, dtype=numpy.uint64, count=n*step)
+ self.lost[self.i, i] = numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
+ self.powlost[self.i, i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+ self.lost2[self.i] = numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
+ self.Minlost[self.i] = numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
+ self.Maxlost[self.i] = numpy.fromfile(self.fin, dtype=numpy.int64, count=1)[0]
+ self.powlost2[self.i] = numpy.fromfile(self.fin, dtype=numpy.float64, count=1)[0]
+ self.Minpowlost[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+ self.Maxpowlost[self.i] = numpy.fromfile(self.fin, dtype=numpy.float32, count=1)[0]
+
+ if self.vlong == 1:
+ tab = numpy.fromfile(self.fin, dtype=numpy.uint64, count=n * step)
else:
- tab=numpy.fromfile(self.fin, dtype=numpy.uint32, count=n*step)
-
- if self.ib[self.i]>0:
- tabp=numpy.fromfile(self.fin, dtype=numpy.uint32, count=3*step)
+ tab = numpy.fromfile(self.fin, dtype=numpy.uint32, count=n * step)
+
+ if self.ib[self.i] > 0:
+ tabp = numpy.fromfile(self.fin, dtype=numpy.uint32, count=3 * step)
- def _avg_merge(self,other,param):
+ def _avg_merge(self, other, param):
'''
returns the average of the parameter
weighted by how many Nruns in self and other object
This allows for different lengths of the two arrays..
'''
- mine=getattr(self,param)
- new=getattr(other,param)
- if len(mine)>len(new):
+ mine = getattr(self, param)
+ new = getattr(other, param)
+ if len(mine) > len(new):
ret = mine.copy()
- ret[:len(new)] = (mine[:len(new)]*self.Nrun+new*other.Nrun)/(self.Nrun+other.Nrun)
- elif len(mine)<len(new):
+ ret[:len(new)] = (mine[:len(new)] * self.Nrun + new * other.Nrun) / (self.Nrun + other.Nrun)
+ elif len(mine) < len(new):
ret = new.copy()
- ret[:len(mine)] = (mine*self.Nrun+new[:len(mine)]*other.Nrun)/(self.Nrun+other.Nrun)
+ ret[:len(mine)] = (mine * self.Nrun + new[:len(mine)] * other.Nrun) / (self.Nrun + other.Nrun)
else:
- ret = (mine*self.Nrun+new*other.Nrun)/(self.Nrun+other.Nrun)
+ ret = (mine * self.Nrun + new * other.Nrun) / (self.Nrun + other.Nrun)
return ret
- def _sum_merge(self,other,param):
+ def _sum_merge(self, other, param):
'''
returns the sum of the parameter
This allows for different lengths of the two arrays..
'''
- mine=getattr(self,param)
- new=getattr(other,param)
- if len(mine)>len(new):
+ mine = getattr(self, param)
+ new = getattr(other, param)
+ if len(mine) > len(new):
ret = mine.copy()
ret[:len(new)] += new
- elif len(mine)<len(new):
+ elif len(mine) < len(new):
ret = new.copy()
ret[:len(mine)] += mine
else:
- ret = mine+new
+ ret = mine + new
return ret
- def _concatenate_merge(self,other,param):
+ def _concatenate_merge(self, other, param):
'''
returns the concatenation of the two matrices
@@ -813,38 +807,38 @@ class density_file:
'''
import numpy
- mine=getattr(self,param)
- new=getattr(other,param)
- ret=numpy.zeros(( max([len(mine),len(new)]), len(mine[0])+len(new[0]) ))
- ret[:len(mine),:len(mine[0])]=mine
- ret[:len(new),len(mine[0]):]=new
+ mine = getattr(self, param)
+ new = getattr(other, param)
+ ret = numpy.zeros((max([len(mine), len(new)]), len(mine[0]) + len(new[0])))
+ ret[:len(mine), :len(mine[0])] = mine
+ ret[:len(new), len(mine[0]):] = new
return ret
- def _fun_merge(self,other,function,param):
+ def _fun_merge(self, other, function, param):
'''
returns the function applied on the parameter
This allows for different lengths of the two arrays..
'''
- mine=getattr(self,param)
- new=getattr(other,param)
- if len(mine)>len(new):
+ mine = getattr(self, param)
+ new = getattr(other, param)
+ if len(mine) > len(new):
ret = mine.copy()
- ret[:len(new)] = function(mine[:len(new)],new)
- elif len(mine)<len(new):
+ ret[:len(new)] = function(mine[:len(new)], new)
+ elif len(mine) < len(new):
ret = new.copy()
- ret[:len(mine)] = function(mine,new[:len(mine)])
+ ret[:len(mine)] = function(mine, new[:len(mine)])
else:
- ret = function(mine,new)
+ ret = function(mine, new)
return ret
- def merge(self,objects):
+ def merge(self, objects):
'''
Merge with list of objects
'''
import numpy
- if not isinstance(objects,list):
+ if not isinstance(objects, list):
raise TypeError("You tried to merge a non-list")
# for now we only allow objects with same version..
@@ -854,90 +848,91 @@ class density_file:
# merge info..
for o in objects:
- if len(self.ib)<len(o.ib):
+ if len(self.ib) < len(o.ib):
raise ValueError("Sorry, not implemented yet. Complain to Yngve")
- self.ib=self._avg_merge(o,'ib')
+ self.ib = self._avg_merge(o, 'ib')
# this looks strange to me, but it is what TraceWin does..
- self.moy=self._sum_merge(o,'moy')
- self.moy2=self._sum_merge(o,'moy')
+ self.moy = self._sum_merge(o, 'moy')
+ self.moy2 = self._sum_merge(o, 'moy')
- self._max=self._fun_merge(o,numpy.maximum,'_max')
- self._min=self._fun_merge(o,numpy.minimum,'_min')
+ self._max = self._fun_merge(o, numpy.maximum, '_max')
+ self._min = self._fun_merge(o, numpy.minimum, '_min')
- if self.version>=5:
+ if self.version >= 5:
# this looks strange to me, but it is what TraceWin does..
- self.rms_size=self._sum_merge(o,'rms_size')
- self.rms_size2=self._sum_merge(o,'rms_size2')
+ self.rms_size = self._sum_merge(o, 'rms_size')
+ self.rms_size2 = self._sum_merge(o, 'rms_size2')
- if self.version>=6:
- self.max_pos_moy=self._fun_merge(o,numpy.maximum,'max_pos_moy')
- self.min_pos_moy=self._fun_merge(o,numpy.minimum,'min_pos_moy')
+ if self.version >= 6:
+ self.max_pos_moy = self._fun_merge(o, numpy.maximum, 'max_pos_moy')
+ self.min_pos_moy = self._fun_merge(o, numpy.minimum, 'min_pos_moy')
- if self.version>=7:
+ if self.version >= 7:
# this looks strange to me, but it is what TraceWin does..
- self.rms_emit=self._sum_merge(o,'rms_emit')
- self.rms_emit2=self._sum_merge(o,'rms_emit2')
+ self.rms_emit = self._sum_merge(o, 'rms_emit')
+ self.rms_emit2 = self._sum_merge(o, 'rms_emit2')
- if self.version>=8:
+ if self.version >= 8:
# Warning: TraceWin does NOT merge these data in any way
- self.energy_accept=self._avg_merge(o,'energy_accept')
- self.phase_ouv_pos=self._avg_merge(o,'phase_ouv_pos')
- self.phase_ouv_neg=self._avg_merge(o,'phase_ouv_neg')
+ self.energy_accept = self._avg_merge(o, 'energy_accept')
+ self.phase_ouv_pos = self._avg_merge(o, 'phase_ouv_pos')
+ self.phase_ouv_neg = self._avg_merge(o, 'phase_ouv_neg')
# Note, we don't get into the problem of differing table sizes
# particles are lost, because we have written zeroes for
# the rest of the tables
- self.lost=self._concatenate_merge(o,'lost')
- self.powlost=self._concatenate_merge(o,'powlost')
+ self.lost = self._concatenate_merge(o, 'lost')
+ self.powlost = self._concatenate_merge(o, 'powlost')
- self.lost2=self._sum_merge(o,'lost2')
- self.powlost2=self._sum_merge(o,'powlost2')
+ self.lost2 = self._sum_merge(o, 'lost2')
+ self.powlost2 = self._sum_merge(o, 'powlost2')
- self.Minlost=self._fun_merge(o,numpy.minimum,'Minlost')
- self.Maxlost=self._fun_merge(o,numpy.maximum,'Maxlost')
- self.Minpowlost=self._fun_merge(o,numpy.minimum,'Minpowlost')
- self.Maxpowlost=self._fun_merge(o,numpy.maximum,'Maxpowlost')
+ self.Minlost = self._fun_merge(o, numpy.minimum, 'Minlost')
+ self.Maxlost = self._fun_merge(o, numpy.maximum, 'Maxlost')
+ self.Minpowlost = self._fun_merge(o, numpy.minimum, 'Minpowlost')
+ self.Maxpowlost = self._fun_merge(o, numpy.maximum, 'Maxpowlost')
# Note: We are ignoring tab/tabp data...
# merge final info (make sure to do this last!)
- self.Np=self._sum_merge(o,'Np')
- self.Nrun+=o.Nrun
+ self.Np = self._sum_merge(o, 'Np')
+ self.Nrun += o.Nrun
- def savetohdf(self,filename='Density.h5',group='TraceWin', force=False):
+ def savetohdf(self, filename='Density.h5', group='TraceWin', force=False):
'''
Saves data to HDF5
'''
- import h5py,sys
+ import h5py
+ import sys
- fout = h5py.File(filename,'a')
+ fout = h5py.File(filename, 'a')
if group in fout:
if force:
del fout[group]
else:
if sys.flags.debug:
- print("Group {} already exist in {}".format(group,filename))
+ print("Group {} already exist in {}".format(group, filename))
return
group = fout.create_group(group)
# header attributes..
- group.attrs['version']=self.version
- group.attrs['year']=self.year
- group.attrs['Nrun']=self.Nrun
- group.attrs['vlong']=self.vlong
+ group.attrs['version'] = self.version
+ group.attrs['year'] = self.year
+ group.attrs['Nrun'] = self.Nrun
+ group.attrs['vlong'] = self.vlong
- length=len(self.z)
+ length = len(self.z)
- partran = sum(self.Np)>0
+ partran = sum(self.Np) > 0
# one number per location
- arrays= ['z', 'nelp', 'ib', 'Np', 'Xouv', 'Youv']
- array_units=['m', '', 'mA', '', 'm', 'm']
- if self.version>=8:
+ arrays = ['z', 'nelp', 'ib', 'Np', 'Xouv', 'Youv']
+ array_units = ['m', '', 'mA', '', 'm', 'm']
+ if self.version >= 8:
arrays += ['energy_accept', 'phase_ouv_pos', 'phase_ouv_neg']
array_units += [ 'eV', 'deg', 'deg']
if partran:
@@ -945,70 +940,69 @@ class density_file:
array_units += [ '', '', '', 'W*w', 'W', 'W']
# 7 numbers per location..
- coordinates= ['moy', 'moy2', '_max', '_min']
- coordinate_units=['m', 'm*m', 'm', 'm']
- if self.version>=5 and partran:
- coordinates += ['rms_size','rms_size2']
- coordinate_units += [ 'm', 'm*m']
- if self.version>=6 and partran:
+ coordinates = ['moy', 'moy2', '_max', '_min']
+ coordinate_units = ['m', 'm*m', 'm', 'm']
+ if self.version >= 5 and partran:
+ coordinates += ['rms_size', 'rms_size2']
+ coordinate_units += [ 'm', 'm*m']
+ if self.version >= 6 and partran:
coordinates += ['min_pos_moy', 'max_pos_moy']
coordinate_units += [ 'm', 'm']
-
- for val,unit in zip(arrays,array_units):
- data_set=group.create_dataset(val, (length,), dtype='f')
- data_set[...]=getattr(self,val)
+ for val, unit in zip(arrays, array_units):
+ data_set = group.create_dataset(val, (length,), dtype='f')
+ data_set[...] = getattr(self, val)
if unit:
- data_set.attrs['unit']=unit
+ data_set.attrs['unit'] = unit
- for val,unit in zip(coordinates,coordinate_units):
- data_set=group.create_dataset(val, (length,7), dtype='f')
- data_set[...]=getattr(self,val)
+ for val, unit in zip(coordinates, coordinate_units):
+ data_set = group.create_dataset(val, (length, 7), dtype='f')
+ data_set[...] = getattr(self, val)
if unit:
- data_set.attrs['unit']=unit
+ data_set.attrs['unit'] = unit
- if self.version>=7 and partran:
+ if self.version >= 7 and partran:
# 3 numbers per location..
- emit_data=['rms_emit', 'rms_emit2']
- emit_units=['m*rad', 'm*m*rad*rad']
- for val,unit in zip(emit_data,emit_units):
- data_set=group.create_dataset(val, (length,3), dtype='f')
- data_set[...]=getattr(self,val)
+ emit_data = ['rms_emit', 'rms_emit2']
+ emit_units = ['m*rad', 'm*m*rad*rad']
+ for val, unit in zip(emit_data, emit_units):
+ data_set = group.create_dataset(val, (length, 3), dtype='f')
+ data_set[...] = getattr(self, val)
if unit:
- data_set.attrs['unit']=unit
+ data_set.attrs['unit'] = unit
if partran:
# 1 numbers per location and per run..
- data=['lost', 'powlost']
- units=['', 'W']
- for val,unit in zip(data, units):
- data_set=group.create_dataset(val, (length,self.Nrun), dtype='f')
- data_set[...]=getattr(self,val)
+ data = ['lost', 'powlost']
+ units = ['', 'W']
+ for val, unit in zip(data, units):
+ data_set = group.create_dataset(val, (length, self.Nrun), dtype='f')
+ data_set[...] = getattr(self, val)
if unit:
- data_set.attrs['unit']=unit
+ data_set.attrs['unit'] = unit
fout.close()
class remote_data_merger:
def __init__(self, base='.'):
- self._base=base
- self._files=[]
+ self._base = base
+ self._files = []
- def add_file(self,filepath):
+ def add_file(self, filepath):
import os
if os.path.exists(filepath):
- fname=filepath
+ fname = filepath
else:
- fullpath=os.path.join(self._base,filepath)
+ fullpath = os.path.join(self._base, filepath)
if os.path.exists(fullpath):
- fname=fullpath
+ fname = fullpath
else:
- raise ValueError("Could not find file "+filepath)
+ raise ValueError("Could not find file " + filepath)
if fname not in self._files:
self._files.append(fname)
- def generate_partran_out(self,filename=None):
+ def generate_partran_out(self, filename=None):
'''
Creates a string to be written to file
each line is a list.
@@ -1019,25 +1013,25 @@ class remote_data_merger:
import numpy as np
- h1=[]
- h2=[]
+ h1 = []
+ h2 = []
- d1=[]
- d2=[]
- d3=[]
+ d1 = []
+ d2 = []
+ d3 = []
if self._files:
for f in self._files:
- string=open(f,'r').read()
- split=string.split('$$$')
- if split[9]!='Data_Error':
+ string = open(f, 'r').read()
+ split = string.split('$$$')
+ if split[9] != 'Data_Error':
raise ValueError("Magic problem, please complain to Yngve")
- thisdata=split[10].strip().split('\n')
+ thisdata = split[10].strip().split('\n')
if not h1:
- h1=[thisdata[0]+" (std in paranthesis)"]
- h2=thisdata[2:10]
+ h1 = [thisdata[0] + " (std in paranthesis)"]
+ h2 = thisdata[2:10]
d1.append(thisdata[1].split())
d2.append(thisdata[10])
d3.append(thisdata[11])
@@ -1045,54 +1039,56 @@ class remote_data_merger:
# fix d1:
for i in range(len(d1)):
for j in range(len(d1[0])):
- d1[i][j]=float(d1[i][j])
- d1=np.array(d1)
- means=d1.mean(axis=0)
- stds=d1.std(axis=0)
- d1=[]
+ d1[i][j] = float(d1[i][j])
+ d1 = np.array(d1)
+ means = d1.mean(axis=0)
+ stds = d1.std(axis=0)
+ d1 = []
for i in range(len(stds)):
- if stds[i]/means[i]<1e-10:
- stds[i]=0.0
+ if stds[i] / means[i] < 1e-10:
+ stds[i] = 0.0
for i in range(len(stds)):
# some small std are removed..
- if stds[i]/means[i]>1e-8:
- d1.append('%f(%f)' % (means[i],stds[i]))
- else: #error is 0
+ if stds[i] / means[i] > 1e-8:
+ d1.append('%f(%f)' % (means[i], stds[i]))
+ else: # error is 0
d1.append(str(means[i]))
- d1=[' '.join(d1)]
+ d1 = [' '.join(d1)]
# create data:
- data=h1+d1+h2+d2+d3
+ data = h1 + d1 + h2 + d2 + d3
if filename:
- open(filename,'w').write('\n'.join(data))
+ open(filename, 'w').write('\n'.join(data))
return data
+
class partran(dict):
'''
Read partran1.out files..
'''
- def __init__(self,filename):
- self.filename=filename
+ def __init__(self, filename):
+ self.filename = filename
self._readAsciiFile()
def _readAsciiFile(self):
import numpy
- stream=open(self.filename,'r')
+ stream = open(self.filename, 'r')
for i in range(10):
- line=stream.readline()
- if line.strip()[0]=='#':
+ line = stream.readline()
+ if line.strip()[0] == '#':
break
- self.columns=['NUM']+line.split()[1:]
- self.data=numpy.loadtxt(stream)
+ self.columns = ['NUM'] + line.split()[1:]
+ self.data = numpy.loadtxt(stream)
- self._dict={}
+ self._dict = {}
for i in range(len(self.columns)):
- self[self.columns[i]]=self.data[:,i]
+ self[self.columns[i]] = self.data[:, i]
+
class field_map:
'''
@@ -1102,47 +1098,47 @@ class field_map:
'''
def __init__(self, filename):
- self._filename=filename
+ self._filename = filename
self._load_data(filename)
- def _load_data(self,filename):
- import os,numpy
+ def _load_data(self, filename):
+ import os
+ import numpy
if not os.path.isfile(filename):
raise ValueError("Cannot find file {}".format(filename))
- fin = open(filename,'r')
- l=fin.readline().split()
- self.start=[]
- self.end=[]
- numindexes=[]
- while len(l)>1:
- numindexes.append(int(l[0])+1)
- if len(l)==2:
+ fin = open(filename, 'r')
+ l = fin.readline().split()
+ self.start = []
+ self.end = []
+ numindexes = []
+ while len(l) > 1:
+ numindexes.append(int(l[0]) + 1)
+ if len(l) == 2:
self.start.append(0.0)
self.end.append(float(l[1]))
else:
self.start.append(float(l[1]))
self.end.append(float(l[2]))
- l=fin.readline().split()
+ l = fin.readline().split()
- self.z=numpy.arange(self.start[0],self.end[0],(self.end[0]-self.start[0])/numindexes[0])
- if len(self.start)>1:
- self.y=numpy.arange(self.start[1],self.end[1],(self.end[1]-self.start[1])/numindexes[1])
- if len(self.start)>2:
- self.x=numpy.arange(self.start[2],self.end[2],(self.end[2]-self.start[2])/(numindexes[2]))
+ self.z = numpy.arange(self.start[0], self.end[0], (self.end[0] - self.start[0]) / numindexes[0])
+ if len(self.start) > 1:
+ self.y = numpy.arange(self.start[1], self.end[1], (self.end[1] - self.start[1]) / numindexes[1])
+ if len(self.start) > 2:
+ self.x = numpy.arange(self.start[2], self.end[2], (self.end[2] - self.start[2]) / (numindexes[2]))
- self.norm=float(l[0])
- self.map=numpy.loadtxt(fin).reshape(numindexes)
+ self.norm = float(l[0])
+ self.map = numpy.loadtxt(fin).reshape(numindexes)
def savemap(self, filename):
- fout=open(filename,'w')
- for n,s in zip(self.map.shape,self.size):
- fout.write('{} {}\n'.format(n-1,s))
+ fout = open(filename, 'w')
+ for n, s in zip(self.map.shape, self.size):
+ fout.write('{} {}\n'.format(n - 1, s))
fout.write('{}\n'.format(self.norm))
- l=1
+ totmapshape = 1
for i in self.map.shape:
- l*=i
- data=self.map.reshape(l)
+ totmapshape *= i
+ data = self.map.reshape(totmapshape)
for j in data:
fout.write('{}\n'.format(j))
-
--
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