From 4efa952906fd1891bc9338969ebea371c0e370ae Mon Sep 17 00:00:00 2001
From: Gamelin Alexis <gamelin@synchrotron-soleil.fr>
Date: Fri, 24 Apr 2020 14:35:54 +0200
Subject: [PATCH] Tools : Add Yokoya_elliptic function + misc modif
Add Yokoya_elliptic function to compute Yokoya factors for an elliptic shape.
Add an option to divide by value in read_CST
Add docstrings to read_CST and read_IW2D
---
.../data/Yokoya_elliptic_from_Elias_USPAS.csv | 119 ++++++++++++++++++
collective_effects/tools.py | 108 ++++++++++++++--
2 files changed, 219 insertions(+), 8 deletions(-)
create mode 100644 collective_effects/data/Yokoya_elliptic_from_Elias_USPAS.csv
diff --git a/collective_effects/data/Yokoya_elliptic_from_Elias_USPAS.csv b/collective_effects/data/Yokoya_elliptic_from_Elias_USPAS.csv
new file mode 100644
index 0000000..88feaf6
--- /dev/null
+++ b/collective_effects/data/Yokoya_elliptic_from_Elias_USPAS.csv
@@ -0,0 +1,119 @@
+x,dipy,dipx,quady,quadx
+-1.38778e-17,1,1.00307,-0.00270479,0.00429341
+7.94873e-06,0.999988,1.00304,-0.00268229,0.0042735
+0.00131154,0.99802,0.998283,0.00100816,0.00100816
+0.0184543,0.972133,0.935779,0.0495391,-0.0419323
+0.0195168,0.970529,0.931905,0.0521597,-0.0445937
+0.0221637,0.966532,0.922254,0.0586882,-0.0511943
+0.0233454,0.96535,0.917945,0.0616028,-0.0541411
+0.036898,0.9518,0.86594,0.0950297,-0.0879369
+0.0376001,0.951098,0.863246,0.0965459,-0.0896878
+0.0390229,0.949675,0.857042,0.0996185,-0.0932359
+0.0404405,0.948258,0.850861,0.10268,-0.0967708
+0.046659,0.942615,0.823745,0.116109,-0.112278
+0.0579595,0.932362,0.789359,0.140512,-0.140458
+0.0585291,0.931845,0.787626,0.141742,-0.141878
+0.0596446,0.930833,0.784232,0.144151,-0.143994
+0.0639448,0.926931,0.774182,0.153438,-0.152153
+0.0765568,0.919567,0.744707,0.180674,-0.176081
+0.0790209,0.918129,0.737585,0.185995,-0.180757
+0.079368,0.917926,0.736581,0.18656,-0.181415
+0.0796277,0.917774,0.735831,0.186982,-0.181819
+0.098641,0.90472,0.680875,0.217927,-0.211402
+0.101367,0.902849,0.676101,0.222364,-0.215644
+0.101839,0.902525,0.675275,0.222994,-0.216378
+0.102833,0.902214,0.673535,0.224321,-0.217924
+0.121457,0.896392,0.640927,0.24919,-0.2469
+0.126297,0.893069,0.632452,0.255654,-0.254432
+0.126324,0.893051,0.632405,0.255689,-0.254465
+0.135123,0.887009,0.616999,0.267106,-0.265333
+0.143668,0.881143,0.605496,0.278192,-0.275888
+0.147338,0.880067,0.600556,0.282953,-0.28042
+0.148477,0.879733,0.599023,0.28408,-0.281827
+0.164594,0.875006,0.577327,0.300014,-0.295503
+0.169035,0.872782,0.571349,0.304404,-0.299271
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+0.177209,0.868689,0.561814,0.311798,-0.306206
+0.182895,0.865841,0.555182,0.31573,-0.31085
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+0.246069,0.853639,0.498241,0.350479,-0.349956
+0.246999,0.853501,0.497793,0.350922,-0.35039
+0.266624,0.850405,0.488348,0.36027,-0.359567
+0.269271,0.849578,0.487075,0.361531,-0.360804
+0.276534,0.847307,0.483579,0.364202,-0.3642
+0.286242,0.844273,0.478907,0.367772,-0.36641
+0.290515,0.843676,0.476851,0.369344,-0.367383
+0.302025,0.84207,0.47313,0.373577,-0.370004
+0.308485,0.841169,0.471041,0.375354,-0.371474
+0.317085,0.841142,0.468261,0.377721,-0.373432
+0.328118,0.841109,0.464694,0.380756,-0.375867
+0.328441,0.841064,0.46459,0.380845,-0.375938
+0.334771,0.840181,0.462842,0.382587,-0.377335
+0.350361,0.838005,0.458536,0.384111,-0.380775
+0.358946,0.837979,0.456166,0.384951,-0.382669
+0.364884,0.837961,0.454526,0.385532,-0.383162
+0.372612,0.837937,0.452392,0.386087,-0.383804
+0.372912,0.837844,0.452309,0.386108,-0.383829
+0.39223,0.831805,0.447911,0.387495,-0.385433
+0.396896,0.831791,0.446849,0.38783,-0.385821
+0.405467,0.831765,0.444897,0.388445,-0.387712
+0.413464,0.83174,0.443077,0.389451,-0.389476
+0.432805,0.831682,0.440122,0.391884,-0.393744
+0.438756,0.831664,0.439213,0.392633,-0.395057
+0.452603,0.831622,0.437097,0.394375,-0.395967
+0.454023,0.831617,0.43688,0.39457,-0.396061
+0.455057,0.831614,0.436727,0.394712,-0.396129
+0.487177,0.828947,0.431971,0.399129,-0.39824
+0.493006,0.828463,0.431108,0.399931,-0.398668
+0.495812,0.828454,0.430692,0.400316,-0.398874
+0.495892,0.828454,0.43068,0.400322,-0.39888
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+0.536396,0.828331,0.427621,0.40323,-0.401826
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+0.573045,0.828219,0.424852,0.403118,-0.404367
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+0.579393,0.8282,0.424372,0.40359,-0.404593
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+0.652895,0.822647,0.419392,0.405912,-0.404816
+0.662089,0.821953,0.41803,0.405884,-0.404844
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+0.670406,0.821851,0.417316,0.405859,-0.40487
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diff --git a/collective_effects/tools.py b/collective_effects/tools.py
index 7209a4b..d758e62 100644
--- a/collective_effects/tools.py
+++ b/collective_effects/tools.py
@@ -1,22 +1,41 @@
# -*- coding: utf-8 -*-
"""
-Tools and utilities functions for the Wakefield and impedances classes.
+Tools and utilities functions for the Wakefield and Impedances classes.
@author: Alexis Gamelin
-@date: 14/01/2020
+@date: 24/04/2020
"""
import pandas as pd
import numpy as np
from scipy.integrate import trapz
from collective_effects.wakefield import Wakefield, Impedance, WakeFunction
-#from ..Tracking.synchrotron import Synchrotron
-def read_CST(file, impedance_type='long'):
- """Read CST file format into an Impedance object."""
+def read_CST(file, impedance_type='long', divide_by=None):
+ """
+ Read CST file format into an Impedance object.
+
+ Parameters
+ ----------
+ file : str
+ Path to the file to read.
+ impedance_type : str, optional
+ Type of the Impedance object.
+ divide_by : float, optional
+ Divide the impedance by a value. Mainly used to normalize transverse
+ impedance by displacement.
+
+ Returns
+ -------
+ result : Impedance object
+ Data from file.
+ """
df = pd.read_csv(file, comment="#", header = None, sep = "\t",
names = ["Frequency","Real","Imaginary"])
df["Frequency"] = df["Frequency"]*1e9
+ if divide_by is not None:
+ df["Real"] = df["Real"]/divide_by
+ df["Imaginary"] = df["Imaginary"]/divide_by
df.set_index("Frequency", inplace = True)
result = Impedance(variable = df.index,
function = df["Real"] + 1j*df["Imaginary"],
@@ -24,7 +43,21 @@ def read_CST(file, impedance_type='long'):
return result
def read_IW2D(file, impedance_type='long'):
- """Read IW2D file format into an Impedance object."""
+ """
+ Read IW2D file format into an Impedance object.
+
+ Parameters
+ ----------
+ file : str
+ Path to the file to read.
+ impedance_type : str, optional
+ Type of the Impedance object.
+
+ Returns
+ -------
+ result : Impedance object
+ Data from file.
+ """
df = pd.read_csv(file, sep = ' ', header = None,
names = ["Frequency","Real","Imaginary"], skiprows=1)
df.set_index("Frequency", inplace = True)
@@ -61,12 +94,14 @@ def loss_factor(wake, sigma):
if(wake.impedance_type == "long"):
kloss = trapz(x = omega,
y = 1/np.pi*wake.data["real"][positive_index]*gaussian_spectrum)
- if(wake.impedance_type == "xdip" or wake.impedance_type == "ydip"):
+ elif(wake.impedance_type == "xdip" or wake.impedance_type == "ydip"):
kloss = trapz(x = omega,
y = 1/np.pi*wake.data["imag"][positive_index]*gaussian_spectrum)
- if(wake.impedance_type == "xquad" or wake.impedance_type == "yquad"):
+ elif(wake.impedance_type == "xquad" or wake.impedance_type == "yquad"):
kloss = trapz(x = omega,
y = 1/np.pi*wake.data["imag"][positive_index]*gaussian_spectrum)
+ else:
+ raise TypeError("Impedance type not recognized.")
if isinstance(wake, WakeFunction):
# To be implemented.
@@ -187,3 +222,60 @@ def effective_impedance(ring, imp, m, mu, sigma, xi=None, mode="Hermite"):
" and ydip impedance type.")
return Zeff
+
+
+def Yokoya_elliptic(x_radius , y_radius):
+ """
+ Compute Yokoya factors for an elliptic beam pipe.
+ Function adapted from N. Mounet IW2D.
+
+ Parameters
+ ----------
+ x_radius : float
+ Horizontal semi-axis of the ellipse in [m].
+ y_radius : float
+ Vertical semi-axis of the ellipse in [m].
+
+ Returns
+ -------
+ yoklong : float
+ Yokoya factor for the longitudinal impedance.
+ yokxdip : float
+ Yokoya factor for the dipolar horizontal impedance.
+ yokydip : float
+ Yokoya factor for the dipolar vertical impedance.
+ yokxquad : float
+ Yokoya factor for the quadrupolar horizontal impedance.
+ yokyquad : float
+ Yokoya factor for the quadrupolar vertical impedance.
+ """
+ if y_radius < x_radius:
+ small_semiaxis = y_radius
+ large_semiaxis = x_radius
+ else:
+ small_semiaxis = x_radius
+ large_semiaxis = y_radius
+
+ # read Yokoya factors interpolation file
+ # BEWARE: columns are ratio, dipy, dipx, quady, quadx
+ yokoya_file = pd.read_csv("collective_effects/data/" +
+ "Yokoya_elliptic_from_Elias_USPAS.csv")
+ ratio_col = yokoya_file["x"]
+ # compute semi-axes ratio (first column of this file)
+ ratio = (large_semiaxis - small_semiaxis)/(large_semiaxis + small_semiaxis)
+
+ # interpolate Yokoya file at the correct ratio
+ yoklong = 1
+
+ if y_radius < x_radius:
+ yokydip = np.interp(ratio, ratio_col, yokoya_file["dipy"])
+ yokxdip = np.interp(ratio, ratio_col, yokoya_file["dipx"])
+ yokyquad = np.interp(ratio, ratio_col, yokoya_file["quady"])
+ yokxquad = np.interp(ratio, ratio_col, yokoya_file["quadx"])
+ else:
+ yokxdip = np.interp(ratio, ratio_col, yokoya_file["dipy"])
+ yokydip = np.interp(ratio, ratio_col, yokoya_file["dipx"])
+ yokxquad = np.interp(ratio, ratio_col, yokoya_file["quady"])
+ yokyquad = np.interp(ratio, ratio_col, yokoya_file["quadx"])
+
+ return (yoklong, yokxdip, yokydip, yokxquad, yokyquad)
--
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