diff --git a/collective_effects/tools.py b/collective_effects/tools.py
index d758e62323b2d7df5b40c498b827fc1be788cdca..606db8b048577707c3eb2dc5a0f29629bf036516 100644
--- a/collective_effects/tools.py
+++ b/collective_effects/tools.py
@@ -9,6 +9,7 @@ Tools and utilities functions for the Wakefield and Impedances classes.
import pandas as pd
import numpy as np
from scipy.integrate import trapz
+from scipy.interpolate import interp1d
from collective_effects.wakefield import Wakefield, Impedance, WakeFunction
def read_CST(file, impedance_type='long', divide_by=None):
@@ -36,6 +37,8 @@ def read_CST(file, impedance_type='long', divide_by=None):
if divide_by is not None:
df["Real"] = df["Real"]/divide_by
df["Imaginary"] = df["Imaginary"]/divide_by
+ if impedance_type == "long":
+ df["Real"] = np.abs(df["Real"])
df.set_index("Frequency", inplace = True)
result = Impedance(variable = df.index,
function = df["Real"] + 1j*df["Imaginary"],
@@ -74,32 +77,36 @@ def loss_factor(wake, sigma):
Parameters
----------
- wake: Wakefield, Impedance or WakeFunction object.
- sigma: RMS bunch length in [s]
+ wake : Wakefield, Impedance or WakeFunction object.
+ sigma : float
+ RMS bunch length in [s]
Returns
-------
- kloss: loss factor in [V/C] or kick factor in [V/C/m] depanding on the
- impedance type.
+ kloss: float
+ Loss factor in [V/C] or kick factor in [V/C/m] depanding on the
+ impedance type.
- [1] Handbook of accelerator physics and engineering, 3rd printing.
+ References
+ ----------
+ [1] : Handbook of accelerator physics and engineering, 3rd printing.
"""
if isinstance(wake, Impedance):
positive_index = wake.data.index > 0
- omega = 2*np.pi*wake.data.index[positive_index]
- gaussian_spectrum = np.exp(-1*omega**2*sigma**2)
+ frequency = wake.data.index[positive_index]
+ sd = spectral_density(frequency, sigma, m=0)
if(wake.impedance_type == "long"):
- kloss = trapz(x = omega,
- y = 1/np.pi*wake.data["real"][positive_index]*gaussian_spectrum)
+ kloss = trapz(x = frequency,
+ y = 2*wake.data["real"][positive_index]*sd)
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)
+ kloss = trapz(x = frequency,
+ y = 2*wake.data["imag"][positive_index]*sd)
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)
+ kloss = trapz(x = frequency,
+ y = 2*wake.data["imag"][positive_index]*sd)
else:
raise TypeError("Impedance type not recognized.")
@@ -133,8 +140,10 @@ def spectral_density(frequency, sigma, m = 1, mode="Hermite"):
Returns
-------
numpy array
-
- [1] Handbook of accelerator physics and engineering, 3rd printing.
+
+ References
+ ----------
+ [1] : Handbook of accelerator physics and engineering, 3rd printing.
"""
if mode == "Hermite":
@@ -142,6 +151,70 @@ def spectral_density(frequency, sigma, m = 1, mode="Hermite"):
-1*(2*np.pi*frequency*sigma)**2)
else:
raise NotImplementedError("Not implemanted yet.")
+
+def Gaussian_bunch_spectrum(frequency, sigma):
+ """
+ Compute a Gaussian bunch spectrum [1].
+
+ Parameters
+ ----------
+ frequency : array
+ sample points of the beam spectrum in [Hz].
+ sigma : float
+ RMS bunch length in [s].
+
+ Returns
+ -------
+ bunch_spectrum : array
+ Bunch spectrum sampled at points given in frequency.
+
+ References
+ ----------
+ [1] : Gamelin, A. (2018). Collective effects in a transient microbunching
+ regime and ion cloud mitigation in ThomX. p86, Eq. 4.19
+ """
+ return np.exp(-1/2*(2*np.pi*frequency)**2*sigma**2)
+
+
+def beam_spectrum(frequency, M, bunch_spacing, sigma=None,
+ bunch_spectrum=None):
+ """
+ Compute the beam spectrum assuming constant spacing between bunches [1].
+
+ Parameters
+ ----------
+ frequency : list or numpy array
+ sample points of the beam spectrum in [Hz].
+ M : int
+ Number of bunches.
+ bunch_spacing : float
+ Time between two bunches in [s].
+ sigma : float, optional
+ If bunch_spectrum is None then a Gaussian bunch with sigma RMS bunch
+ length in [s] is assumed.
+ bunch_spectrum : array, optional
+ Bunch spectrum sampled at points given in frequency.
+
+ Returns
+ -------
+ beam_spectrum : array
+
+ References
+ ----------
+ [1] Rumolo, G - Beam Instabilities - CAS - CERN Accelerator School:
+ Advanced Accelerator Physics Course - 2014, Eq. 9
+ """
+
+ if bunch_spectrum is None:
+ bunch_spectrum = Gaussian_bunch_spectrum(frequency, sigma)
+
+ beam_spectrum = (bunch_spectrum * np.exp(1j*np.pi*frequency *
+ bunch_spacing*(M-1)) *
+ np.sin(M*np.pi*frequency*bunch_spacing) /
+ np.sin(np.pi*frequency*bunch_spacing))
+
+ return beam_spectrum
+
def effective_impedance(ring, imp, m, mu, sigma, xi=None, mode="Hermite"):
"""
@@ -279,3 +352,58 @@ def Yokoya_elliptic(x_radius , y_radius):
yokyquad = np.interp(ratio, ratio_col, yokoya_file["quadx"])
return (yoklong, yokxdip, yokydip, yokxquad, yokyquad)
+
+def beam_loss_factor(impedance, frequency, spectrum, ring):
+ """
+ Compute "beam" loss factor using the beam spectrum, uses a sum instead of
+ integral compared to loss_factor [1].
+
+ Parameters
+ ----------
+ impedance : Impedance of type "long"
+ frequency : array
+ Sample points of spectrum.
+ spectrum : array
+ Beam spectrum to consider.
+ ring : Synchrotron object
+
+ Returns
+ -------
+ kloss_beam : float
+ Beam loss factor in [V/C].
+
+ References
+ ----------
+ [1] : Handbook of accelerator physics and engineering, 3rd printing.
+ Eq (3) p239.
+ """
+ pmax = np.floor(impedance.data.index.max()/ring.f0)
+ pmin = np.floor(impedance.data.index.min()/ring.f0)
+
+ if pmin >= 0:
+ # Add negative part
+ negative_index = impedance.data.index*-1
+ negative_data = impedance.data.set_index(negative_index)
+ negative_data["imag"] = -1*negative_data["imag"]
+ negative_data = negative_data.drop(0)
+
+ all_data = impedance.data.append(negative_data)
+ all_data = all_data.sort_index()
+ impedance.data = all_data
+
+ pmin = -1*pmax
+
+ p = np.arange(pmin+1,pmax)
+ pf0 = p*ring.f0
+ ReZ = np.real(impedance(pf0))
+ spectral_density = np.abs(spectrum)**2
+ # interpolation of the spectrum is needed to avoid problems liked to
+ # division by 0
+ # computing the spectrum directly to the frequency points gives
+ # wrong results
+ spect = interp1d(frequency, spectral_density)
+ kloss_beam = ring.f0 * np.sum(ReZ*spect(pf0))
+
+ return kloss_beam
+
+
diff --git a/collective_effects/wakefield.py b/collective_effects/wakefield.py
index e604d9082a8c8ac8b6de3b9ad5b4f2ec80e49c24..015221e393fad2bc02ca85e7fea30f879b31848b 100644
--- a/collective_effects/wakefield.py
+++ b/collective_effects/wakefield.py
@@ -694,7 +694,7 @@ class ImpedanceModel(Element):
attr = attr_list[index]
# Set all impedances with common indexes using + zero_impedance
sum_imp = getattr(getattr(self, attr), Z_type) + zero_impedance
- ax.fill_between(sum_imp.data.index, total_imp,
+ ax.fill_between(sum_imp.data.index*1e-9, total_imp,
total_imp + sum_imp.data[component])
total_imp += sum_imp.data[component]
if attr_list is None:
@@ -705,16 +705,18 @@ class ImpedanceModel(Element):
if sigma is not None:
spect = tools.spectral_density(zero_impedance.data.index, sigma)
spect = spect/spect.max()*total_imp.max()
- ax.plot(sum_imp.data.index, spect, 'r', linewidth=2.5)
+ ax.plot(sum_imp.data.index*1e-9, spect, 'r', linewidth=2.5)
- ax.legend(legend)
- ax.set_xlabel("Frequency [Hz]")
+ ax.legend(legend, loc="upper left")
+ ax.set_xlabel("Frequency [GHz]")
if Z_type == "Zlong":
ax.set_ylabel(Z_type + " [Ohm] - " + component + " part")
else:
ax.set_ylabel(Z_type + " [Ohm/m] - " + component + " part")
+ return fig
+
def summary(self, sigma, attr_list=None, list_components=None):
if attr_list is None: