diff --git a/collective_effects/__init__.py b/collective_effects/__init__.py
index d14360ffa3ea6025eeba8dd2a0b1cead9fb592f3..50123bb7163425ebe74e564e981faa67474b4e2d 100644
--- a/collective_effects/__init__.py
+++ b/collective_effects/__init__.py
@@ -9,8 +9,9 @@ from mbtrack2.collective_effects.instabilities import mbi_threshold, cbi_thresho
from mbtrack2.collective_effects.resistive_wall import skin_depth, CircularResistiveWall
from mbtrack2.collective_effects.resonator import Resonator
from mbtrack2.collective_effects.tapers import StupakovRectangularTaper, StupakovCircularTaper
-from mbtrack2.collective_effects.wakefield import ComplexData, Impedance, WakeFunction, ImpedanceModel, WakeField
+from mbtrack2.collective_effects.wakefield import ComplexData, Impedance, WakeFunction, WakeField
from mbtrack2.collective_effects.utilities import read_CST, read_IW2D, spectral_density
from mbtrack2.collective_effects.utilities import gaussian_bunch_spectrum, beam_spectrum, effective_impedance
from mbtrack2.collective_effects.utilities import yokoya_elliptic, beam_loss_factor
-from mbtrack2.collective_effects.utilities import double_sided_impedance
\ No newline at end of file
+from mbtrack2.collective_effects.utilities import double_sided_impedance
+from mbtrack2.collective_effects.impedance_model import ImpedanceModel
\ No newline at end of file
diff --git a/collective_effects/impedance_model.py b/collective_effects/impedance_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e1db30d0094dafd625d10f9cc09d9633b51fb1a
--- /dev/null
+++ b/collective_effects/impedance_model.py
@@ -0,0 +1,424 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 28 15:18:23 2020
+
+@author: gamelina
+"""
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import trapz
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+from mbtrack2.collective_effects.utilities import (beam_spectrum, gaussian_bunch_spectrum,
+ beam_loss_factor, spectral_density,
+ effective_impedance)
+from mbtrack2.collective_effects.wakefield import WakeField
+from mbtrack2.tracking.element import Element
+
+class ImpedanceModel(Element):
+ """
+ Define the impedance model of the machine.
+
+ Parameters
+ ----------
+ ring : Synchrotron object
+ wakefield_list : list of WakeField objects
+ WakeFields to add to the model.
+ wakefiled_postions : list
+ Longitudinal positions corresponding to the added Wakfields.
+
+ Attributes
+ ----------
+ wakefields : list of WakeField objects
+ WakeFields in the model.
+ positions : array
+ WakeFields positions.
+ names : array
+ Names of (unique) WakeField objects.
+ sum : WakeField
+ Sum of every WakeField in the model.
+ sum_"name" : WakeField
+ Sum of every Wakefield with the same "name".
+ sum_names : array
+ Names of attributes where the WakeFields are summed by name.
+
+ Methods
+ -------
+ add(wakefield_list, wakefiled_postions)
+ Add elements to the model.
+ add_multiple_elements(wakefield, wakefiled_postions)
+ Add the same element at different locations to the model.
+ sum_elements()
+ Sum all WakeFields into self.sum.
+ update_name_list()
+ Update self.names with uniques names of self.wakefields.
+ find_wakefield(name)
+ Return indexes of WakeFields with the same name in self.wakefields.
+ sum_by_name(name)
+ Sum the elements with the same name in the model into sum_name.
+ sum_by_name_all(name)
+ Sum all the elements with the same name in the model into sum_name.
+ plot_area(Z_type="Zlong", component="real", sigma=None, attr_list=None)
+ Plot the contributions of different kind of WakeFields.
+ """
+
+ def __init__(self, ring, wakefield_list=None, wakefiled_postions=None):
+ self.ring = ring
+ self.optics = self.ring.optics
+ self.wakefields = []
+ self.positions = np.array([])
+ self.names = np.array([])
+ self.sum_names = np.array([])
+ self.add(wakefield_list, wakefiled_postions)
+
+ def track(self, beam):
+ """
+ Track a beam object through this Element.
+
+ Parameters
+ ----------
+ beam : Beam object
+ """
+ raise NotImplementedError
+
+ def sum_elements(self):
+ """Sum all WakeFields into self.sum"""
+ beta = self.optics.beta(self.positions)
+ self.sum = WakeField.add_several_wakefields(self.wakefields, beta)
+ if "sum" not in self.sum_names:
+ self.sum_names = np.append(self.sum_names, "sum")
+
+ def update_name_list(self):
+ """Update self.names with uniques names of self.wakefields."""
+ for wakefield in self.wakefields:
+ if wakefield.name is None:
+ continue
+ if wakefield.name not in self.names:
+ self.names = np.append(self.names, wakefield.name)
+
+ def count_elements(self):
+ """Count number of each type of WakeField in the model."""
+ self.count = np.zeros(len(self.names))
+ for wakefield in self.wakefields:
+ if wakefield.name is not None:
+ self.count += (wakefield.name == self.names).astype(int)
+
+ def find_wakefield(self, name):
+ """
+ Return indexes of WakeFields with the same name in self.wakefields.
+
+ Parameters
+ ----------
+ name : str
+ WakeField name.
+
+ Returns
+ -------
+ index : list
+ Index of positions in self.wakefields.
+
+ """
+ index = []
+ for i, wakefield in enumerate(self.wakefields):
+ if wakefield.name == name:
+ index.append(i)
+ return index
+
+ def sum_by_name(self, name):
+ """
+ Sum the elements with the same name in the model into sum_name.
+
+ Parameters
+ ----------
+ name : str
+ Name of the WakeField to sum.
+ """
+ attribute_name = "sum_" + name
+ index = self.find_wakefield(name)
+ beta = self.optics.beta(self.positions[index])
+ wakes = []
+ for i in index:
+ wakes.append(self.wakefields[i])
+ wake_sum = WakeField.add_several_wakefields(wakes, beta)
+ setattr(self, attribute_name, wake_sum)
+ if attribute_name not in self.sum_names:
+ self.sum_names = np.append(self.sum_names, attribute_name)
+
+ def sum_by_name_all(self):
+ """
+ Sum all the elements with the same name in the model into sum_name.
+ """
+ for name in self.names:
+ self.sum_by_name(name)
+
+ def add(self, wakefield_list, wakefiled_postions):
+ """
+ Add elements to the model.
+
+ Parameters
+ ----------
+ wakefield_list : list of WakeField objects
+ WakeFields to add to the model.
+ wakefiled_postions : list
+ Longitudinal positions corresponding to the added Wakfields.
+ """
+ if (wakefield_list is not None) and (wakefiled_postions is not None):
+ for wakefield in wakefield_list:
+ self.wakefields.append(wakefield)
+
+ for position in wakefiled_postions:
+ self.positions = np.append(self.positions, position)
+
+ self.update_name_list()
+
+ def add_multiple_elements(self, wakefield, wakefiled_postions):
+ """
+ Add the same element at different locations to the model.
+
+ Parameters
+ ----------
+ WakeField : WakeField object
+ WakeField to add to the model.
+ wakefiled_postions : list
+ Longitudinal positions corresponding to the added Wakfield.
+ """
+ for position in wakefiled_postions:
+ self.positions = np.append(self.positions, position)
+ self.wakefields.append(wakefield)
+
+ self.update_name_list()
+
+ def plot_area(self, Z_type="Zlong", component="real", sigma=None,
+ attr_list=None, zoom=False):
+ """
+ Plot the contributions of different kind of WakeFields.
+
+ Parameters
+ ----------
+ Z_type : str, optional
+ Type of impedance to plot.
+ component : str, optional
+ Component to plot, can be "real" or "imag".
+ sigma : float, optional
+ RMS bunch length in [s] to use for the spectral density. If equal
+ to None, the spectral density is not plotted.
+ attr_list : list or array of str, optional
+ Attributes to plot.
+ zoom : bool
+ If True, add a zoomed plot on top right corner.
+
+ """
+ if attr_list is None:
+ attr_list = self.sum_names[self.sum_names != "sum"]
+
+ # manage legend
+ Ztype_dict = {"Zlong":0, "Zxdip":1, "Zydip":2, "Zxquad":3, "Zyquad":4}
+ scale = [1e-3, 1e-6, 1e-6, 1e-6, 1e-6]
+ label_list = [r"$Z_{long} \; [k\Omega]$",
+ r"$\sum_{j} \beta_{x,j} Z_{x,j}^{Dip} \; [M\Omega]$",
+ r"$\sum_{j} \beta_{y,j} Z_{y,j}^{Dip} \; [M\Omega]$",
+ r"$\sum_{j} \beta_{x,j} Z_{x,j}^{Quad} \; [M\Omega]$",
+ r"$\sum_{j} \beta_{y,j} Z_{y,j}^{Quad} \; [M\Omega]$"]
+ leg = Ztype_dict[Z_type]
+
+ # sort plot by decresing area size
+ area = np.zeros((len(attr_list),))
+ for index, attr in enumerate(attr_list):
+ try:
+ sum_imp = getattr(getattr(self, attr), Z_type)
+ area[index] = trapz(sum_imp.data[component], sum_imp.data.index)
+ except AttributeError:
+ pass
+ sorted_index = area.argsort()[::-1]
+
+ # Init fig
+ fig = plt.figure()
+ ax = fig.gca()
+ zero_impedance = getattr(self.sum, Z_type)*0
+ total_imp = 0
+ legend = []
+
+ if sigma is not None:
+ legend.append("Spectral density for sigma = " + str(sigma) + " s")
+
+ # Main plot
+ for index in sorted_index:
+ attr = attr_list[index]
+ # Set all impedances with common indexes using + zero_impedance
+ try:
+ sum_imp = getattr(getattr(self, attr), Z_type) + zero_impedance
+ ax.fill_between(sum_imp.data.index*1e-9, total_imp,
+ total_imp + sum_imp.data[component]*scale[leg])
+ total_imp += sum_imp.data[component]*scale[leg]
+ if attr[:4] == "sum_":
+ legend.append(attr[4:])
+ else:
+ legend.append(attr)
+ except AttributeError:
+ pass
+
+ if sigma is not None:
+ spect = spectral_density(zero_impedance.data.index, sigma)
+ spect = spect/spect.max()*total_imp.max()
+ ax.plot(sum_imp.data.index*1e-9, spect, 'r', linewidth=2.5)
+
+ ax.legend(legend, loc="upper left")
+ ax.set_xlabel("Frequency [GHz]")
+ ax.set_ylabel(label_list[leg] + " - " + component + " part")
+ ax.set_title(label_list[leg] + " - " + component + " part")
+
+ if zoom is True:
+ in_ax = inset_axes(ax,
+ width="30%", # width = 30% of parent_bbox
+ height=1.5, # height : 1 inch
+ loc=1)
+
+ total_imp = 0
+ for index in sorted_index:
+ attr = attr_list[index]
+ # Set all impedances with common indexes using + zero_impedance
+ try:
+ sum_imp = getattr(getattr(self, attr), Z_type) + zero_impedance
+ in_ax.fill_between(sum_imp.data.index*1e-3, total_imp,
+ total_imp + sum_imp.data[component]*1e-9)
+ total_imp += sum_imp.data[component]*1e-9
+ except AttributeError:
+ pass
+ in_ax.set_xlim([0, 200])
+ in_ax.set_xlabel("Frequency [kHz]")
+ in_ax.set_ylabel(r"$[G\Omega]$")
+
+ return fig
+
+ def effective_impedance(self, m, mu, sigma, M, tuneS, xi=None,
+ mode="Hermite"):
+ """
+ Compute the longitudinal and transverse effective impedance.
+
+ Parameters
+ ----------
+ mu : int
+ coupled bunch mode number, goes from 0 to (M-1) where M is the
+ number of bunches
+ m : int
+ head-tail (or azimutal/synchrotron) mode number
+ sigma : float
+ RMS bunch length in [s]
+ M : int
+ Number of bunches.
+ tuneS : float
+ Synchrotron tune.
+ xi : float, optional
+ (non-normalized) chromaticity
+ mode: str, optional
+ type of the mode taken into account for the computation:
+ -"Hermite" modes for Gaussian bunches
+
+ Returns
+ -------
+ summary : DataFrame
+ Longitudinal and transverse effective impedance.
+
+ """
+
+ attr_list = self.sum_names
+
+ eff_array = np.zeros((len(attr_list),3), dtype=complex)
+
+ for i, attr in enumerate(attr_list):
+ try:
+ impedance = getattr(getattr(self, attr), "Zlong")
+ eff_array[i,0] = effective_impedance(self.ring, impedance,
+ m, mu, sigma, M, tuneS,
+ xi, mode)
+ except AttributeError:
+ pass
+
+ try:
+ impedance = getattr(getattr(self, attr), "Zxdip")
+ eff_array[i,1] = effective_impedance(self.ring, impedance,
+ m, mu, sigma, M, tuneS,
+ xi, mode)
+ except AttributeError:
+ pass
+
+ try:
+ impedance = getattr(getattr(self, attr), "Zydip")
+ eff_array[i,2] = effective_impedance(self.ring, impedance,
+ m, mu, sigma, M, tuneS,
+ xi, mode)
+ except AttributeError:
+ pass
+
+ eff_array[:,0] = eff_array[:,0]*self.ring.omega0*1e3
+ eff_array[:,1] = eff_array[:,1]*1e-3
+ eff_array[:,2] = eff_array[:,2]*1e-3
+
+ summary = pd.DataFrame(eff_array, index=attr_list,
+ columns=["Z/n [mOhm]",
+ "sum betax x Zxeff [kOhm]",
+ "sum betay x Zyeff [kOhm]"])
+
+ return summary
+
+
+ def energy_loss(self, sigma, M, bunch_spacing, I, n_points=10e6):
+ """
+ Compute the beam and bunch loss factor and energy losses for each type
+ of element in the model assuming Gaussian bunches and constant spacing
+ between bunches.
+
+ Parameters
+ ----------
+ sigma : float
+ RMS bunch length in [s].
+ M : int
+ Number of bunches in the beam.
+ bunch_spacing : float
+ Time between two bunches in [s].
+ I : float
+ Total beam current in [A].
+ n_points : float, optional
+ Number of points used in the frequency spectrums.
+
+ Returns
+ -------
+ summary : Dataframe
+ Contains the beam and bunch loss factor and energy loss for the
+ full model and for each type of different component.
+
+ """
+
+ fmax = self.sum.Zlong.data.index.max()
+ fmin = self.sum.Zlong.data.index.min()
+
+ Q = I*self.ring.T0/M
+
+ if fmin >= 0:
+ fmin = -1*fmax
+ f = np.linspace(fmin, fmax, int(n_points))
+
+ beam_spect = beam_spectrum(f, M, bunch_spacing, sigma= sigma)
+
+ bunch_spect = gaussian_bunch_spectrum(f, sigma)
+
+ attr_list = self.sum_names
+
+ loss_array = np.zeros((len(attr_list),2))
+
+ for i, attr in enumerate(attr_list):
+ try:
+ impedance = getattr(getattr(self, attr), "Zlong")
+ loss_array[i,0] = beam_loss_factor(impedance, f, beam_spect, self.ring)
+ loss_array[i,1] = beam_loss_factor(impedance, f, bunch_spect, self.ring)
+ except AttributeError:
+ pass
+
+ loss_array = loss_array*1e-12
+ summary = pd.DataFrame(loss_array, index=attr_list,
+ columns=["loss factor (beam) [V/pC]", "loss factor (bunch) [V/pC]"])
+
+ summary["P (beam) [W]"] = summary["loss factor (beam) [V/pC]"]*1e12*Q**2/(self.ring.T0)
+ summary["P (bunch) [W]"] = summary["loss factor (bunch) [V/pC]"]*1e12*Q**2/(self.ring.T0)*M
+
+ return summary
diff --git a/collective_effects/wakefield.py b/collective_effects/wakefield.py
index 3895111b08909391a5a61888c1cc2e8d2d4be845..645a9f2958a6ecbfd80321928ebb96c4a73feb77 100644
--- a/collective_effects/wakefield.py
+++ b/collective_effects/wakefield.py
@@ -11,17 +11,11 @@ import warnings
import re
import pandas as pd
import numpy as np
-import matplotlib.pyplot as plt
import scipy as sc
from copy import deepcopy
from scipy.interpolate import interp1d
from scipy.integrate import trapz
from scipy.constants import c
-from mpl_toolkits.axes_grid1.inset_locator import inset_axes
-from mbtrack2.tracking.element import Element
-from mbtrack2.collective_effects.utilities import (beam_spectrum, gaussian_bunch_spectrum,
- beam_loss_factor, spectral_density,
- effective_impedance)
class ComplexData:
"""
@@ -770,411 +764,4 @@ class WakeField:
return wake_sum
else:
raise ValueError("Error in input.")
-
-class ImpedanceModel(Element):
- """
- Define the impedance model of the machine.
-
- Parameters
- ----------
- ring : Synchrotron object
- wakefield_list : list of WakeField objects
- WakeFields to add to the model.
- wakefiled_postions : list
- Longitudinal positions corresponding to the added Wakfields.
-
- Attributes
- ----------
- wakefields : list of WakeField objects
- WakeFields in the model.
- positions : array
- WakeFields positions.
- names : array
- Names of (unique) WakeField objects.
- sum : WakeField
- Sum of every WakeField in the model.
- sum_"name" : WakeField
- Sum of every Wakefield with the same "name".
- sum_names : array
- Names of attributes where the WakeFields are summed by name.
-
- Methods
- -------
- add(wakefield_list, wakefiled_postions)
- Add elements to the model.
- add_multiple_elements(wakefield, wakefiled_postions)
- Add the same element at different locations to the model.
- sum_elements()
- Sum all WakeFields into self.sum.
- update_name_list()
- Update self.names with uniques names of self.wakefields.
- find_wakefield(name)
- Return indexes of WakeFields with the same name in self.wakefields.
- sum_by_name(name)
- Sum the elements with the same name in the model into sum_name.
- sum_by_name_all(name)
- Sum all the elements with the same name in the model into sum_name.
- plot_area(Z_type="Zlong", component="real", sigma=None, attr_list=None)
- Plot the contributions of different kind of WakeFields.
- """
-
- def __init__(self, ring, wakefield_list=None, wakefiled_postions=None):
- self.ring = ring
- self.optics = self.ring.optics
- self.wakefields = []
- self.positions = np.array([])
- self.names = np.array([])
- self.sum_names = np.array([])
- self.add(wakefield_list, wakefiled_postions)
-
- def track(self, beam):
- """
- Track a beam object through this Element.
-
- Parameters
- ----------
- beam : Beam object
- """
- raise NotImplementedError
-
- def sum_elements(self):
- """Sum all WakeFields into self.sum"""
- beta = self.optics.beta(self.positions)
- self.sum = WakeField.add_several_wakefields(self.wakefields, beta)
- if "sum" not in self.sum_names:
- self.sum_names = np.append(self.sum_names, "sum")
-
- def update_name_list(self):
- """Update self.names with uniques names of self.wakefields."""
- for wakefield in self.wakefields:
- if wakefield.name is None:
- continue
- if wakefield.name not in self.names:
- self.names = np.append(self.names, wakefield.name)
-
- def count_elements(self):
- """Count number of each type of WakeField in the model."""
- self.count = np.zeros(len(self.names))
- for wakefield in self.wakefields:
- if wakefield.name is not None:
- self.count += (wakefield.name == self.names).astype(int)
-
- def find_wakefield(self, name):
- """
- Return indexes of WakeFields with the same name in self.wakefields.
-
- Parameters
- ----------
- name : str
- WakeField name.
-
- Returns
- -------
- index : list
- Index of positions in self.wakefields.
-
- """
- index = []
- for i, wakefield in enumerate(self.wakefields):
- if wakefield.name == name:
- index.append(i)
- return index
-
- def sum_by_name(self, name):
- """
- Sum the elements with the same name in the model into sum_name.
-
- Parameters
- ----------
- name : str
- Name of the WakeField to sum.
- """
- attribute_name = "sum_" + name
- index = self.find_wakefield(name)
- beta = self.optics.beta(self.positions[index])
- wakes = []
- for i in index:
- wakes.append(self.wakefields[i])
- wake_sum = WakeField.add_several_wakefields(wakes, beta)
- setattr(self, attribute_name, wake_sum)
- if attribute_name not in self.sum_names:
- self.sum_names = np.append(self.sum_names, attribute_name)
-
- def sum_by_name_all(self):
- """
- Sum all the elements with the same name in the model into sum_name.
- """
- for name in self.names:
- self.sum_by_name(name)
-
- def add(self, wakefield_list, wakefiled_postions):
- """
- Add elements to the model.
-
- Parameters
- ----------
- wakefield_list : list of WakeField objects
- WakeFields to add to the model.
- wakefiled_postions : list
- Longitudinal positions corresponding to the added Wakfields.
- """
- if (wakefield_list is not None) and (wakefiled_postions is not None):
- for wakefield in wakefield_list:
- self.wakefields.append(wakefield)
-
- for position in wakefiled_postions:
- self.positions = np.append(self.positions, position)
-
- self.update_name_list()
-
- def add_multiple_elements(self, wakefield, wakefiled_postions):
- """
- Add the same element at different locations to the model.
-
- Parameters
- ----------
- WakeField : WakeField object
- WakeField to add to the model.
- wakefiled_postions : list
- Longitudinal positions corresponding to the added Wakfield.
- """
- for position in wakefiled_postions:
- self.positions = np.append(self.positions, position)
- self.wakefields.append(wakefield)
-
- self.update_name_list()
-
- def plot_area(self, Z_type="Zlong", component="real", sigma=None,
- attr_list=None, zoom=False):
- """
- Plot the contributions of different kind of WakeFields.
-
- Parameters
- ----------
- Z_type : str, optional
- Type of impedance to plot.
- component : str, optional
- Component to plot, can be "real" or "imag".
- sigma : float, optional
- RMS bunch length in [s] to use for the spectral density. If equal
- to None, the spectral density is not plotted.
- attr_list : list or array of str, optional
- Attributes to plot.
- zoom : bool
- If True, add a zoomed plot on top right corner.
-
- """
- if attr_list is None:
- attr_list = self.sum_names[self.sum_names != "sum"]
-
- # manage legend
- Ztype_dict = {"Zlong":0, "Zxdip":1, "Zydip":2, "Zxquad":3, "Zyquad":4}
- scale = [1e-3, 1e-6, 1e-6, 1e-6, 1e-6]
- label_list = [r"$Z_{long} \; [k\Omega]$",
- r"$\sum_{j} \beta_{x,j} Z_{x,j}^{Dip} \; [M\Omega]$",
- r"$\sum_{j} \beta_{y,j} Z_{y,j}^{Dip} \; [M\Omega]$",
- r"$\sum_{j} \beta_{x,j} Z_{x,j}^{Quad} \; [M\Omega]$",
- r"$\sum_{j} \beta_{y,j} Z_{y,j}^{Quad} \; [M\Omega]$"]
- leg = Ztype_dict[Z_type]
-
- # sort plot by decresing area size
- area = np.zeros((len(attr_list),))
- for index, attr in enumerate(attr_list):
- try:
- sum_imp = getattr(getattr(self, attr), Z_type)
- area[index] = trapz(sum_imp.data[component], sum_imp.data.index)
- except AttributeError:
- pass
- sorted_index = area.argsort()[::-1]
-
- # Init fig
- fig = plt.figure()
- ax = fig.gca()
- zero_impedance = getattr(self.sum, Z_type)*0
- total_imp = 0
- legend = []
-
- if sigma is not None:
- legend.append("Spectral density for sigma = " + str(sigma) + " s")
-
- # Main plot
- for index in sorted_index:
- attr = attr_list[index]
- # Set all impedances with common indexes using + zero_impedance
- try:
- sum_imp = getattr(getattr(self, attr), Z_type) + zero_impedance
- ax.fill_between(sum_imp.data.index*1e-9, total_imp,
- total_imp + sum_imp.data[component]*scale[leg])
- total_imp += sum_imp.data[component]*scale[leg]
- if attr[:4] == "sum_":
- legend.append(attr[4:])
- else:
- legend.append(attr)
- except AttributeError:
- pass
-
- if sigma is not None:
- spect = spectral_density(zero_impedance.data.index, sigma)
- spect = spect/spect.max()*total_imp.max()
- ax.plot(sum_imp.data.index*1e-9, spect, 'r', linewidth=2.5)
-
- ax.legend(legend, loc="upper left")
- ax.set_xlabel("Frequency [GHz]")
- ax.set_ylabel(label_list[leg] + " - " + component + " part")
- ax.set_title(label_list[leg] + " - " + component + " part")
-
- if zoom is True:
- in_ax = inset_axes(ax,
- width="30%", # width = 30% of parent_bbox
- height=1.5, # height : 1 inch
- loc=1)
-
- total_imp = 0
- for index in sorted_index:
- attr = attr_list[index]
- # Set all impedances with common indexes using + zero_impedance
- try:
- sum_imp = getattr(getattr(self, attr), Z_type) + zero_impedance
- in_ax.fill_between(sum_imp.data.index*1e-3, total_imp,
- total_imp + sum_imp.data[component]*1e-9)
- total_imp += sum_imp.data[component]*1e-9
- except AttributeError:
- pass
- in_ax.set_xlim([0, 200])
- in_ax.set_xlabel("Frequency [kHz]")
- in_ax.set_ylabel(r"$[G\Omega]$")
-
- return fig
-
- def effective_impedance(self, m, mu, sigma, M, tuneS, xi=None,
- mode="Hermite"):
- """
- Compute the longitudinal and transverse effective impedance.
-
- Parameters
- ----------
- mu : int
- coupled bunch mode number, goes from 0 to (M-1) where M is the
- number of bunches
- m : int
- head-tail (or azimutal/synchrotron) mode number
- sigma : float
- RMS bunch length in [s]
- M : int
- Number of bunches.
- tuneS : float
- Synchrotron tune.
- xi : float, optional
- (non-normalized) chromaticity
- mode: str, optional
- type of the mode taken into account for the computation:
- -"Hermite" modes for Gaussian bunches
-
- Returns
- -------
- summary : DataFrame
- Longitudinal and transverse effective impedance.
-
- """
-
- attr_list = self.sum_names
-
- eff_array = np.zeros((len(attr_list),3), dtype=complex)
-
- for i, attr in enumerate(attr_list):
- try:
- impedance = getattr(getattr(self, attr), "Zlong")
- eff_array[i,0] = effective_impedance(self.ring, impedance,
- m, mu, sigma, M, tuneS,
- xi, mode)
- except AttributeError:
- pass
-
- try:
- impedance = getattr(getattr(self, attr), "Zxdip")
- eff_array[i,1] = effective_impedance(self.ring, impedance,
- m, mu, sigma, M, tuneS,
- xi, mode)
- except AttributeError:
- pass
-
- try:
- impedance = getattr(getattr(self, attr), "Zydip")
- eff_array[i,2] = effective_impedance(self.ring, impedance,
- m, mu, sigma, M, tuneS,
- xi, mode)
- except AttributeError:
- pass
-
- eff_array[:,0] = eff_array[:,0]*self.ring.omega0*1e3
- eff_array[:,1] = eff_array[:,1]*1e-3
- eff_array[:,2] = eff_array[:,2]*1e-3
-
- summary = pd.DataFrame(eff_array, index=attr_list,
- columns=["Z/n [mOhm]",
- "sum betax x Zxeff [kOhm]",
- "sum betay x Zyeff [kOhm]"])
-
- return summary
-
-
- def energy_loss(self, sigma, M, bunch_spacing, I, n_points=10e6):
- """
- Compute the beam and bunch loss factor and energy losses for each type
- of element in the model assuming Gaussian bunches and constant spacing
- between bunches.
-
- Parameters
- ----------
- sigma : float
- RMS bunch length in [s].
- M : int
- Number of bunches in the beam.
- bunch_spacing : float
- Time between two bunches in [s].
- I : float
- Total beam current in [A].
- n_points : float, optional
- Number of points used in the frequency spectrums.
-
- Returns
- -------
- summary : Dataframe
- Contains the beam and bunch loss factor and energy loss for the
- full model and for each type of different component.
-
- """
-
- fmax = self.sum.Zlong.data.index.max()
- fmin = self.sum.Zlong.data.index.min()
-
- Q = I*self.ring.T0/M
-
- if fmin >= 0:
- fmin = -1*fmax
- f = np.linspace(fmin, fmax, int(n_points))
-
- beam_spect = beam_spectrum(f, M, bunch_spacing, sigma= sigma)
-
- bunch_spect = gaussian_bunch_spectrum(f, sigma)
-
- attr_list = self.sum_names
-
- loss_array = np.zeros((len(attr_list),2))
-
- for i, attr in enumerate(attr_list):
- try:
- impedance = getattr(getattr(self, attr), "Zlong")
- loss_array[i,0] = beam_loss_factor(impedance, f, beam_spect, self.ring)
- loss_array[i,1] = beam_loss_factor(impedance, f, bunch_spect, self.ring)
- except AttributeError:
- pass
-
- loss_array = loss_array*1e-12
- summary = pd.DataFrame(loss_array, index=attr_list,
- columns=["loss factor (beam) [V/pC]", "loss factor (bunch) [V/pC]"])
-
- summary["P (beam) [W]"] = summary["loss factor (beam) [V/pC]"]*1e12*Q**2/(self.ring.T0)
- summary["P (bunch) [W]"] = summary["loss factor (bunch) [V/pC]"]*1e12*Q**2/(self.ring.T0)*M
-
- return summary
+
\ No newline at end of file