Layout Plotting¶

In [1]:

import os
import numpy as np

from impact import Impact

from impact.parsers import parse_impact_input
from impact.plotting import layout_plot
from impact.fieldmaps import ele_field, lattice_field

from bokeh.plotting import output_notebook
from bokeh.plotting import show


import matplotlib.pyplot as plt
from ipywidgets import interact

output_notebook(hide_banner=True)

import os import numpy as np from impact import Impact from impact.parsers import parse_impact_input from impact.plotting import layout_plot from impact.fieldmaps import ele_field, lattice_field from bokeh.plotting import output_notebook from bokeh.plotting import show import matplotlib.pyplot as plt from ipywidgets import interact output_notebook(hide_banner=True)

In [2]:

infile = "templates/lcls_injector/ImpactT.in"

infile = "templates/lcls_injector/ImpactT.in"

matplotlib plotting from the Impact object¶

In [3]:

I = Impact(infile)
I.plot(figsize=(12, 4))

I = Impact(infile) I.plot(figsize=(12, 4))

In [4]:

# Track 1 particle
I.track1()

# These are the fields seen by the bunch centroid
I.plot("Ez", y2="Bz")

# Track 1 particle I.track1() # These are the fields seen by the bunch centroid I.plot("Ez", y2="Bz")

In [5]:

# Fieldmaps can be added to existing axes

from impact.plot import add_fieldmaps_to_axes

fig, ax = plt.subplots()
add_fieldmaps_to_axes(I, t=1, ax=ax, bounds=(0, 10))

# Fieldmaps can be added to existing axes from impact.plot import add_fieldmaps_to_axes fig, ax = plt.subplots() add_fieldmaps_to_axes(I, t=1, ax=ax, bounds=(0, 10))

Low-level get field¶

In [6]:

# Get fieldmaps
fmaps = I.fieldmaps

# This shows the field from a single element
ele_field(I.ele["GUN"], z=0.1, fmaps=fmaps)

# Get fieldmaps fmaps = I.fieldmaps # This shows the field from a single element ele_field(I.ele["GUN"], z=0.1, fmaps=fmaps)

Out[6]:

np.float64(1738690.5187003822)

In [7]:

# this gets the total field from all elements in the lattice
lattice_field(I.lattice, z=0.3, fmaps=fmaps)

# this gets the total field from all elements in the lattice lattice_field(I.lattice, z=0.3, fmaps=fmaps)

Out[7]:

np.float64(0.0)

In [8]:

I.input["fieldmaps"].keys()

I.input["fieldmaps"].keys()

Out[8]:

dict_keys(['rfdata201', 'rfdata102', 'rfdata4', 'rfdata5', 'rfdata6', 'rfdata7'])

In [9]:

# Period in ps
T = 1 / 2856000000.0
T / 1e-12  # ps

# Period in ps T = 1 / 2856000000.0 T / 1e-12 # ps

Out[9]:

350.140056022409

In [10]:

I = Impact(infile)
I.plot(figsize=(12, 4), field_t=0)

I = Impact(infile) I.plot(figsize=(12, 4), field_t=0)

In [11]:

# Make a function to get a field array at a given phase/2pi

zz = np.linspace(0, I.stop, 1000)


def field_at(phi0):
    Ez_at = np.array([I.field(z=z, t=phi0 / 2856000000.0, component="Ez") for z in zz])
    plt.plot(zz, Ez_at / 1e6)
    plt.ylim(-100, 100)
    # plt.xlim(2, 2.2)


# def field_at(phi0):
#    t=phi0/2856000000.0
#    fig = I.plot(figsize=(12,4), field_t=t, return_figure=True)
#    fig.axes[0].set_ylim(-100,100)
#    fig.axes[1].set_ylim(-0, 0.3)

field_at(0)

# Make a function to get a field array at a given phase/2pi zz = np.linspace(0, I.stop, 1000) def field_at(phi0): Ez_at = np.array([I.field(z=z, t=phi0 / 2856000000.0, component="Ez") for z in zz]) plt.plot(zz, Ez_at / 1e6) plt.ylim(-100, 100) # plt.xlim(2, 2.2) # def field_at(phi0): # t=phi0/2856000000.0 # fig = I.plot(figsize=(12,4), field_t=t, return_figure=True) # fig.axes[0].set_ylim(-100,100) # fig.axes[1].set_ylim(-0, 0.3) field_at(0)

In [12]:

# Visualize the oscillating fields
interact(field_at, phi0=(0, 2, 0.01))

# Visualize the oscillating fields interact(field_at, phi0=(0, 2, 0.01))

Out[12]:

<function __main__.field_at(phi0)>

In [13]:

# These are the fields seen by the bunch
I.track1()
zz = I.stat("mean_z")
tt = I.stat("t")

Ez_seen = np.array(
    [
        lattice_field(I.lattice, z=z, t=t, fmaps=fmaps, component="Ez")
        for z, t in zip(zz, tt)
    ]
)
zz = zz[: len(tt)]
plt.plot(zz, Ez_seen)

# These are the fields seen by the bunch I.track1() zz = I.stat("mean_z") tt = I.stat("t") Ez_seen = np.array( [ lattice_field(I.lattice, z=z, t=t, fmaps=fmaps, component="Ez") for z, t in zip(zz, tt) ] ) zz = zz[: len(tt)] plt.plot(zz, Ez_seen)

Out[13]:

[<matplotlib.lines.Line2D at 0x7f2bd54ff230>]

In [14]:

# MeV energy gain
-np.trapz(Ez_seen, zz) / 1e6

# MeV energy gain -np.trapz(Ez_seen, zz) / 1e6

/tmp/ipykernel_2340/1062521924.py:2: DeprecationWarning: `trapz` is deprecated. Use `trapezoid` instead, or one of the numerical integration functions in `scipy.integrate`.
  -np.trapz(Ez_seen, zz) / 1e6

Out[14]:

np.float64(34.272824268974844)

In [15]:

# This is pretty close to the tracked kinetic energy
I.stat("mean_kinetic_energy")[-1] / 1e6

# This is pretty close to the tracked kinetic energy I.stat("mean_kinetic_energy")[-1] / 1e6

Out[15]:

np.float64(34.269711)

Bokeh plotting from the input file¶

In [16]:

infile = "templates/lcls_injector/ImpactT.in"
os.path.exists(infile)

dat = parse_impact_input(infile)
header = dat["header"]
lattice = dat["lattice"]

infile = "templates/lcls_injector/ImpactT.in" os.path.exists(infile) dat = parse_impact_input(infile) header = dat["header"] lattice = dat["lattice"]

In [17]:

layout = layout_plot(lattice)
show(layout)

layout = layout_plot(lattice) show(layout)

Output Plotting¶

In [18]:

from bokeh.plotting import figure
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, Range1d
from impact.parsers import load_many_fort
from impact.parsers import UNITS

from bokeh.plotting import figure from bokeh.layouts import column from bokeh.models import ColumnDataSource, Range1d from impact.parsers import load_many_fort from impact.parsers import UNITS

In [19]:

opath = "templates/lcls_injector/output/"
data = load_many_fort(opath, verbose=True)
ds = ColumnDataSource(data)

opath = "templates/lcls_injector/output/" data = load_many_fort(opath, verbose=True) ds = ColumnDataSource(data)

Loaded fort 18 : Time and energy
Loaded fort 24 : RMS X information
Loaded fort 25 : RMS Y information
Loaded fort 26 : RMS Z information
Loaded fort 27 : Max amplitude information
Loaded fort 28 : Load balance and loss diagnostics
Loaded fort 29 : Cube root of third moments of the beam distribution
Loaded fort 30 : Fourth root of the fourth moments of the beam distribution

In [20]:

def plot1(y_axis, x_axis="mean_z", source=ds):
    yunit = UNITS[y_axis]
    ylabel = y_axis + " (" + yunit + ")"

    xunit = UNITS[x_axis]
    xlabel = x_axis + " (" + xunit + ")"
    p = figure(width=600, height=200, x_axis_label=xlabel, y_axis_label=ylabel)
    p.line(x_axis, y_axis, source=source, color="red")
    return p


show(plot1("norm_emit_x"))

def plot1(y_axis, x_axis="mean_z", source=ds): yunit = UNITS[y_axis] ylabel = y_axis + " (" + yunit + ")" xunit = UNITS[x_axis] xlabel = x_axis + " (" + xunit + ")" p = figure(width=600, height=200, x_axis_label=xlabel, y_axis_label=ylabel) p.line(x_axis, y_axis, source=source, color="red") return p show(plot1("norm_emit_x"))

Stacking plots¶

This also links the panning, zooming

In [21]:

p1 = plot1("sigma_x")
p2 = plot1("norm_emit_x")
zmax = 2
layout.x_range = Range1d(0, zmax)
p1.x_range = Range1d(0, zmax)

layout.width = p1.width

# Link panning, zooming
layout.x_range = p1.x_range
p2.x_range = p1.x_range

show(column(p1, p2, layout))

p1 = plot1("sigma_x") p2 = plot1("norm_emit_x") zmax = 2 layout.x_range = Range1d(0, zmax) p1.x_range = Range1d(0, zmax) layout.width = p1.width # Link panning, zooming layout.x_range = p1.x_range p2.x_range = p1.x_range show(column(p1, p2, layout))