Impact-T fieldmap reconstruction¶

In [1]:

from impact import Impact, fieldmaps
from pmd_beamphysics.interfaces.impact import create_fourier_coefficients
import numpy as np

from impact import Impact, fieldmaps from pmd_beamphysics.interfaces.impact import create_fourier_coefficients import numpy as np

In [2]:

ifile = "templates/lcls_injector/ImpactT.in"
I = Impact(input_file=ifile)

ifile = "templates/lcls_injector/ImpactT.in" I = Impact(input_file=ifile)

In [3]:

# Fieldmaps are stored here
I.input["fieldmaps"].keys()

# Fieldmaps are stored here I.input["fieldmaps"].keys()

Out[3]:

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

In [4]:

# Look at a solrf element.
I.ele["SOL1"]

# Look at a solrf element. I.ele["SOL1"]

Out[4]:

{'description': 'name:SOL1',
 'original': '0.49308 0 0 105 0.0 0.0 0.0 0.0 102 0.15 0.0 0.0 0.0 0.0 0.0 0.2457 /!name:SOL1',
 'L': 0.49308,
 'type': 'solrf',
 'zedge': 0.0,
 'rf_field_scale': 0.0,
 'rf_frequency': 0.0,
 'theta0_deg': 0.0,
 'filename': 'rfdata102',
 'radius': 0.15,
 'x_offset': 0.0,
 'y_offset': 0.0,
 'x_rotation': 0.0,
 'y_rotation': 0.0,
 'z_rotation': 0.0,
 'solenoid_field_scale': 0.2457,
 's': 0.49308,
 'name': 'SOL1'}

In [5]:

# This is its fieldmap filename
I.ele["SOL1"]["filename"]

# This is its fieldmap filename I.ele["SOL1"]["filename"]

Out[5]:

'rfdata102'

In [6]:

# That data is here.
fmap = I.input["fieldmaps"]["rfdata102"]
fmap

# That data is here. fmap = I.input["fieldmaps"]["rfdata102"] fmap

Out[6]:

{'info': {'format': 'solrf',
  'filePath': '/home/runner/work/lume-impact/lume-impact/docs/examples/templates/lcls_injector/rfdata102'},
 'field': {'Ez': {'z0': np.float64(0.0),
   'z1': np.float64(0.0),
   'L': np.float64(0.0),
   'fourier_coefficients': array([0.])},
  'Bz': {'z0': np.float64(-0.53308),
   'z1': np.float64(0.49308),
   'L': np.float64(1.02616),
   'fourier_coefficients': array([ 7.59104159e-01,  1.86860998e-01,  2.68209187e-09, -4.75732117e-01,
           5.68488796e-09, -2.63303014e-01, -2.00685489e-10,  8.31385362e-02,
          -1.07061180e-09,  3.32147505e-02,  3.34109023e-09, -4.03166145e-03,
           2.36977653e-09,  7.55695585e-02,  7.03628508e-09,  4.05472421e-02,
          -7.93679210e-09, -2.78630279e-02, -2.80674359e-08, -1.23309101e-02,
          -1.21750197e-08, -1.11917490e-03,  2.72183881e-08, -1.76941899e-02,
           2.07775590e-08, -6.82928971e-03, -4.64611502e-09,  9.04327137e-03,
          -2.74644209e-08,  3.46208848e-03,  1.22819960e-08,  4.56318358e-04,
           2.11520498e-08,  4.27450120e-03, -2.88644504e-08,  7.52482987e-04,
          -3.59752217e-08, -2.87393685e-03,  1.26542743e-08, -8.38592686e-04,
           3.01815044e-08, -1.30737597e-04, -9.86177028e-09, -1.04357300e-03,
          -6.63856936e-09, -2.53914168e-06,  2.18126093e-08,  8.16316559e-04,
          -9.00342349e-09,  1.78532652e-04, -3.75346464e-08, -1.85656369e-05,
           1.66112357e-08,  2.70906299e-04,  4.30436466e-08, -6.91061736e-05,
           1.79889178e-08, -3.21395974e-04, -3.28634884e-08, -6.26834704e-05,
           4.78721227e-08,  4.53788783e-05, -2.40687516e-08, -7.00121686e-05,
          -1.09529996e-08,  5.88552784e-06, -3.18410200e-08,  9.16404141e-05,
           2.20705961e-08, -1.40213052e-05,  8.62505486e-08, -7.90063994e-05,
           1.80561930e-08,  9.73707320e-06, -3.55099131e-08, -3.24265398e-05,
           8.04201228e-09, -2.52641442e-05,  2.39895303e-08,  7.72642702e-07,
          -1.86279094e-08,  3.37057351e-05, -5.60221048e-08,  8.99206468e-06,
           3.60553118e-08, -1.69794491e-05,  4.76241852e-08, -8.20592718e-06,
          -3.75317080e-08, -1.46785073e-05, -1.35667390e-08, -8.04441471e-06,
           9.17261718e-08, -3.13719566e-05,  5.84058221e-08, -4.49735425e-06,
          -1.30484352e-07, -1.47358475e-05, -1.10775372e-07,  1.89208186e-05,
           5.67838231e-08, -3.75811096e-05,  6.90257455e-08, -2.31210131e-05,
          -2.80681931e-08,  4.15569850e-07, -3.99391984e-08,  2.35273720e-05,
           1.13469662e-07, -2.13950099e-05,  1.17155810e-08, -2.05874941e-05,
          -6.31687124e-08,  2.11433614e-05, -9.36452667e-08, -3.11181571e-05,
          -6.42682642e-08, -1.59322792e-05, -1.78752181e-08])}}}

In [7]:

# Reconstruction function
fieldmaps.fieldmap_reconstruction_solrf(fmap["field"]["Bz"], 0)

# Reconstruction function fieldmaps.fieldmap_reconstruction_solrf(fmap["field"]["Bz"], 0)

Out[7]:

np.float64(0.006497827018877966)

Basic plot¶

In [8]:

import matplotlib.pyplot as plt

%matplotlib inline
%config InlineBackend.figure_format='retina'

import matplotlib.pyplot as plt %matplotlib inline %config InlineBackend.figure_format='retina'

In [9]:

zlist = np.linspace(0, 0.49308, 1000)
fieldlist = [
    fieldmaps.fieldmap_reconstruction_solrf(fmap["field"]["Bz"], z) for z in zlist
]

zlist = np.linspace(0, 0.49308, 1000) fieldlist = [ fieldmaps.fieldmap_reconstruction_solrf(fmap["field"]["Bz"], z) for z in zlist ]

In [10]:

# z at max field
zlist[np.argmax(np.array(fieldlist))]

# z at max field zlist[np.argmax(np.array(fieldlist))]

Out[10]:

np.float64(0.19496156156156158)

In [11]:

plt.plot(zlist, fieldlist);

plt.plot(zlist, fieldlist);

In [12]:

# Integrated field (approximate)
field_scale = 0.243  # from imput file
BL = np.sum(fieldlist) * 0.49308 / 1000  # T*m
BL * field_scale * 10  # T*m -> kG*m

# Integrated field (approximate) field_scale = 0.243 # from imput file BL = np.sum(fieldlist) * 0.49308 / 1000 # T*m BL * field_scale * 10 # T*m -> kG*m

Out[12]:

np.float64(0.47251221486003464)

In [13]:

1 / BL

1 / BL

Out[13]:

np.float64(5.142724195436521)

Create Fieldmap¶

In [14]:

fmap2 = fmap.copy()
fmap2["field"]["Bz"]["z0"] = min(zlist)
fmap2["field"]["Bz"]["z1"] = max(zlist)
fmap2["field"]["Bz"]["L"] = np.ptp(zlist)
fmap2["field"]["Bz"]["fourier_coefficients"] = create_fourier_coefficients(
    zlist, fieldlist, n=20
)

fmap2 = fmap.copy() fmap2["field"]["Bz"]["z0"] = min(zlist) fmap2["field"]["Bz"]["z1"] = max(zlist) fmap2["field"]["Bz"]["L"] = np.ptp(zlist) fmap2["field"]["Bz"]["fourier_coefficients"] = create_fourier_coefficients( zlist, fieldlist, n=20 )

In [15]:

fieldlist2 = [
    fieldmaps.fieldmap_reconstruction_solrf(fmap2["field"]["Bz"], z) for z in zlist
]
plt.plot(zlist, fieldlist, label="original")
plt.plot(zlist, fieldlist2, "--", label="created")
plt.legend()

fieldlist2 = [ fieldmaps.fieldmap_reconstruction_solrf(fmap2["field"]["Bz"], z) for z in zlist ] plt.plot(zlist, fieldlist, label="original") plt.plot(zlist, fieldlist2, "--", label="created") plt.legend()

Out[15]:

<matplotlib.legend.Legend at 0x7f02e21a5d90>

In [16]:

fmap2

fmap2

Out[16]:

{'info': {'format': 'solrf',
  'filePath': '/home/runner/work/lume-impact/lume-impact/docs/examples/templates/lcls_injector/rfdata102'},
 'field': {'Ez': {'z0': np.float64(0.0),
   'z1': np.float64(0.0),
   'L': np.float64(0.0),
   'fourier_coefficients': array([0.])},
  'Bz': {'z0': np.float64(0.0),
   'z1': np.float64(0.49308),
   'L': np.float64(0.49308),
   'fourier_coefficients': array([ 7.89495139e-01,  4.33867017e-01, -3.57732944e-01,  2.94026907e-02,
          -1.53404612e-01,  3.25260823e-02,  5.58082653e-02,  6.13403407e-02,
           2.39379983e-02,  3.57981962e-03, -1.24552364e-03, -1.02459657e-02,
           1.68094954e-02,  1.23265799e-03,  7.41061917e-03, -2.88377447e-03,
          -2.34475616e-03, -3.69833230e-03, -3.94627426e-04, -5.82441696e-05,
           1.26215240e-04,  3.02765112e-04, -1.36723089e-03, -2.26429980e-04,
          -2.39596500e-04,  3.45355578e-04, -4.64071534e-05,  2.62624018e-04,
           2.21612127e-05,  6.48490182e-05, -1.18646462e-04,  1.19955063e-05,
           2.07495250e-04,  6.46051459e-05, -2.35246836e-05, -3.25265093e-05,
           1.09808260e-04, -1.75593316e-05, -9.23404592e-05])}}}

In [17]:

from numpy import sin, cos, pi, arange

from numpy import sin, cos, pi, arange

In [18]:

# Raw data from
coefs = fmap2["field"]["Bz"]["fourier_coefficients"]
coefs

# Raw data from coefs = fmap2["field"]["Bz"]["fourier_coefficients"] coefs

Out[18]:

array([ 7.89495139e-01,  4.33867017e-01, -3.57732944e-01,  2.94026907e-02,
       -1.53404612e-01,  3.25260823e-02,  5.58082653e-02,  6.13403407e-02,
        2.39379983e-02,  3.57981962e-03, -1.24552364e-03, -1.02459657e-02,
        1.68094954e-02,  1.23265799e-03,  7.41061917e-03, -2.88377447e-03,
       -2.34475616e-03, -3.69833230e-03, -3.94627426e-04, -5.82441696e-05,
        1.26215240e-04,  3.02765112e-04, -1.36723089e-03, -2.26429980e-04,
       -2.39596500e-04,  3.45355578e-04, -4.64071534e-05,  2.62624018e-04,
        2.21612127e-05,  6.48490182e-05, -1.18646462e-04,  1.19955063e-05,
        2.07495250e-04,  6.46051459e-05, -2.35246836e-05, -3.25265093e-05,
        1.09808260e-04, -1.75593316e-05, -9.23404592e-05])

In [19]:

A0 = coefs[0]  # constant factor
A = coefs[1::2]  # cos parts
B = coefs[2::2]  # sin parts

A0 = coefs[0] # constant factor A = coefs[1::2] # cos parts B = coefs[2::2] # sin parts

In [20]:

L = 0.6


@np.vectorize
def f(z):
    phase = 2 * pi * (z / L - 1 / 2)
    return A0 / 2 + sum(
        [
            A[n - 1] * cos(n * phase) + B[n - 1] * sin(n * phase)
            for n in arange(1, len(A) + 1)
        ]
    )


z0 = np.linspace(0, L, 100)

plt.plot(z0, f(z0))

L = 0.6 @np.vectorize def f(z): phase = 2 * pi * (z / L - 1 / 2) return A0 / 2 + sum( [ A[n - 1] * cos(n * phase) + B[n - 1] * sin(n * phase) for n in arange(1, len(A) + 1) ] ) z0 = np.linspace(0, L, 100) plt.plot(z0, f(z0))

Out[20]:

[<matplotlib.lines.Line2D at 0x7f02da0f1e50>]