APEX Gun, IMPACT-T¶

In [1]:

from impact import Impact
from distgen import Generator

import os

# Nicer plotting
import matplotlib.pyplot as plt
import matplotlib

%config InlineBackend.figure_format = 'retina'
matplotlib.rcParams["figure.figsize"] = (8, 4)

from impact import Impact from distgen import Generator import os # Nicer plotting import matplotlib.pyplot as plt import matplotlib %config InlineBackend.figure_format = 'retina' matplotlib.rcParams["figure.figsize"] = (8, 4)

In [2]:

IMPACT_IN = "../templates/apex_gun/ImpactT.in"
DISTGEN_IN = "../templates/apex_gun/distgen.yaml"
NUMPROCS = 0
os.path.exists(IMPACT_IN)

IMPACT_IN = "../templates/apex_gun/ImpactT.in" DISTGEN_IN = "../templates/apex_gun/distgen.yaml" NUMPROCS = 0 os.path.exists(IMPACT_IN)

Out[2]:

True

In [3]:

G = Generator(DISTGEN_IN)
G["n_particle"] = 10000

G.run()
P0 = G.particles
factor = 1.01
P0.x *= factor
P0.y *= 1 / factor
P0.plot("x", "y")

G = Generator(DISTGEN_IN) G["n_particle"] = 10000 G.run() P0 = G.particles factor = 1.01 P0.x *= factor P0.y *= 1 / factor P0.plot("x", "y")

In [4]:

P0["charge"]

P0["charge"]

Out[4]:

np.float64(1.0000000000000004e-10)

In [5]:

# Make Impact object
I = Impact(IMPACT_IN)

# Make Impact object I = Impact(IMPACT_IN)

In [6]:

I.track1()
I.plot("mean_kinetic_energy")

I.track1() I.plot("mean_kinetic_energy")

In [7]:

I.initial_particles = P0

# Change some things
I.header["Nx"] = 32
I.header["Ny"] = 32
I.header["Nz"] = 32
I.header["Dt"] = 1e-13

# I.total_charge = P0['charge']
I.total_charge = 0  # Turn off space charge

# Other switches
I.timeout = 1000

# Change stop location
I.stop = 0.15

# Switches for MPI
I.numprocs = NUMPROCS

I.initial_particles = P0 # Change some things I.header["Nx"] = 32 I.header["Ny"] = 32 I.header["Nz"] = 32 I.header["Dt"] = 1e-13 # I.total_charge = P0['charge'] I.total_charge = 0 # Turn off space charge # Other switches I.timeout = 1000 # Change stop location I.stop = 0.15 # Switches for MPI I.numprocs = NUMPROCS

In [8]:

%%time
I.run()
print(I)

%%time I.run() print(I)

================ Impact-T Summary ================
10000 particles
1 bunch of electrons
total charge: 0.0 pC
Distribution type: read
Cathode start at z = 0 m
   emission time: 5.0643085710098247e-11 s
   image charges neglected after z = 0.02 m
Processor domain: 1 x 2 = 2 CPUs
Space charge grid: 32 x 32 x 32
Maximum time steps: 1000000
Reference Frequency: 1300000000.0 Hz
Initial reference time: -2.5403549006681168e-11 s
Simulation starting from the beginning
=================================================
Impact-T finished in /tmp/tmpfntozqdf
CPU times: user 292 ms, sys: 68.5 ms, total: 360 ms
Wall time: 45 s

Particles¶

In [9]:

# Particles are automatically parsed in to openpmd-beamphysics ParticleGroup objects
I.output["particles"]

# Particles are automatically parsed in to openpmd-beamphysics ParticleGroup objects I.output["particles"]

Out[9]:

{'initial_particles': <ParticleGroup with 10000 particles at 0x7f94cf286330>,
 'final_particles': <ParticleGroup with 10000 particles at 0x7f94cf4ac4a0>}

In [10]:

# Get the final particles, calculate some statistic
P = I.output["particles"]["final_particles"]
P["mean_energy"]

# Get the final particles, calculate some statistic P = I.output["particles"]["final_particles"] P["mean_energy"]

Out[10]:

np.float64(1269508.2687502417)

In [11]:

# Show the units
P.units("mean_energy")

# Show the units P.units("mean_energy")

Out[11]:

pmd_unit('eV', 1.602176634e-19, (2, 1, -2, 0, 0, 0, 0))

In [12]:

P.plot("z", "pz")

P.plot("z", "pz")

Stats¶

In [13]:

# Impact's own calculated statistics can be retieved
len(I.stat("norm_emit_x")), I.stat("norm_emit_x")[-1]

# Impact's own calculated statistics can be retieved len(I.stat("norm_emit_x")), I.stat("norm_emit_x")[-1]

Out[13]:

(1239, np.float64(9.6541591e-07))

In [14]:

# Stats can also be computed from the particles. For example:
I.particles["final_particles"]["norm_emit_x"]

# Stats can also be computed from the particles. For example: I.particles["final_particles"]["norm_emit_x"]

Out[14]:

np.float64(9.6552389497075e-07)

In [15]:

# Compare these.
key1 = "mean_z"
key2 = "sigma_x"
units1 = str(I.units(key1))
units2 = str(I.units(key2))
plt.xlabel(key1 + f" ({units1})")
plt.ylabel(key2 + f" ({units2})")
plt.plot(I.stat(key1), I.stat(key2))
plt.scatter(
    [I.particles[name][key1] for name in I.particles],
    [I.particles[name][key2] for name in I.particles],
    color="red",
)

# Compare these. key1 = "mean_z" key2 = "sigma_x" units1 = str(I.units(key1)) units2 = str(I.units(key2)) plt.xlabel(key1 + f" ({units1})") plt.ylabel(key2 + f" ({units2})") plt.plot(I.stat(key1), I.stat(key2)) plt.scatter( [I.particles[name][key1] for name in I.particles], [I.particles[name][key2] for name in I.particles], color="red", )

Out[15]:

<matplotlib.collections.PathCollection at 0x7f94c70dca40>