Scanning Astra evaluations¶

In [1]:

# Useful for debugging
%load_ext autoreload
%autoreload 2

# Useful for debugging %load_ext autoreload %autoreload 2

In [2]:

from astra import evaluate_astra_with_distgen

import matplotlib.pyplot as plt

import numpy as np
import os

from astra import evaluate_astra_with_distgen import matplotlib.pyplot as plt import numpy as np import os

In [3]:

# Input files
ASTRA_IN = 'templates/dcgun/astra.in'
DISTGEN_IN = 'templates/dcgun/distgen.yaml'

# Input files ASTRA_IN = 'templates/dcgun/astra.in' DISTGEN_IN = 'templates/dcgun/distgen.yaml'

In [4]:

# Run Astra with Distgen

# Basic settings
settings0 = {'zstop':1, 'zemit':10, 'zphase':10, 'phases':True}
settings0['distgen:n_particle'] = 2000

# Solenoid field
settings0['maxb(1)'] = 0.04

# Run Astra with Distgen # Basic settings settings0 = {'zstop':1, 'zemit':10, 'zphase':10, 'phases':True} settings0['distgen:n_particle'] = 2000 # Solenoid field settings0['maxb(1)'] = 0.04

In [5]:

# This is the basic function we will use
outputs = evaluate_astra_with_distgen(settings0, astra_input_file=ASTRA_IN,
                              distgen_input_file=DISTGEN_IN)
outputs

# This is the basic function we will use outputs = evaluate_astra_with_distgen(settings0, astra_input_file=ASTRA_IN, distgen_input_file=DISTGEN_IN) outputs

Out[5]:

{'error': False,
 'end_mean_z': 1.0,
 'end_mean_t': 4.0156e-09,
 'end_mean_x': -8.1128e-11,
 'end_sigma_x': 0.00034241,
 'end_sigma_xp': 0.00058502,
 'end_norm_emit_x': 1.7516000000000001e-07,
 'end_cov_x__xp': 1.72026784e-07,
 'end_mean_y': 5.7747e-09,
 'end_sigma_y': 0.00034245,
 'end_sigma_yp': 0.0005843,
 'end_norm_emit_y': 1.7455e-07,
 'end_cov_y__yp': 1.7197839e-07,
 'end_mean_kinetic_energy': 499790.0,
 'end_sigma_z': 0.0021695000000000004,
 'end_sigma_energy': 1.7883,
 'end_norm_emit_z': 0.0038791,
 'end_cov_z__energy': 7.779827000000001e-05,
 'end_n_particle_loss': 0,
 'end_total_charge': 9.999999999999999e-11,
 'end_higher_order_energy_spread': 1.7879755920478746,
 'fingerprint': 'c71d39dfb11bd6db0f35e04ba612b590'}

In [6]:

# Make a bare minimum function to evaluate
def f(b):
    s = settings0.copy()
    s['maxb(1)'] = b
    return evaluate_astra_with_distgen(s, astra_input_file=ASTRA_IN,
                          distgen_input_file=DISTGEN_IN)
# Make a list to scan
blist = np.linspace(0, 0.1, 20)
f(0)

# Make a bare minimum function to evaluate def f(b): s = settings0.copy() s['maxb(1)'] = b return evaluate_astra_with_distgen(s, astra_input_file=ASTRA_IN, distgen_input_file=DISTGEN_IN) # Make a list to scan blist = np.linspace(0, 0.1, 20) f(0)

Out[6]:

{'error': False,
 'end_mean_z': 1.0,
 'end_mean_t': 4.0156e-09,
 'end_mean_x': -3.6051e-09,
 'end_sigma_x': 0.0006159000000000001,
 'end_sigma_xp': 0.00065659,
 'end_norm_emit_x': 1.7486999999999998e-07,
 'end_cov_x__xp': 3.9120120300000003e-07,
 'end_mean_y': -5.7092e-09,
 'end_sigma_y': 0.00061615,
 'end_sigma_yp': 0.00065711,
 'end_norm_emit_y': 1.7486e-07,
 'end_cov_y__yp': 3.917050395e-07,
 'end_mean_kinetic_energy': 499790.0,
 'end_sigma_z': 0.0021695000000000004,
 'end_sigma_energy': 1.7883,
 'end_norm_emit_z': 0.0038791,
 'end_cov_z__energy': 7.776355800000001e-05,
 'end_n_particle_loss': 0,
 'end_total_charge': 9.999999999999999e-11,
 'end_higher_order_energy_spread': 1.7879772256019293,
 'fingerprint': 'f2d8e898c2ee41ce7a659b5a28b3ffb2'}

In [7]:

# Get a parallel executor

# Processes. These lines are needed to get this to work on macOS
import multiprocessing as mp                                                                                     
mp.set_start_method("fork")    
from concurrent.futures import ProcessPoolExecutor as Executor

#from concurrent.futures import ThreadPoolExecutor as Executor # DO NOT USE
#from dask.distributed import Client as Executor

executor=Executor()

# Get a parallel executor # Processes. These lines are needed to get this to work on macOS import multiprocessing as mp mp.set_start_method("fork") from concurrent.futures import ProcessPoolExecutor as Executor #from concurrent.futures import ThreadPoolExecutor as Executor # DO NOT USE #from dask.distributed import Client as Executor executor=Executor()

In [8]:

# Make a map object. Note that this does not execute immediately.
results = executor.map(f, blist)

# Make a map object. Note that this does not execute immediately. results = executor.map(f, blist)

In [9]:

# Actually evalute the funciton and gather the results
outputs = []
for r in results:
    outputs.append(r)
#    outputs.append(r.result())

# Actually evalute the funciton and gather the results outputs = [] for r in results: outputs.append(r) # outputs.append(r.result())

In [10]:

# Simple plot
plt.plot(blist, [o['end_sigma_x'] for o in outputs])

# Simple plot plt.plot(blist, [o['end_sigma_x'] for o in outputs])

Out[10]:

[<matplotlib.lines.Line2D at 0x1471dc790>]

Better: robust evaluation, form dataset¶

In [11]:

from tempfile import TemporaryDirectory
SCRATCH=TemporaryDirectory()

from tempfile import TemporaryDirectory SCRATCH=TemporaryDirectory()

In [12]:

# Make a robust evaluate
def evaluate(b):
    try:
        s = settings0.copy()
        s['maxb(1)'] = b
        output = evaluate_astra_with_distgen(s, astra_input_file=ASTRA_IN,
                              distgen_input_file=DISTGEN_IN, archive_path=SCRATCH.name)
        # Add the input
        output['maxb(1)'] = b
        output['Exception'] = False
    except:
        output = {}
        output['maxb(1)'] = b
        output['Exception'] = True
        
    return output

# Make a robust evaluate def evaluate(b): try: s = settings0.copy() s['maxb(1)'] = b output = evaluate_astra_with_distgen(s, astra_input_file=ASTRA_IN, distgen_input_file=DISTGEN_IN, archive_path=SCRATCH.name) # Add the input output['maxb(1)'] = b output['Exception'] = False except: output = {} output['maxb(1)'] = b output['Exception'] = True return output

In [13]:

SCRATCH.name

SCRATCH.name

Out[13]:

'/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn/T/tmp8jg8ann9'

In [14]:

# This will cause a crash in Astra
evaluate(1e9)

# This will cause a crash in Astra evaluate(1e9)

Out[14]:

{'maxb(1)': 1000000000.0, 'Exception': True}

In [15]:

# Get a new executor
executor =  Executor()

# Make a map object. Note that this does not execute immediately.

blist[-1] = 1e9 # Add something that will crash
results = executor.map(evaluate, blist)
# Actually evalute the funciton and gather the results
outputs = []
for r in results:
    outputs.append(r)

# Get a new executor executor = Executor() # Make a map object. Note that this does not execute immediately. blist[-1] = 1e9 # Add something that will crash results = executor.map(evaluate, blist) # Actually evalute the funciton and gather the results outputs = [] for r in results: outputs.append(r)

In [16]:

# Archive files are here
os.listdir(SCRATCH.name)

# Archive files are here os.listdir(SCRATCH.name)

Out[16]:

['d70364f093fe5d4a9d3acd0d0193fa91.h5',
 '284fa843911e69ab6122d23d79ad4806.h5',
 '926798389bda3d66c00620a39ddd3b8b.h5',
 'c191147e9f05abf7a8ba60eb73fbddbd.h5',
 'da89d10555a9b4c7b864fbb542ac497b.h5',
 '7b87b3f42160e0dc6a00aa7fa134604c.h5',
 '961dc67f12dc8c627e0a53ad04219881.h5',
 '1d54b0678b9ef6bfc42b0c2ff2120ae5.h5',
 '0a331d558139ac3a05c4cf676a8455b6.h5',
 '9a8e21bd4149448d46774270247b7c2b.h5',
 'd6fd70f4af4f613844fd65600267f9fb.h5',
 '7c0baedf140c3dbeef2fc3a73c15ea7b.h5',
 'dae144c959cdf177ec97e3b59f159015.h5',
 'efffe3c12cb38f91092589b38d338d29.h5',
 'aa14936ee8e37ca095d6d052bb7b2c04.h5',
 '47ce10b464da11518826a3c6d6654f42.h5',
 'e6bd6c13a2d22ad150081863f1c6fb59.h5',
 '1c33e9ed18d9ff88f91eda547cfe96cd.h5',
 '3124ef1903c236f1962db3ec8dacd2fe.h5']

Pandas¶

In [17]:

import pandas as pd

import pandas as pd

In [18]:

# Make a DataFrame
df = pd.DataFrame(outputs)
df

# Make a DataFrame df = pd.DataFrame(outputs) df

Out[18]:

	error	end_mean_z	end_mean_t	end_mean_x	end_sigma_x	end_sigma_xp	end_norm_emit_x	end_cov_x__xp	end_mean_y	end_sigma_y	...	end_sigma_energy	end_norm_emit_z	end_cov_z__energy	end_n_particle_loss	end_total_charge	end_higher_order_energy_spread	fingerprint	archive	maxb(1)	Exception
0	False	1.0	4.015600e-09	-3.605100e-09	0.000616	0.000657	1.748700e-07	3.912012e-07	-5.709200e-09	0.000616	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787977	d70364f093fe5d4a9d3acd0d0193fa91	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	0.000000e+00	False
1	False	1.0	4.015600e-09	-2.727100e-09	0.000608	0.000647	1.749400e-07	3.799807e-07	-5.900700e-09	0.000608	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787977	47ce10b464da11518826a3c6d6654f42	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	5.263158e-03	False
2	False	1.0	4.015600e-09	-1.654600e-09	0.000584	0.000621	1.750100e-07	3.477970e-07	-5.564700e-09	0.000584	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787977	284fa843911e69ab6122d23d79ad4806	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	1.052632e-02	False
3	False	1.0	4.015600e-09	-5.596500e-10	0.000546	0.000581	1.750700e-07	2.999282e-07	-4.652900e-09	0.000545	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787977	d6fd70f4af4f613844fd65600267f9fb	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	1.578947e-02	False
4	False	1.0	4.015600e-09	3.749800e-10	0.000496	0.000535	1.751200e-07	2.448360e-07	-3.170800e-09	0.000495	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	da89d10555a9b4c7b864fbb542ac497b	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	2.105263e-02	False
5	False	1.0	4.015600e-09	9.793000e-10	0.000439	0.000499	1.751500e-07	1.935768e-07	-1.156600e-09	0.000439	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	7c0baedf140c3dbeef2fc3a73c15ea7b	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	2.631579e-02	False
6	False	1.0	4.015600e-09	1.098900e-09	0.000384	0.000492	1.751700e-07	1.589409e-07	1.296300e-09	0.000384	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	7b87b3f42160e0dc6a00aa7fa134604c	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	3.157895e-02	False
7	False	1.0	4.015600e-09	5.689600e-10	0.000347	0.000534	1.751700e-07	1.545434e-07	4.052900e-09	0.000347	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	dae144c959cdf177ec97e3b59f159015	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	3.684211e-02	False
8	False	1.0	4.015600e-09	-6.653800e-10	0.000349	0.000629	1.751500e-07	1.937603e-07	6.928300e-09	0.000349	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	aa14936ee8e37ca095d6d052bb7b2c04	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	4.210526e-02	False
9	False	1.0	4.015600e-09	-2.662300e-09	0.000399	0.000768	1.751300e-07	2.886720e-07	9.753800e-09	0.000399	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	efffe3c12cb38f91092589b38d338d29	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	4.736842e-02	False
10	False	1.0	4.015600e-09	-5.442600e-09	0.000491	0.000938	1.751000e-07	4.490753e-07	1.230300e-08	0.000490	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	926798389bda3d66c00620a39ddd3b8b	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	5.263158e-02	False
11	False	1.0	4.015600e-09	-8.943500e-09	0.000610	0.001129	1.750800e-07	6.815942e-07	1.438200e-08	0.000610	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	1d54b0678b9ef6bfc42b0c2ff2120ae5	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	5.789474e-02	False
12	False	1.0	4.015600e-09	-1.305700e-08	0.000746	0.001333	1.750700e-07	9.890153e-07	1.580400e-08	0.000746	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	0a331d558139ac3a05c4cf676a8455b6	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	6.315789e-02	False
13	False	1.0	4.015600e-09	-1.761700e-08	0.000890	0.001543	1.751000e-07	1.369874e-06	1.640300e-08	0.000891	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787976	e6bd6c13a2d22ad150081863f1c6fb59	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	6.842105e-02	False
14	False	1.0	4.015600e-09	-2.254200e-08	0.001038	0.001754	1.751800e-07	1.818200e-06	1.601500e-08	0.001040	...	1.7883	0.003879	0.000078	0.0	1.000000e-10	1.787977	3124ef1903c236f1962db3ec8dacd2fe	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	7.368421e-02	False
15	False	1.0	4.015600e-09	-2.742500e-08	0.001186	0.001962	1.753100e-07	2.323812e-06	1.459800e-08	0.001189	...	1.7883	0.003879	0.000077	0.0	1.000000e-10	1.787978	961dc67f12dc8c627e0a53ad04219881	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	7.894737e-02	False
16	False	1.0	4.015600e-09	-3.215500e-08	0.001330	0.002161	1.755100e-07	2.871803e-06	1.223600e-08	0.001334	...	1.7883	0.003879	0.000077	0.0	1.000000e-10	1.787978	9a8e21bd4149448d46774270247b7c2b	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	8.421053e-02	False
17	False	1.0	4.015600e-09	-3.645000e-08	0.001467	0.002349	1.757800e-07	3.444868e-06	8.855100e-09	0.001472	...	1.7883	0.003879	0.000077	0.0	1.000000e-10	1.787979	c191147e9f05abf7a8ba60eb73fbddbd	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	8.947368e-02	False
18	False	1.0	4.015600e-09	-4.010700e-08	0.001595	0.002522	1.761000e-07	4.022045e-06	4.511600e-09	0.001601	...	1.7883	0.003879	0.000077	0.0	1.000000e-10	1.787980	1c33e9ed18d9ff88f91eda547cfe96cd	/var/folders/2f/l5_mybzs30j4qqvyj98w1_nw0000gn...	9.473684e-02	False
19	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.000000e+09	True

20 rows × 25 columns

In [19]:

# Get all runs without an exeption
filtered_df=df[~df['Exception']]

# Get all runs without an exeption filtered_df=df[~df['Exception']]

Visualize with Bokeh¶

In [20]:

from bokeh.plotting import figure, output_notebook, show

from bokeh.models import ColumnDataSource

# Allows plots to show up in the notebook
output_notebook()

from bokeh.plotting import figure, output_notebook, show from bokeh.models import ColumnDataSource # Allows plots to show up in the notebook output_notebook()

Loading BokehJS ...

In [21]:

# Form a Bokeh style data source
source = ColumnDataSource(filtered_df)
source.column_names

# Form a Bokeh style data source source = ColumnDataSource(filtered_df) source.column_names

Out[21]:

['index',
 'error',
 'end_mean_z',
 'end_mean_t',
 'end_mean_x',
 'end_sigma_x',
 'end_sigma_xp',
 'end_norm_emit_x',
 'end_cov_x__xp',
 'end_mean_y',
 'end_sigma_y',
 'end_sigma_yp',
 'end_norm_emit_y',
 'end_cov_y__yp',
 'end_mean_kinetic_energy',
 'end_sigma_z',
 'end_sigma_energy',
 'end_norm_emit_z',
 'end_cov_z__energy',
 'end_n_particle_loss',
 'end_total_charge',
 'end_higher_order_energy_spread',
 'fingerprint',
 'archive',
 'maxb(1)',
 'Exception']

In [22]:

# A simple plot
p = figure()
p.circle(x='maxb(1)', y='end_sigma_x', source=source)
show(p)

# A simple plot p = figure() p.circle(x='maxb(1)', y='end_sigma_x', source=source) show(p)

In [23]:

# Fancier plot

from bokeh.models.tools import HoverTool

hover = HoverTool()


hover.tooltips=[
    ('sigma_x', '@end_sigma_x'),
    ('norm_emit_x', '@end_norm_emit_x'),
    ('archive', '@archive')
]
p = figure(tools='tap')
p.circle(x='maxb(1)', y='end_sigma_x', source=source, size=10, color='green')

p.add_tools(hover)

show(p)

# Fancier plot from bokeh.models.tools import HoverTool hover = HoverTool() hover.tooltips=[ ('sigma_x', '@end_sigma_x'), ('norm_emit_x', '@end_norm_emit_x'), ('archive', '@archive') ] p = figure(tools='tap') p.circle(x='maxb(1)', y='end_sigma_x', source=source, size=10, color='green') p.add_tools(hover) show(p)

In [24]:

# Load all archives
from astra import Astra
Alist = []
for a in filtered_df['archive']:
    A = Astra()
    A.load_archive(a)
    Alist.append(A)

# Load all archives from astra import Astra Alist = [] for a in filtered_df['archive']: A = Astra() A.load_archive(a) Alist.append(A)

In [25]:

plt.plot(
    Alist[0].output['stats']['mean_z'],
    Alist[0].output['stats']['sigma_x']
)

plt.plot( Alist[0].output['stats']['mean_z'], Alist[0].output['stats']['sigma_x'] )

Out[25]:

[<matplotlib.lines.Line2D at 0x15774f4f0>]