autobox_test_rig/timing_example.py at master · aakepley/autobox_test_rig · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
## importing relevant libraries

import matplotlib.pyplot as plt
import numpy as np
import numpy.ma as ma
import pickle
import sys
import os
import glob
import math
%matplotlib inline
print("test")
from importlib import reload
import re
from astropy.table import Table

sys.path.append('/users/akepley/code')
import test_rig


## getting base data set

testDir = '../casa610-105_realdata'
tests = glob.glob(os.path.join(testDir,"*.*.*.*_*"))

casa610results = {}

for test in tests:
    project = os.path.basename(test)
    print(project)
    casa610results[project] = test_rig.tCleanTime_newlogs_simple(test)

print(len(casa610results))

# getting the test data set

testDir = '../build84_realdata_norestart'
tests = glob.glob(os.path.join(testDir,"*.*.*.*_*"))

#tests = [os.path.join(testDir,project) for project in projectList]
tests.remove(os.path.join(testDir,'2017.1.00019.S_2020_03_25T21_23_20.339'))

build84results = {}

for test in tests:
    project = os.path.basename(test)
    print(project)
    build84results[project] = test_rig.tCleanTime_newlogs_simple(test,serial=True)

print(len(build84results))

reload(test_rig)
results = test_rig.makeAstropyTimingTable(casa610results,build84results,serial=True)

## Getting the mask difference results

maskfile = '/lustre/naasc/sciops/comm/akepley/casa_test/cas9386/mask_diff_casa610-105_build84/casa610-105_build84_mask_comp.csv'

maskdb = Table.read(maskfile,format='ascii.csv',header_start=2)

results.add_column(np.zeros(len(results['imagename'])), name='nPixMaskBase')
results.add_column(np.zeros(len(results['imagename'])), name='nPixMaskTest')
results.add_column(ma.zeros(len(results['imagename'])), name='fracDiff')
results.add_column(np.zeros(len(results['imagename'])), name='nChanMaskBase')
results.add_column(np.zeros(len(results['imagename'])), name='nChanMaskTest')
results.add_column(np.zeros(len(results['imagename'])), name='maxMaskBase')
results.add_column(np.zeros(len(results['imagename'])), name='maxMaskTest')
results.add_column(np.zeros(len(results['imagename'])), name='FOVPixelsBase')
results.add_column(np.zeros(len(results['imagename'])), name='FOVPixelsTest')

fracDiffMask = np.ones(len(results['imagename']),dtype=bool)

for mask in maskdb:
    imagename = mask['Mask'].replace('iter1','iter0')

    idx = results['imagename'] == imagename

    if mask['fracDiff'] != '--':
        results['fracDiff'][idx] = float(mask['fracDiff'])
        fracDiffMask[idx] = False
    else:
        results['fracDiff'][idx] = 99
        fracDiffMask[idx] = True

    results['nPixMaskBase'][idx] = mask['nPixMaskBase']
    results['nPixMaskTest'][idx] = mask['nPixMaskTest']
    results['nChanMaskBase'][idx] = mask['nChanMaskBase']
    results['nChanMaskTest'][idx] = mask['nChanMaskTest']
    results['maxMaskBase'][idx] = mask['maxMaskBase']
    results['maxMaskTest'][idx] = mask['maxMaskTest']
    results['FOVPixelsBase'][idx] = mask['FOVPixelsBase']
    results['FOVPixelsTest'][idx] = mask['FOVPixelsTest']

results['fracDiff'].mask = fracDiffMask

## getting b75 results

b75 = Table.read('/lustre/naasc/sciops/comm/akepley/casa_test/cas9386/b75_calc/test_data_b75.csv',format='ascii.csv',header_start=1)

results.add_column(np.zeros(len(results['imagename'])), name='b75')

for line in b75 :
    idx = results['project'] == line['Project']
    nimage = len(results['project'][idx])

    results['b75'][idx] = np.repeat(line['b75'],nimage)

## Plotting different in time.

plt.figure(figsize=(8,6))
sp_12m = (results['specmode'] == 'cube') & (results['iter'] == 'iter0') & (results['gridder'] == 'standard') & (results['array'] == '12m')
mosaic_12m = (results['specmode'] == 'cube') & (results['iter'] == 'iter0') & (results['gridder'] == 'mosaic') & (results['array'] == '12m')
sp_7m = (results['specmode'] == 'cube') & (results['iter'] == 'iter0') & (results['gridder'] == 'standard') & (results['array'] == '7m')
mosaic_7m = (results['specmode'] == 'cube') & (results['iter'] == 'iter0') & (results['gridder'] == 'mosaic') & (results['array'] == '7m')
ephem = (results['project'] == '2017.1.00750.T_2020_03_22T21_23_03.693') & (results['specmode'] == 'cubesource') & (results['iter'] == 'iter0')

plt.scatter(results[sp_12m]['totalTime_casa610']/3600.0, results[sp_12m]['totalTime_pdiff'],label='single 12m')
plt.scatter(results[mosaic_12m]['totalTime_casa610']/3600.0,results[mosaic_12m]['totalTime_pdiff'],label='mosaic 12m')
plt.scatter(results[sp_7m]['totalTime_casa610']/3600.0,results[sp_7m]['totalTime_pdiff'],label='single 7m')
plt.scatter(results[mosaic_7m]['totalTime_casa610']/3600.0,results[mosaic_7m]['totalTime_pdiff'],label='mosaic 7m')
plt.scatter(results[ephem]['totalTime_casa610']/3600.0,results[ephem]['totalTime_pdiff'],marker='s',color='black',label='ephem')

plt.xlim(-1,12.5)
plt.ylim(-100,100)

plt.xlabel('Total Time for Whole Clean (hr)')
plt.ylabel("% Diff between CASA 6.1.0 and Build 84")
plt.title("Cube")
plt.axhline(0.0,color='gray',lw=2,linestyle=':')
plt.axhspan(-10,10,color='blue',alpha=0.25)
plt.axvspan(-0.25,0.4,color='blue',alpha=0.25)

plt.legend()


labelidx = ((results['specmode'] == 'cube') & (results['iter'] == 'iter0')
            & (results['totalTime_pdiff'] > 30) & (results['totalTime_casa610']/3600.0 > 2.0))
for image in results[labelidx]:
    name = '.'.join(image['imagename'].split('.')[2:4])
    plt.text(image['totalTime_casa610']/3600.00,image['totalTime_pdiff'],name,rotation=45)

labelidx = ((results['specmode'] == 'cube') & (results['iter'] == 'iter0')
            & (results['totalTime_pdiff'] > 10) & (results['totalTime_casa610']/3600.0 > 2.0))
for image in results[labelidx]:
    name = '.'.join(image['imagename'].split('.')[2:4])
    plt.text(image['totalTime_casa610']/3600.00,image['totalTime_pdiff'],name,rotation=45)