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Copy pathVLBI_pipe_functions.py
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VLBI_pipe_functions.py
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import re, os, json, inspect, sys, copy, glob, tarfile, random, math, shutil, ast
import collections
from collections import OrderedDict
try:
from collections import Mapping, Iterable
except:
from collections.abc import Mapping, Iterable
## Numerical routines
import numpy as np
## Plotting routines
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib import gridspec
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.lines as mlines
## Sci-py dependencies
from scipy.interpolate import interp1d
from scipy import signal
from scipy.constants import c as speed_light
from itertools import cycle
from scipy.special import j1
from os import environ
try:
# CASA 6
import casatools
from casatasks import *
casalog.showconsole(True)
casa6=True
except:
# CASA 5
from casac import casac as casatools
from taskinit import *
from bandpass_cli import bandpass_cli as bandpass
from importfitsidi_cli import importfitsidi_cli as importfitsidi
from flagdata_cli import flagdata_cli as flagdata
from applycal_cli import applycal_cli as applycal
from split_cli import split_cli as split
from gencal_cli import gencal_cli as gencal
from partition_cli import partition_cli as partition
from tclean_cli import tclean_cli as tclean
from statwt_cli import statwt_cli as statwt
casa6=False
try:
from astropy.io import fits
except:
import pyfits as fits
class NpEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(NpEncoder, self).default(obj)
def json_load_byteified(file_handle):
return _byteify(
json.load(file_handle, object_hook=_byteify),
ignore_dicts=True
)
def json_loads_byteified(json_text):
return _byteify(
json.loads(json_text, object_hook=_byteify),
ignore_dicts=True
)
def json_load_byteified_dict(file_handle,casa6):
if casa6==True:
return convert_temp(_byteify(
json.load(file_handle, object_hook=_byteify, object_pairs_hook=OrderedDict),
ignore_dicts=True))
else:
return convert(_byteify(
json.load(file_handle, object_hook=_byteify, object_pairs_hook=OrderedDict),
ignore_dicts=True))
def json_loads_byteified_dict(json_text,casa6):
if casa6==True:
return convert_temp(_byteify(
json.loads(json_text, object_hook=_byteify, object_pairs_hook=OrderedDict),
ignore_dicts=True))
else:
return convert(_byteify(
json.loads(json_text, object_hook=_byteify, object_pairs_hook=OrderedDict),
ignore_dicts=True))
def convert(data):
if isinstance(data, basestring):
return str(data)
elif isinstance(data, Mapping):
return OrderedDict(map(convert, data.iteritems()))
elif isinstance(data, Iterable):
return type(data)(map(convert, data))
else:
return data
def convert_temp(data):
if isinstance(data, str):
return str(data)
elif isinstance(data, Mapping):
return OrderedDict(map(convert_temp, data.items()))
elif isinstance(data, Iterable):
return type(data)(map(convert_temp, data))
else:
return data
def _byteify(data, ignore_dicts=False):
# if this is a unicode string, return its string representation
try:
if isinstance(data, unicode):
return data.encode('utf-8')
except:
if isinstance(data, str):
return data
# if this is a list of values, return list of byteified values
if isinstance(data, list):
return [ _byteify(item, ignore_dicts=True) for item in data ]
# if this is a dictionary, return dictionary of byteified keys and values
# but only if we haven't already byteified it
if isinstance(data, dict) and not ignore_dicts:
try:
return {
_byteify(key, ignore_dicts=True): _byteify(value, ignore_dicts=True)
for key, value in data.iteritems()
}
except:
return {
_byteify(key, ignore_dicts=True): _byteify(value, ignore_dicts=True)
for key, value in data.items()
}
# if it's anything else, return it in its original form
return data
def load_json(filename,Odict=False,casa6=False):
if Odict==False:
with open(filename, "r") as f:
json_data = json_load_byteified(f)
f.close()
else:
with open(filename, "r") as f:
json_data = json_load_byteified_dict(f,casa6)
f.close()
return json_data
def save_json(filename,array,append=False):
if append==False:
write_mode='w'
else:
write_mode='a'
with open(filename, write_mode) as f:
json.dump(array, f,indent=4, separators=(',', ': '),cls=NpEncoder)
f.close()
def headless(inputfile):
''' Parse the list of inputs given in the specified file. (Modified from evn_funcs.py)'''
INPUTFILE = open(inputfile, "r")
control=collections.OrderedDict()
# a few useful regular expressions
newline = re.compile(r'\n')
space = re.compile(r'\s')
char = re.compile(r'\w')
comment = re.compile(r'#.*')
# parse the input file assuming '=' is used to separate names from values
for line in INPUTFILE:
if char.match(line):
line = comment.sub(r'', line)
line = line.replace("'", '')
(param, value) = line.split('=')
param = newline.sub(r'', param)
param = param.strip()
param = space.sub(r'', param)
value = newline.sub(r'', value)
value = value.replace(' ','').strip()
valuelist = value.split(',')
if len(valuelist) == 1:
if valuelist[0] == '0' or valuelist[0]=='1' or valuelist[0]=='2':
control[param] = int(valuelist[0])
else:
control[param] = str(valuelist[0])
else:
control[param] = ','.join(valuelist)
return control
def rmfiles(files):
func_name = inspect.stack()[0][3]
for i in files:
if "*" in i:
files_to_die = glob.glob(i)
casalog.post(priority="INFO",origin=func_name,message='Files matching with %s - deleting'% i)
for j in files_to_die:
if os.path.exists(j) == True:
casalog.post(priority="INFO",origin=func_name,message='File %s found - deleting'% j)
os.system('rm %s'%j)
else:
pass
elif os.path.exists(i) == True:
casalog.post(priority="INFO",origin=func_name,message='File %s found - deleting'% i)
os.system('rm %s'%i)
else:
casalog.post(priority="INFO",origin=func_name,message='No file found - %s'% i)
return
def rmdirs(dirs):
func_name = inspect.stack()[0][3]
for i in dirs:
if "*" in i:
files_to_die = glob.glob(i)
casalog.post(priority="INFO",origin=func_name,message='Directories matching with %s - deleting'% i)
for j in files_to_die:
if os.path.exists(j) == True:
casalog.post(priority="INFO",origin=func_name,message='Directory/table %s found - deleting'% j)
os.system('rm -r %s'%j)
else:
pass
elif os.path.exists(i) == True:
casalog.post(priority="INFO",origin=func_name,message='Directory/table %s found - deleting'% i)
os.system('rm -r %s'%i)
else:
casalog.post(priority="INFO",origin=func_name,message='No file found - %s'% i)
return
def init_pipe_run(inputs,params):
inputs2=OrderedDict({})
for a,b in zip(inputs.keys(),inputs.values()):
inputs2[a]=b
for i in inputs2.keys():
inputs2[i] = 0
save_json(filename='%s/vp_steps_run.json'%params['global']['cwd'],array=inputs2,append=False)
gaintables=OrderedDict({})
for a,b in zip(('gaintable','gainfield','spwmap','interp','parang'), ([],[],[],[],params['global']['do_parang'])):
gaintables[a]=b
save_json(filename='%s/vp_gaintables.json'%params['global']['cwd'],array=gaintables,append=False)
save_json(filename='%s/vp_gaintables.last.json'%(params['global']['cwd']), array=OrderedDict({}), append=False)
def find_fitsidi(idifilepath="",cwd="",project_code=""):
func_name = inspect.stack()[0][3]
casalog.post(priority="INFO",origin=func_name,message='Will attempt to find fitsidifiles in %s'%idifilepath)
### Try first with project code and end
fitsidifiles=[]
for i in os.listdir(idifilepath):
if (i.startswith(project_code.lower())|i.startswith(project_code.upper()))&(('IDI' in i)|(i.endswith("idifits"))):
fitsidifiles.append('%s/%s'%(idifilepath,i))
casalog.post(priority="INFO",origin=func_name,message='FOUND - %s'% i)
if fitsidifiles == []:
for i in os.listdir(idifilepath):
if ('IDI' in i)|i.endswith('idifits'):
fitsidifiles.append('%s/%s'%(idifilepath,i))
casalog.post(priority="INFO",origin=func_name,message='FOUND - %s'% i)
if fitsidifiles == []:
casalog.post(priority="SEVERE",origin=func_name,message='Cannot find any fitsidifiles in %s.. exiting'%idifilepath)
sys.exit()
else:
return fitsidifiles
def write_hpc_headers(step,params):
func_name = inspect.stack()[0][3]
hpc_opts = {}
hpc_opts['job_manager'] = params['global']['job_manager']
hpc_opts['job_name'] = 'vp_%s'%step
hpc_opts['email_progress'] = params['global']["email_progress"]
hpc_opts['hpc_account'] = params['global']['HPC_project_code']
hpc_opts['error'] = step
if ((hpc_opts['job_manager'] == 'pbs')|(hpc_opts['job_manager'] == 'bash')|(hpc_opts['job_manager'] == 'slurm')):
pass
else:
casalog.post(priority='SEVERE',origin=func_name, message='Incorrect job manager, please select from pbs, slurm or bash')
sys.exit()
for i in ['partition','walltime','nodetype']:
if params[step]["hpc_options"][i] == 'default':
hpc_opts[i] = params['global']['default_%s'%i]
else:
hpc_opts[i] = params[step]["hpc_options"][i]
for i in ['nodes','cpus','mpiprocs','mem']:
if params[step]["hpc_options"][i] == -1:
hpc_opts[i] = params['global']['default_%s'%i]
else:
hpc_opts[i] = params[step]["hpc_options"][i]
hpc_dict = {'slurm':{
'partition' :'#SBATCH --partition=%s'%hpc_opts['partition'],
'nodetype' :'',
'cpus' :'#SBATCH -n %s'%hpc_opts['cpus'],
'nodes' :'#SBATCH -N %s'%(hpc_opts['nodes']),
'mpiprocs' :'',
'walltime' :'#SBATCH --time=%s'%hpc_opts['walltime'],
'job_name' :'#SBATCH -J %s'%hpc_opts['job_name'],
'hpc_account' :'#SBATCH --account %s'%hpc_opts['hpc_account'],
'mem' :'#SBATCH --mem=%s'%hpc_opts['mem'],
'email_progress':'#SBATCH --mail-type=BEGIN,END,FAIL\n#SBATCH --mail-user=%s'%hpc_opts['email_progress'],
'error':'#SBATCH -o logs/%s.sh.stdout.log\n#SBATCH -e logs/%s.sh.stderr.log'%(hpc_opts['error'],hpc_opts['error'])
},
'pbs':{
'partition' :'#PBS -q %s'%hpc_opts['partition'],
'nodetype' :'',
'cpus' :'#PBS -l select=%s:ncpus=%s:mpiprocs=%s:nodetype=%s'%(hpc_opts['nodes'],hpc_opts['cpus'],hpc_opts['mpiprocs'],hpc_opts['nodetype']),
'nodes' :'',
'mpiprocs' :'',
'mem' :'',
'walltime' :'#PBS -l walltime=%s'%hpc_opts['walltime'],
'job_name' :'#PBS -N %s'%hpc_opts['job_name'],
'hpc_account' :'#PBS -P %s'%hpc_opts['hpc_account'],
'email_progress':'#PBS -m abe -M %s'%hpc_opts['email_progress'],
'error':'#PBS -o logs/%s.sh.stdout.log\n#PBS -e logs/%s.sh.stderr.log'%(hpc_opts['error'],hpc_opts['error'])
},
'bash':{
'partition' :'',
'nodetype' :'',
'cpus' :'',
'nodes' :'',
'mpiprocs' :'',
'walltime' :'',
'job_name' :'',
'hpc_account' :'',
'mem' :'',
'email_progress':'',
'error':''
}
}
hpc_header= ['#!%s'% environ['SHELL']]
if step == 'apply_to_all':
file = open("%s/target_files.txt"%params['global']['cwd'], "r")
nonempty_lines = [line.strip("\n") for line in file if line != "\n"]
line_count = len(nonempty_lines)
file.close()
if params[step]['hpc_options']['max_jobs'] == -1:
tasks = '0-'+str(line_count-1)
else:
if (line_count-1) > params[step]['hpc_options']['max_jobs']:
tasks = '0-'+str(line_count-1)+'%'+str(params[step]['hpc_options']['max_jobs'])
else:
tasks = '0-'+str(line_count-1)
hpc_dict['slurm']['array_job'] = '#SBATCH --array='+tasks
hpc_dict['pbs']['array_job'] = '#PBS -t '+tasks
hpc_dict['bash']['array_job'] = ''
hpc_opts['array_job'] = -1
hpc_job = hpc_opts['job_manager']
for i in hpc_opts.keys():
if i != 'job_manager':
if hpc_opts[i] != '':
if hpc_dict[hpc_opts['job_manager']][i] !='':
hpc_header.append(hpc_dict[hpc_job][i])
with open('job_%s.%s'%(step,hpc_job), 'w') as filehandle:
for listitem in hpc_header:
filehandle.write('%s\n' % listitem)
def write_commands(step,inputs,params,parallel,aoflag,casa6):
func_name = inspect.stack()[0][3]
commands=[]
casapath=params['global']['casapath']
vlbipipepath=params['global']["vlbipipe_path"]
if aoflag==False:
if parallel == True:
mpicasapath = params['global']['mpicasapath']
else:
mpicasapath = ''
if params['global']['singularity'] == True:
singularity='singularity exec'
else:
singularity=''
if (params['global']['job_manager'] == 'pbs'):
job_commands=''
commands.append('cd %s'%params['global']['cwd'])
if (parallel == True):
job_commands='--map-by node -hostfile $PBS_NODEFILE'
else:
job_commands=''
if casa6 == False:
commands.append('%s %s %s %s --nologger --log2term -c %s/run_%s.py'%(mpicasapath,job_commands,singularity,casapath,vlbipipepath,step))
else:
commands.append('%s %s %s %s %s/run_%s.py'%(mpicasapath,job_commands,singularity,casapath,vlbipipepath,step))
elif aoflag=='both':
strategies = params[step]['AO_flag_strategy']
fields=params[step]['AO_flag_fields']
if os.path.exists('%s/%s_msinfo.json'%(params['global']['cwd'],params['global']['project_code']))==False:
msinfo = get_ms_info(msfile)
save_json(filename='%s/%s_msinfo.json'%(params['global']['cwd'],params['global']['project_code']), array=get_ms_info('%s/%s.ms'%(params['global']['cwd'],params['global']['project_code'])), append=False)
else:
msinfo = load_json('%s/%s_msinfo.json'%(params['global']['cwd'],params['global']['project_code']))
for i in range(len(fields)):
if (params['global']['job_manager'] == 'pbs'):
commands.append('cd %s'%params['global']['cwd'])
for k in params['global']['AOflag_command']:
commands.append(k)
msfile='%s.ms'%params['global']['project_code']
ids = []
for j in fields[i]:
ids.append(str(msinfo['FIELD']['fieldtoID'][j]))
commands[-1] = commands[-1]+' -fields %s '%(",".join(ids))
commands[-1] = commands[-1]+'-strategy %s %s'%(params[step]['AO_flag_strategy'][i],msfile)
msfile='%s.ms'%params['global']['project_code']
if parallel == True:
mpicasapath = params['global']['mpicasapath']
else:
mpicasapath = ''
if params['global']['singularity'] == True:
singularity='singularity exec'
else:
singularity=''
if (params['global']['job_manager'] == 'pbs'):
job_commands=''
commands.append('cd %s'%params['global']['cwd'])
if (parallel == True):
job_commands='--map-by node -hostfile $PBS_NODEFILE'
else:
job_commands=''
if casa6 == False:
commands.append('%s %s %s %s --nologger --log2term -c %s/run_%s.py'%(mpicasapath,job_commands,singularity,casapath,vlbipipepath,step))
else:
commands.append('%s %s %s %s %s/run_%s.py'%(mpicasapath,job_commands,singularity,casapath,vlbipipepath,step))
elif aoflag==True:
strategies = params[step]['AO_flag_strategy']
fields=params[step]['AO_flag_fields']
if os.path.exists('%s/%s_msinfo.json'%(params['global']['cwd'],params['global']['project_code']))==False:
msinfo = get_ms_info(msfile)
save_json(filename='%s/%s_msinfo.json'%(params['global']['cwd'],params['global']['project_code']), array=get_ms_info('%s/%s.ms'%(params['global']['cwd'],params['global']['project_code'])), append=False)
else:
msinfo = load_json('%s/%s_msinfo.json'%(params['global']['cwd'],params['global']['project_code']))
for i in range(len(fields)):
if (params['global']['job_manager'] == 'pbs'):
commands.append('cd %s'%params['global']['cwd'])
for k in params['global']['AOflag_command']:
commands.append(k)
msfile='%s.ms'%params['global']['project_code']
ids = []
for j in fields[i]:
ids.append(str(msinfo['FIELD']['fieldtoID'][j]))
commands[-1] = commands[-1]+' -fields %s '%(",".join(ids))
commands[-1] = commands[-1]+'-strategy %s %s'%(params[step]['AO_flag_strategy'][i],msfile)
elif aoflag=='apply_to_all':
if (params['global']['job_manager'] == 'pbs'):
commands.append('cd %s'%params['global']['cwd'])
variable='${array[$a]}'
elif (params['global']['job_manager'] == 'slurm'):
commands.append('a=$SLURM_ARRAY_TASK_ID')
variable='${array[$a]}'
commands.append('readarray array < %s/target_files.txt'%params['global']['cwd'])
if (params['global']['job_manager'] == 'bash'):
variable=''
if parallel == True:
mpicasapath = params['global']['mpicasapath']
else:
mpicasapath = ''
if params['global']['singularity'] == True:
singularity='singularity exec'
else:
singularity=''
if (params['global']['job_manager'] == 'pbs'):
job_commands=''
commands.append('cd %s'%params['global']['cwd'])
if (parallel == True):
job_commands='--map-by node -hostfile $PBS_NODEFILE'
else:
job_commands=''
if casa6 == False:
commands.append('%s %s %s %s --nologger --log2term -c %s/run_%s.py 0 %s'%(mpicasapath,job_commands,singularity,casapath,vlbipipepath,step,variable))
else:
commands.append('%s %s %s %s %s/run_%s.py 0 %s'%(mpicasapath,job_commands,singularity,casapath,vlbipipepath,step,variable))
if params["init_flag"]["run_AOflag"] == True:
if (params['global']['job_manager'] == 'bash'):
commands.append('for a in \"${array[@]}\"')
commands.append('do')
variable="$a"
commands.append("IFS=' ' read -r -a arrays <<< \"%s\""%variable)
for i in params['global']['AOflag_command']:
commands.append(i)
tar_idx = find_nestlist(params['init_flag']['AO_flag_fields'], params['global']['targets'][0])[0]
commands[-1] = commands[-1]+' -strategy %s ${arrays[1]}_presplit.ms'%(params['init_flag']['AO_flag_strategy'][tar_idx])
if (params['global']['job_manager'] == 'bash'):
commands.append('done')
variable=""
if casa6 == False:
commands.append('%s %s %s %s --nologger --log2term -c %s/run_%s.py 1 %s'%(mpicasapath,job_commands,singularity,casapath,vlbipipepath,step,variable))
else:
commands.append('%s %s %s %s %s/run_%s.py 1 %s'%(mpicasapath,job_commands,singularity,casapath,vlbipipepath,step,variable))
else:
casalog.post(priority='SEVERE',origin=func_name,message='Error with writing commands.')
sys.exit()
commands.append('mv "casa"*"log" "logs"')
job_m = params['global']['job_manager']
with open('job_%s.%s'%(step,job_m), 'a') as filehandle:
for listitem in commands:
filehandle.write('%s\n' % listitem)
def find_nestlist(mylist, char):
for sub_list in mylist:
if char in sub_list:
return (mylist.index(sub_list), sub_list.index(char))
raise ValueError("'{char}' is not in list".format(char = char))
def write_job_script(steps,job_manager):
commands=['#!%s'%environ['SHELL'], 'set -e']
for i,j in enumerate(steps):
if i==0:
depend=''
else:
if job_manager=='pbs':
depend='-W depend=afterany:$%s'%(steps[i-1])
if job_manager=='slurm':
depend='--dependency=afterany:$%s'%(steps[i-1])
if job_manager=='bash':
depend=''
if job_manager=='pbs':
commands.append("%s=$(qsub %s job_%s.pbs)"%(j,depend,j))
if job_manager=='slurm':
commands.append('%s=$(sbatch --parsable %s job_%s.slurm)'%(j,depend,j))
if job_manager=='bash':
commands.append('%s job_%s.bash'%(environ['SHELL'],j))
with open('vp_runfile.bash','w') as f:
for listitem in commands:
f.write('%s\n' % listitem)
f.close()
def get_ms_info(msfile):
tb = casatools.table()
ms = casatools.ms()
msinfo={}
## antenna information
tb.open('%s/ANTENNA'%msfile)
ants = tb.getcol('NAME')
ant={}
ant['anttoID'] =dict(zip(ants, np.arange(0,len(ants),1)))
ant['IDtoant'] = dict(zip(np.arange(0,len(ants),1).astype(str),ants))
msinfo['ANTENNAS']=ant
tb.close()
## get spw information
tb.open('%s/SPECTRAL_WINDOW'%msfile)
spw={}
spw['nspws'] = len(tb.getcol('TOTAL_BANDWIDTH'))
spw['bwidth'] = np.sum(tb.getcol('TOTAL_BANDWIDTH'))
spw['spw_bw'] = spw['bwidth']/spw['nspws']
spw['freq_range'] = [tb.getcol('CHAN_FREQ')[0][0],tb.getcol('CHAN_FREQ')[0][0]+spw['bwidth']]
spw['cfreq'] = np.average(spw['freq_range'])
if ((np.max(tb.getcol('CHAN_WIDTH')) == np.min(tb.getcol('CHAN_WIDTH')))&(np.max(tb.getcol('NUM_CHAN')) == np.min(tb.getcol('NUM_CHAN')))) == True:
spw['same_spws'] = True
spw['nchan'] = np.max(tb.getcol('NUM_CHAN'))
else:
spw['same_spws'] = False
spw['nchan'] = tb.getcol('NUM_CHAN')
if spw['same_spws'] == True:
spw['chan_width'] = tb.getcol('CHAN_WIDTH')[0][0]
else:
spw['chan_width'] = np.average(tb.getcol('CHAN_WIDTH'))
tb.close()
tb.open('%s/POLARIZATION'%msfile)
spw['npol'] = tb.getcol('NUM_CORR')[0]
polariz = tb.getcol('CORR_TYPE').flatten()
ID_to_pol={'0': 'Undefined',
'1': 'I',
'2': 'Q',
'3': 'U',
'4': 'V',
'5': 'RR',
'6': 'RL',
'7': 'LR',
'8': 'LL',
'9': 'XX',
'10': 'XY',
'11': 'YX',
'12': 'YY',
'13': 'RX',
'14': 'RY',
'15': 'LX',
'16': 'LY',
'17': 'XR',
'18': 'XL',
'19': 'YR',
'20': 'YL',
'21': 'PP',
'22': 'PQ',
'23': 'QP',
'24': 'QQ',
'25': 'RCircular',
'26': 'LCircular',
'27': 'Linear',
'28': 'Ptotal',
'29': 'Plinear',
'30': 'PFtotal',
'31': 'PFlinear',
'32': 'Pangle'}
pol2=[]
for i,j in enumerate(polariz):
pol2.append(ID_to_pol[str(j)])
spw['spw_pols'] = pol2
tb.close()
msinfo['SPECTRAL_WINDOW'] = spw
## Get field information
tb.open('%s/FIELD'%msfile)
fields = tb.getcol('NAME')
field = {}
field['fieldtoID'] =dict(zip(fields, np.arange(0,len(fields),1)))
field['IDtofield'] = dict(zip(np.arange(0,len(fields),1).astype(str),fields))
tb.close()
## scans
ms.open(msfile)
scans = ms.getscansummary()
ms.close()
scan = {}
for i in list(scans.keys()):
fieldid = scans[i]['0']['FieldId']
if fieldid not in list(scan.keys()):
scan[fieldid] = [i]
else:
vals = scan[fieldid]
scan[fieldid].append(i)
msinfo['SCANS'] = scan
## Get telescope_name
tb.open('%s/OBSERVATION'%msfile)
msinfo['TELE_NAME'] = tb.getcol('TELESCOPE_NAME')[0]
tb.close()
image_params = {}
high_freq = spw['freq_range'][1]
ms.open(msfile)
f = []
indx = []
for i in field['fieldtoID'].keys():
ms.selecttaql('FIELD_ID==%s'%field['fieldtoID'][i])
try:
max_uv = ms.getdata('uvdist')['uvdist'].max()
image_params[i] = ((speed_light/high_freq)/max_uv)*(180./np.pi)*(3.6e6/5.)
f.append(i)
indx.append(field['fieldtoID'][i])
except:
pass
ms.reset()
ms.close()
field = {}
field['fieldtoID'] =dict(zip(f, indx))
field['IDtofield'] =dict(zip(np.array(indx).astype(str),f))
msinfo['FIELD'] = field
msinfo["IMAGE_PARAMS"] = image_params
return msinfo
def fill_flagged_soln(caltable='', fringecal=False):
"""
This is to replace the gaincal solution of flagged/failed solutions by the nearest valid
one.
If you do not do that and applycal blindly with the table your data gets
flagged between calibration runs that have a bad/flagged solution at one edge.
Can be pretty bad when you calibrate every hour or more
(when you are betting on self-cal) of observation (e.g L-band of the EVLA)..one can
lose the whole hour of good data without realizing !
"""
if fringecal==False:
gaincol='CPARAM'
else:
gaincol='FPARAM'
tb = casatools.table()
tb.open(caltable, nomodify=False)
flg=tb.getcol('FLAG')
#sol=tb.getcol('SOLUTION_OK')
ant=tb.getcol('ANTENNA1')
gain=tb.getcol(gaincol)
t=tb.getcol('TIME')
dd=tb.getcol('SPECTRAL_WINDOW_ID')
#dd=tb.getcol('CAL_DESC_ID')
maxant=np.max(ant)
maxdd=np.max(dd)
npol=len(gain[:,0,0])
nchan=len(gain[0,:,0])
k=1
numflag=0.0
for k in range(maxant+1):
for j in range (maxdd+1):
subflg=flg[:,:,(ant==k) & (dd==j)]
subt=t[(ant==k) & (dd==j)]
#subsol=sol[:,:,(ant==k) & (dd==j)]
subgain=gain[:,:,(ant==k) & (dd==j)]
for kk in range(1, len(subt)):
for chan in range(nchan):
for pol in range(npol):
if(subflg[pol,chan,kk] and not subflg[pol,chan,kk-1]):
numflag += 1.0
subflg[pol,chan,kk]=False
#subsol[pol, chan, kk]=True
subgain[pol,chan,kk]=subgain[pol,chan,kk-1]
if(subflg[pol,chan,kk-1] and not subflg[pol,chan,kk]):
numflag += 1.0
subflg[pol,chan,kk-1]=False
#subsol[pol, chan, kk-1]=True
subgain[pol,chan,kk-1]=subgain[pol,chan,kk]
flg[:,:,(ant==k) & (dd==j)]=subflg
#sol[:,:,(ant==k) & (dd==j)]=subsol
gain[:,:,(ant==k) & (dd==j)]=subgain
###
tb.putcol('FLAG', flg)
#tb.putcol('SOLUTION_OK', sol)
tb.putcol(gaincol, gain)
tb.done()
def fill_flagged_soln2(caltable='', fringecal=False):
"""
This is to replace the gaincal solution of flagged/failed solutions by the nearest valid
one.
If you do not do that and applycal blindly with the table your data gets
flagged between calibration runs that have a bad/flagged solution at one edge.
Can be pretty bad when you calibrate every hour or more
(when you are betting on self-cal) of observation (e.g L-band of the EVLA)..one can
lose the whole hour of good data without realizing !
"""
if fringecal==False:
gaincol='CPARAM'
else:
gaincol='FPARAM'
tb=casatools.table()
tb.open(caltable, nomodify=False)
flg=tb.getcol('FLAG')
#sol=tb.getcol('SOLUTION_OK')
ant=tb.getcol('ANTENNA1')
gain=tb.getcol(gaincol)
t=tb.getcol('TIME')
dd=tb.getcol('SPECTRAL_WINDOW_ID')
#dd=tb.getcol('CAL_DESC_ID')
maxant=np.max(ant)
maxdd=np.max(dd)
npol=len(gain[:,0,0])
nchan=len(gain[0,:,0])
k=1
numflag=0.0
for k in range(maxant+1):
for j in range (maxdd+1):
subflg=flg[:,:,(ant==k) & (dd==j)]
subt=t[(ant==k) & (dd==j)]
#subsol=sol[:,:,(ant==k) & (dd==j)]
subgain=gain[:,:,(ant==k) & (dd==j)]
#print 'subgain', subgain.shape
for kk in range(1, len(subt)):
for chan in range(nchan):
for pol in range(npol):
if(subflg[pol,chan,kk] and not subflg[pol,chan,kk-1]):
numflag += 1.0
subflg[pol,chan,kk]=False
#subsol[pol, chan, kk]=True
subgain[pol,chan,kk]=subgain[pol,chan,kk-1]
if(subflg[pol,chan,kk-1] and not subflg[pol,chan,kk]):
numflag += 1.0
subflg[pol,chan,kk-1]=False
#subsol[pol, chan, kk-1]=True
subgain[pol,chan,kk-1]=subgain[pol,chan,kk]
for kk in range(len(subt)-2,-1, -1):
for chan in range(nchan):
for pol in range(npol):
if(subflg[pol,chan,kk] and not subflg[pol,chan,kk+1]):
numflag += 1.0
subflg[pol,chan,kk]=False
#subsol[pol, chan, kk]=True
subgain[pol,chan,kk]=subgain[pol,chan,kk+1]
if(subflg[pol,chan,kk] and not subflg[pol,chan,kk]):
numflag += 1.0
subflg[pol,chan,kk+1]=False
#subsol[pol, chan, kk-1]=True
subgain[pol,chan,kk+1]=subgain[pol,chan,kk]
flg[:,:,(ant==k) & (dd==j)]=subflg
#sol[:,:,(ant==k) & (dd==j)]=subsol
gain[:,:,(ant==k) & (dd==j)]=subgain
###
tb.putcol('FLAG', flg)
#tb.putcol('SOLUTION_OK', sol)
tb.putcol(gaincol, gain)
tb.done()
def filter_tsys_auto(caltable,nsig=[2.5,2.],jump_pc=20):
func_name = inspect.stack()[0][3]
tb=casatools.table()
tb.open(caltable, nomodify=False)
flg=tb.getcol('FLAG')
#sol=tb.getcol('SOLUTION_OK')
gaincol='FPARAM'
ant=tb.getcol('ANTENNA1')
gain=tb.getcol(gaincol)
gain_edit = copy.deepcopy(gain)*0
t=tb.getcol('TIME')
dd=tb.getcol('SPECTRAL_WINDOW_ID')
npol=gain.shape[0]
casalog.post(priority="INFO",origin=func_name,message='Editing and smoothing the tsys table')
for k in range(npol):
for i in np.unique(ant):
for j in np.unique(dd):
flg_temp=flg[k,0,((ant==i)&(dd==j))]
gain_uflg2=gain[k,0,((ant==i)&(dd==j))]
gain_uflg = gain_uflg2[flg_temp==0]
if len(gain_uflg) != 0:
t_temp=t[((ant==i)&(dd==j))][flg_temp==0]
gain_uflg,detected_outliers = hampel_filter(np.array([t_temp,gain_uflg]), 41 ,n_sigmas=nsig[0])
gain_uflg,detected_outliers = hampel_filter(np.array([t_temp,gain_uflg]), 10 ,n_sigmas=nsig[1])
#gain_uflg,detected_outliers = hampel_filter(np.array([t_temp,gain_uflg]), 5 ,n_sigmas=2.5)
gain_uflg, jump = detect_jump_and_smooth(gain_uflg,jump_pc=jump_pc)
if jump == False:
gain_uflg = smooth_series(gain_uflg, 21)
gain_uflg2[flg_temp==0] = gain_uflg
ind = np.where(np.isnan(gain_uflg2[flg_temp==0]))[0]
flg_temp2 = flg_temp[flg_temp==0]
flg_temp2[ind] = 1
flg_temp[flg_temp==0] = flg_temp2
flg[k,0,((ant==i)&(dd==j))] = flg_temp
gain_edit[k,0,((ant==i)&(dd==j))]= gain_uflg2
tb.putcol('FPARAM',gain_edit)
tb.putcol('FLAG',flg)
tb.close()
def smooth_series(y, box_pts):
box = np.ones(box_pts)/box_pts
y_smooth = np.convolve(y, box, mode='valid')
y_smooth = np.hstack([np.ones(np.floor(box_pts/2.).astype(int))*y_smooth[0],y_smooth])
y_smooth = np.hstack([y_smooth,np.ones(np.floor(box_pts/2.).astype(int))*y_smooth[-1]])
return y_smooth
def hampel_filter(input_series, window_size, n_sigmas=3):
n = len(input_series[1])
new_series = input_series.copy()
k = 1.4826 # scale factor for Gaussian distribution
indices = []
# possibly use np.nanmedian
for i in range((window_size),(n - window_size)):
x0 = np.median(input_series[1][(i - window_size):(i + window_size)])
S0 = k * np.median(np.abs(input_series[1][(i - window_size):(i + window_size)] - x0))
if i == window_size:
for j in range(0,window_size):
if (np.abs(input_series[1][j] - x0) > n_sigmas * S0):
new_series[1][j] = x0
indices.append(j)
elif i == ((n - window_size)-1):
for j in range(n-window_size,n):
if (np.abs(input_series[1][j] - x0) > n_sigmas * S0):
new_series[1][j] = x0
indices.append(j)
else:
if (np.abs(input_series[1][i] - x0) > n_sigmas * S0):
new_series[1][i] = x0
indices.append(i)
detected_outliers = np.array(indices)
already_tagged=[]
for i in range(len(input_series[1])):
if ((i<input_series[0].shape[0]-1)&(i>0)):
if i in detected_outliers:
if i not in already_tagged:
low=i-1
t_low=input_series[0][low]
while i+1 in detected_outliers:
i+=1
already_tagged.append(i)
if i < (input_series[0].shape[0]-1):
high=i+1
t_high=input_series[0][high]
f = interp1d(x=[t_low,t_high], y=[input_series[1][low],input_series[1][high]])
input_series[1][low:high]= f(input_series[0][np.arange(low,high,1)])
if 0 in detected_outliers:
input_series[1][0] = input_series[1][1]
if (input_series[0].shape[0]-1) in detected_outliers:
i = (input_series[0].shape[0]-1)
while i-1 in detected_outliers:
i-=1
input_series[1][i:input_series[0].shape[0]] = input_series[1][input_series[0].shape[0]-(i-2)]
return input_series[1],detected_outliers
def detect_jump_and_smooth(array,jump_pc):
jump_pc=jump_pc/100.
try:
for i,j in enumerate(array):
if i<array.shape[0]-2:
if (array[i+1] > 1.1*array[i])|(array[i+1]<0.9*array[i]):
jump=True
low=i
if i < len(array)-1:
i+=1
while ((array[i+1] > (1+jump_pc)*array[i])==False)&((array[i+1]<(1-jump_pc)*array[i])==False):
if i > (len(array)-3):
break
else:
i+=1
high=i+1
diff=int((high-low)/2.)
if diff%2 == 0:
diff=diff+1
array[low:high] = smooth_series(array[low:high],diff)
else:
jump=False
else:
jump=False
return array, jump
except:
jump=False
return array, jump
def append_gaintable(gaintables,caltable_params):
for i,j in enumerate(gaintables.keys()):
if j != 'parang':
gaintables[j].append(caltable_params[i])
return gaintables
def load_gaintables(params,casa6):
cwd=params['global']['cwd']
if os.path.exists('%s/vp_gaintables.json'%(cwd)) == False:
gaintables=OrderedDict({})
for a,b in zip(('gaintable','gainfield','spwmap','interp','parang'), ([],[],[],[],params['global']['do_parang'])):
gaintables[a]=b
else:
gaintables=load_json('%s/vp_gaintables.json'%(cwd),Odict=True,casa6=casa6)
return gaintables
def find_refants(pref_ant,msinfo):
antennas = msinfo['ANTENNAS']['anttoID'].keys()
refant=[]
for i in pref_ant:
if i in antennas:
refant.append(i)
return ",".join(refant)
def calc_edge_channels(value,nspw,nchan):
func_name = inspect.stack()[0][3]
if (type(value) == str):
if value.endswith('%'):
value=np.round((float(value.split('%')[0])/100.)*float(nchan),0).astype(int)
else:
casalog.post(priority='SEVERE',origin=func_name,message='Edge channels either needs to be an integer (number of channels) or a string with a percentage i.e. 5%')
sys.exit()
elif (type(value)==int):
value=value
else:
casalog.post(priority='SEVERE',origin=func_name,message='Edge channels either needs to be an integer (number of channels) or a string with a percentage i.e. 5%')
sys.exit()
flag_chans=[]
for i in range(nspw):
flag_chans.append('%d:0~%d;%d~%d'%(i,value-1,(nchan-1)-(value-1),(nchan-1)))
return ",".join(flag_chans)
def time_convert(mytime, myunit='s'):
qa = casatools.quanta()
if type(mytime) != list:
mytime=[mytime]
myTimestr = []
for i,time in enumerate(mytime):
q1=qa.quantity(time,myunit)
time1=qa.time(q1,form='ymd')[0]
z=0
if i!=0:
if split_str(time1,'/',3)[0] == split_str(myTimestr[z],'/',3)[0]:
time1 = split_str(time1,'/',3)[1]
else:
z=i
myTimestr.append(time1)
return myTimestr
def split_str(strng, sep, pos):
strng = strng.split(sep)
return sep.join(strng[:pos]), sep.join(strng[pos:])
def clip_bad_solutions(fid, table_array, caltable, solint, passmark):
TOL=solint/2.01
tb = casatools.table()
tb.open(caltable)
ant = tb.getcol('ANTENNA1')
value = tb.getcol('FPARAM')
flag = tb.getcol('FLAG')
time_a = tb.getcol('TIME')
time = np.unique(tb.getcol('TIME'))
time = np.unique(np.floor(time/TOL).astype(int))*TOL
field_id = tb.getcol('FIELD_ID')
solns = np.sum(table_array[0],axis=0)
for z in fid.keys():
maxsoln = np.max(solns[fid[z][0]:fid[z][1]])
for j in range(fid[z][0],fid[z][1]+1):
result = np.isclose(time_a, time[j], atol=TOL,rtol=1e-10)
if solns[j] < passmark*maxsoln:
flag[:,0,(result)] = True
tb.open(caltable, nomodify=False)
tb.putcol('FLAG',np.multiply(flag, 1).astype(int))
tb.close()