def get_mean_powers(r, c):
'''
Get autocorrelation powers from redis
and scale them appropriately.
'''
# find indices of correlation matrices representing autos
corr_conf = c['Configuration']['correlator']['hardcoded']
bl_order = sim.get_bl_order(corr_conf['n_ants'])
n_bls = len(bl_order)
auto_indices = np.zeros(corr_conf['n_ants'])
for bn, bl in enumerate(bl_order):
if bl[0] == bl[1]:
auto_indices[bl[0]] = bn
# get current data and store as numpy array
data, ts = r.hmget('RECEIVER:xeng_raw0', ['val', 'timestamp'])
n_chans = c['FEngine']['n_chans']
ts = float(ts)
data_c = np.fromstring(data, dtype=np.int32).reshape(
[corr_conf['n_bands'] * n_chans, n_bls, 1, 2])
# carve out the autos
autos = np.zeros([corr_conf['n_ants'], corr_conf['n_bands'] * n_chans],
dtype=np.float32)
for an, auto_index in enumerate(auto_indices):
autos[an] = data_c[:, auto_index, 0, 1] / c['XEngine'][
'acc_len'] / corr_conf[
'window_len'] #This is scaled relative to a correlator input between +/-7
return autos
def parse_config_file(self):
"""
Parse the instance's config file, saving some parameters as attributes
for easy access.
This method is automatically called on object instantiation.
"""
#some common params
self.n_ants = self.config['Configuration']['correlator']['hardcoded']['n_ants']
self.n_bands = self.config['Configuration']['correlator']['hardcoded']['n_bands']
self.n_inputs= self.config['Configuration']['correlator']['hardcoded']['inputs_per_board']
self.n_chans = self.config['Configuration']['correlator']['hardcoded']['n_chans']
self.n_pols = self.config['Configuration']['correlator']['hardcoded']['n_pols']
self.output_format = self.config['Configuration']['correlator']['hardcoded']['output_format']
self.data_path = self.config['Configuration']['correlator']['runtime']['data_path']
self.adc_clk = float(self.config['FEngine']['adc_clk'] * 1e6)
self.lo_freq = self.config['FEngine']['mix_freq']
self.f_n_chans = self.config['FEngine']['n_chans']
self.x_acc_len = self.config['XEngine']['acc_len']
self.n_bls = (self.n_ants * (self.n_ants + 1))/2
self.bl_order = sim.get_bl_order(self.n_ants)
self.roaches = set([node['host'] for node in self.config['FEngine']['nodes']+self.config['XEngine']['nodes']])
#shortcuts to sections
self.c_testing = self.config['Configuration']['correlator']['runtime']['testing']
self.c_correlator = self.config['Configuration']['correlator']['runtime']
self.c_correlator_hard = self.config['Configuration']['correlator']['hardcoded']
self.c_global = self.config['Configuration']
self.c_antennas = self.config['Antennas']
self.c_array = self.config['Array']
# other useful vars
self.window_len = self.c_correlator_hard['window_len']
self.sync_clocks= self.c_correlator_hard['sync_period']*16 #measured in ADC clocks
self.sync_period = self.sync_clocks / self.adc_clk
self.acc_samples = self.x_acc_len * self.c_correlator_hard['window_len'] * self.f_n_chans * 2
self.acc_time = self.acc_samples / self.adc_clk
# some debugging / info
self._logger.info("ROACHes are %r"%self.roaches)
# ant array shortcuts
self.array_lon = self.c_array['lon']
self.array_lat = self.c_array['lat']
self.ant_locs = [[0., 0., 0.] for a in self.c_antennas]
for ant in self.c_antennas:
self.ant_locs[ant['ant']] = ant['loc'] #this way the ants don't have to be in order in the config file
self.array = antenna_functions.AntArray((self.array_lat, self.array_lon), self.ant_locs)
def get_expected_output(n_ants=16):
'''
Generate the correlation matrix based on the
test vectors specified in firmware.
'''
bl_order = sim.get_bl_order(n_ants) # Get a list of antenna pairs
n_bls = len(bl_order)
oput = np.zeros([4096, n_bls, 4],
dtype=np.complex128) # n_chans * n_baselines * n_stokes
iput = np.zeros(
[n_ants, 2, 4096],
dtype=np.complex128) # n_antennas * n_polarizations * n_chans
# First generate the inputs
# y pol first.
counter = np.arange(4096)
im = counter & 0xf
re = (counter >> 4) & 0xf
# convert to 2's complement binary
re[re > 7] -= 16
im[im > 7] -= 16
# vectors are the same for all antennas
for ant in range(n_ants):
iput[ant, 1, :] = re + 1j * im
# xpol
im = (counter >> 8) & 0xf
# convert to 2's complement binary
im[im > 7] -= 16
# xpol -- odd numbered antennas (i.e., second input of each ROACH) have 1 in real part
for ant in range(n_ants):
if ant % 2 == 0:
iput[ant, 0, :] = 0 + 1j * im
elif ant % 2 == 1:
iput[ant, 0, :] = 1 + 1j * im
# compute correlation matrix
for bn, bl in enumerate(bl_order):
oput[:, bn, 0] = iput[bl[0], 0] * np.conj(iput[bl[1], 0]) #XX
oput[:, bn, 1] = iput[bl[0], 1] * np.conj(iput[bl[1], 1]) #YY
oput[:, bn, 2] = iput[bl[0], 0] * np.conj(iput[bl[1], 1]) #XY
oput[:, bn, 3] = iput[bl[0], 1] * np.conj(iput[bl[1], 0]) #YX
return oput
def get_expected_output(n_ants=16):
'''
Generate the correlation matrix based on the
test vectors specified in firmware.
'''
bl_order = sim.get_bl_order(n_ants) # Get a list of antenna pairs
n_bls = len(bl_order)
oput = np.zeros([4096, n_bls, 4], dtype=np.complex128) # n_chans * n_baselines * n_stokes
iput = np.zeros([n_ants, 2, 4096], dtype=np.complex128) # n_antennas * n_polarizations * n_chans
# First generate the inputs
# y pol first.
counter = np.arange(4096)
im = counter&0xf
re = (counter>>4)&0xf
# convert to 2's complement binary
re[re>7] -= 16
im[im>7] -= 16
# vectors are the same for all antennas
for ant in range(n_ants):
iput[ant, 1, :] = re + 1j*im
# xpol
im = (counter>>8) & 0xf
# convert to 2's complement binary
im[im>7] -= 16
# xpol -- odd numbered antennas (i.e., second input of each ROACH) have 1 in real part
for ant in range(n_ants):
if ant % 2 == 0:
iput[ant, 0, :] = 0 + 1j*im
elif ant % 2 == 1:
iput[ant, 0, :] = 1 + 1j*im
# compute correlation matrix
for bn,bl in enumerate(bl_order):
oput[:, bn, 0] = iput[bl[0],0] * np.conj(iput[bl[1],0]) #XX
oput[:, bn, 1] = iput[bl[0],1] * np.conj(iput[bl[1],1]) #YY
oput[:, bn, 2] = iput[bl[0],0] * np.conj(iput[bl[1],1]) #XY
oput[:, bn, 3] = iput[bl[0],1] * np.conj(iput[bl[1],0]) #YX
return oput
def get_mean_powers(r, c):
'''
Get autocorrelation powers from redis
and scale them appropriately.
'''
# find indices of correlation matrices representing autos
corr_conf = c['Configuration']['correlator']['hardcoded']
bl_order = sim.get_bl_order(corr_conf['n_ants'])
n_bls = len(bl_order)
auto_indices = np.zeros(corr_conf['n_ants'])
for bn, bl in enumerate(bl_order):
if bl[0] == bl[1]:
auto_indices[bl[0]] = bn
# get current data and store as numpy array
data, ts = r.hmget('RECEIVER:xeng_raw0', ['val', 'timestamp'])
n_chans = c['FEngine']['n_chans']
ts = float(ts)
data_c = np.fromstring(data, dtype=np.int32).reshape([corr_conf['n_bands'] * n_chans, n_bls, 1, 2])
# carve out the autos
autos = np.zeros([corr_conf['n_ants'], corr_conf['n_bands'] * n_chans], dtype=np.float32)
for an, auto_index in enumerate(auto_indices):
autos[an] = data_c[:, auto_index, 0, 1] / c['XEngine']['acc_len'] / corr_conf['window_len'] #This is scaled relative to a correlator input between +/-7
return autos
def parse_config_file(self):
"""
Parse the instance's config file, saving some parameters as attributes
for easy access.
This method is automatically called on object instantiation.
"""
#some common params
self.n_ants = self.config['Configuration']['correlator']['hardcoded'][
'n_ants']
self.n_bands = self.config['Configuration']['correlator']['hardcoded'][
'n_bands']
self.n_inputs = self.config['Configuration']['correlator'][
'hardcoded']['inputs_per_board']
self.n_chans = self.config['Configuration']['correlator']['hardcoded'][
'n_chans']
self.n_pols = self.config['Configuration']['correlator']['hardcoded'][
'n_pols']
self.output_format = self.config['Configuration']['correlator'][
'hardcoded']['output_format']
self.data_path = self.config['Configuration']['correlator']['runtime'][
'data_path']
self.adc_clk = float(self.config['FEngine']['adc_clk'] * 1e6)
self.lo_freq = self.config['FEngine']['mix_freq']
self.f_n_chans = self.config['FEngine']['n_chans']
self.x_acc_len = self.config['XEngine']['acc_len']
self.n_bls = (self.n_ants * (self.n_ants + 1)) / 2
self.bl_order = sim.get_bl_order(self.n_ants)
self.roaches = set([
node['host'] for node in self.config['FEngine']['nodes'] +
self.config['XEngine']['nodes']
])
#shortcuts to sections
self.c_testing = self.config['Configuration']['correlator']['runtime'][
'testing']
self.c_correlator = self.config['Configuration']['correlator'][
'runtime']
self.c_correlator_hard = self.config['Configuration']['correlator'][
'hardcoded']
self.c_global = self.config['Configuration']
self.c_antennas = self.config['Antennas']
self.c_array = self.config['Array']
# other useful vars
self.window_len = self.c_correlator_hard['window_len']
self.sync_clocks = self.c_correlator_hard[
'sync_period'] * 16 #measured in ADC clocks
self.sync_period = self.sync_clocks / self.adc_clk
self.acc_samples = self.x_acc_len * self.c_correlator_hard[
'window_len'] * self.f_n_chans * 2
self.acc_time = self.acc_samples / self.adc_clk
# some debugging / info
self._logger.info("ROACHes are %r" % self.roaches)
# ant array shortcuts
self.array_lon = self.c_array['lon']
self.array_lat = self.c_array['lat']
self.ant_locs = [[0., 0., 0.] for a in self.c_antennas]
for ant in self.c_antennas:
self.ant_locs[ant['ant']] = ant[
'loc'] #this way the ants don't have to be in order in the config file
self.array = antenna_functions.AntArray(
(self.array_lat, self.array_lon), self.ant_locs)
"-p",
"--npackets",
dest="npackets",
type="int",
default=100000,
help="Maximum number of packets to read (per pcap files). Default:100000")
(opts, args) = parser.parse_args()
if opts.reorder:
#rel_order[x] = y -> the channel labelled x really y
# i.e. real_channel = rel_order[channel_label]
rel_order = get_rel_order()
else:
rel_order = range(4096)
bl_order = sim.get_bl_order(16)
limit = opts.npackets
hdr_len = 42 + 88
# we only capture 1000 bytes per packet --
# header is 42+88 bytes => 870 bytes data
# => 108 complex values
# => 27 dual-pol baselines (+ some leftover)
n_bls = 27
#n_bls = 16*17/2 * 4
n_windows = 100
packet_cnts = np.zeros(n_windows)
time_slots = np.ones(n_windows) * -1
spectra = np.ones([n_windows, 4096, n_bls * 4], dtype=complex) * 2**31
from optparse import OptionParser
parser = OptionParser(usage='%prog [options] pcap_file1 pcap_file2 ...')
parser.add_option("-r", "--reorder", dest="reorder", action="store_true", default=False,
help="Use this flag to reorder frequency channels based on a known firmware issue. This issue was fixed in firmware version eovsa_corr_2016_Feb_20_1600.bof. This option should be unnecessary for pcap files generated with this, or later, firmware.")
parser.add_option("-p", "--npackets", dest="npackets", type="int", default=100000,
help="Maximum number of packets to read (per pcap files). Default:100000")
(opts, args) = parser.parse_args()
if opts.reorder:
#rel_order[x] = y -> the channel labelled x really y
# i.e. real_channel = rel_order[channel_label]
rel_order = get_rel_order()
else:
rel_order = range(4096)
bl_order = sim.get_bl_order(16)
limit = opts.npackets
hdr_len = 42+88
# we only capture 1000 bytes per packet --
# header is 42+88 bytes => 870 bytes data
# => 108 complex values
# => 27 dual-pol baselines (+ some leftover)
n_bls = 27
#n_bls = 16*17/2 * 4
n_windows = 100
packet_cnts = np.zeros(n_windows)
time_slots = np.ones(n_windows) * -1
spectra = np.ones([n_windows, 4096, n_bls*4], dtype=complex)*2**31
