def executeSearchStations(config, stations, scanlevel, start):
filename = calcFilename(scanlevel, start)
# ignore if rtl_power file not exists
csv_filename = "%s.csv" % filename
exists = os.path.isfile(csv_filename)
if not exists:
showVerbose(
config, "%s %s not exist%s" % (
tcolor.RED,
csv_filename,
tcolor.DEFAULT,
))
return
# Ignore if call summary not exist
summary_filename = "%s.summary" % filename
exists = os.path.isfile(summary_filename)
if not exists:
showVerbose(
config, "%s %s not exist%s" % (
tcolor.RED,
summary_filename,
tcolor.DEFAULT,
))
return
summaries = loadJSON(summary_filename)
print "%sFind stations '%s' : %shz-%shz" % (
tcolor.DEFAULT,
scanlevel['name'],
commons.float2Hz(start),
commons.float2Hz(start + scanlevel['windows']),
)
smooth_max = commons.smooth(np.array(summaries['max']['signal']), 10,
'flat')
limitmin = summaries['min']['peak']['min']['mean'] - summaries['min'][
'peak']['min']['std']
limitmax = summaries['max']['mean'] + summaries['max']['std']
searchStation(scanlevel, stations, summaries, smooth_max, limitmin,
limitmax)
def executeSearchStations(config, stations, scanlevel, start):
filename = calcFilename(scanlevel, start)
# ignore if rtl_power file not exists
csv_filename = "%s.csv" % filename
exists = os.path.isfile(csv_filename)
if not exists:
showVerbose(
config,
"%s %s not exist%s" % (
tcolor.RED,
csv_filename,
tcolor.DEFAULT,
)
)
return
# Ignore if call summary not exist
summary_filename = "%s.summary" % filename
exists = os.path.isfile(summary_filename)
if not exists:
showVerbose(
config,
"%s %s not exist%s" % (
tcolor.RED,
summary_filename,
tcolor.DEFAULT,
)
)
return
summaries = loadJSON(summary_filename)
print "%sFind stations '%s' : %shz-%shz" % (
tcolor.DEFAULT,
scanlevel['name'], commons.float2Hz(start), commons.float2Hz(start + scanlevel['windows']),
)
smooth_max = commons.smooth(np.array(summaries['max']['signal']),10, 'flat')
limitmin = summaries['min']['peak']['min']['mean'] - summaries['min']['peak']['min']['std']
limitmax = summaries['max']['mean'] + summaries['max']['std']
searchStation(scanlevel, stations, summaries, smooth_max, limitmin, limitmax)
def executeSpectre(cmdargs, config, scanlevel, start):
for gain in scanlevel['gains']:
filename = calcFilename(scanlevel, start, gain)
csv_filename = "%s.csv" % filename
exists = os.path.isfile(csv_filename)
if not exists:
showVerbose(
config, "%s %s not exist%s" % (
tcolor.RED,
csv_filename,
tcolor.DEFAULT,
))
return
# Ignore if summary file not exists
summary_filename = "%s.summary" % filename
exists = os.path.isfile(summary_filename)
if not exists:
showVerbose(
config, "%s %s not exist%s" % (
tcolor.RED,
summary_filename,
tcolor.DEFAULT,
))
return
summaries = loadJSON(summary_filename)
# Check if scan exist
img_filename = "%s_spectre.png" % filename
exists = os.path.isfile(img_filename)
if exists:
showVerbose(
config, "%sSpectre '%s' : %shz-%shz%s" %
(tcolor.GREEN, scanlevel['name'], commons.float2Hz(start),
commons.float2Hz(start + scanlevel['windows']),
tcolor.DEFAULT))
return
print "%sSpectre '%s' : %shz-%shz" % (
tcolor.DEFAULT,
scanlevel['name'],
commons.float2Hz(start),
commons.float2Hz(start + scanlevel['windows']),
)
plt.figure(figsize=(15, 10))
plt.grid()
freqs = np.linspace(summaries['freq']['start'],
summaries['freq']['end'],
num=summaries['samples']['nbsamplescolumn'])
limitmin = summaries['min']['peak']['min']['mean'] - summaries['min'][
'peak']['min']['std']
limitmax = summaries['max']['mean'] + summaries['max']['std']
limits = np.linspace(limitmin, limitmax, 5)
# Max
for limit in limits:
plt.axhline(limit, color='blue')
smooth_max = commons.smooth(np.array(summaries['max']['signal']), 10,
'flat')
plt.plot(freqs, smooth_max[:len(freqs)], color='red')
# Set X Limit
locs, labels = plt.xticks()
for idx in range(len(labels)):
labels[idx] = commons.float2Hz(locs[idx])
plt.xticks(locs, labels)
plt.xlabel('Freq in Hz')
# Set Y Limit
# plt.ylim(summary['groundsignal'], summary['maxsignal'])
plt.ylabel('Power density in dB')
plt.savefig(img_filename)
plt.close()
def executeSpectre(cmdargs, config, scanlevel, start):
for gain in scanlevel['gains']:
filename = calcFilename(scanlevel, start, gain)
csv_filename = "%s.csv" % filename
exists = os.path.isfile(csv_filename)
if not exists:
showVerbose(
config,
"%s %s not exist%s" % (
tcolor.RED,
csv_filename,
tcolor.DEFAULT,
)
)
return
# Ignore if summary file not exists
summary_filename = "%s.summary" % filename
exists = os.path.isfile(summary_filename)
if not exists:
showVerbose(
config,
"%s %s not exist%s" % (
tcolor.RED,
summary_filename,
tcolor.DEFAULT,
)
)
return
summaries = loadJSON(summary_filename)
# Check if scan exist
img_filename = "%s_spectre.png" % filename
exists = os.path.isfile(img_filename)
if exists:
showVerbose(
config,
"%sSpectre '%s' : %shz-%shz%s" % (
tcolor.GREEN,
scanlevel['name'], commons.float2Hz(start), commons.float2Hz(start + scanlevel['windows']),
tcolor.DEFAULT
)
)
return
print "%sSpectre '%s' : %shz-%shz" % (
tcolor.DEFAULT,
scanlevel['name'], commons.float2Hz(start), commons.float2Hz(start + scanlevel['windows']),
)
plt.figure(figsize=(15,10))
plt.grid()
freqs = np.linspace(summaries['freq']['start'], summaries['freq']['end'], num=summaries['samples']['nbsamplescolumn'])
limitmin = summaries['min']['peak']['min']['mean'] - summaries['min']['peak']['min']['std']
limitmax = summaries['max']['mean'] + summaries['max']['std']
limits = np.linspace(limitmin, limitmax, 5)
# Max
for limit in limits:
plt.axhline(limit, color='blue')
smooth_max = commons.smooth(np.array(summaries['max']['signal']),10, 'flat')
plt.plot(freqs, smooth_max[:len(freqs)],color='red')
# Set X Limit
locs, labels = plt.xticks()
for idx in range(len(labels)):
labels[idx] = commons.float2Hz(locs[idx])
plt.xticks(locs, labels)
plt.xlabel('Freq in Hz')
# Set Y Limit
# plt.ylim(summary['groundsignal'], summary['maxsignal'])
plt.ylabel('Power density in dB')
plt.savefig(img_filename)
plt.close()
