def __init__(self, filename):
(data, metadata, analysis, meta_analysis) = fcs.read(filename)
# List of column names
columns = map(lambda j: metadata['$P{}N'.format(j)],
range(1, data.shape[0] + 1))
self._data = LabeledColumns(data, columns)
# Same for Analysis
columns = map(lambda j: metadata['$P{}N'.format(j)],
range(1, data.shape[0] + 1))
self._analysis = LabeledColumns(analysis, columns)
# This variable encodes the original length of the data set as imported for use
# normalizing kernel density estimates
self._original_length = int(metadata['$TOT'])
# Name that will appear in the legend of plots
self.title = ''
try:
self.title = metadata['$FIL']
except:
pass
self._metadata = metadata
self._meta_analysis = meta_analysis
def __init__(self, filename):
(data, metadata, analysis, meta_analysis) = fcs.read(filename)
# List of column names
columns = map(lambda j: metadata['$P{}N'.format(j)], range(1,data.shape[0]+1))
# There is an endian-ness bug that requires changing the type of data to satisfy
# pandas
super(FlowData,self).__init__(np.transpose(data).astype('f8'), columns = columns)
# This variable encodes the original length of the data set as imported for use
# normalizing kernel density estimates
self._original_length = int(metadata['$TOT'])
# Name that will appear in the legend of plots
self.title = ''
try:
self.title = metadata['$FIL']
except:
pass
self._metadata = metadata
self._analysis = analysis
self._meta_analysis = meta_analysis
self._data = data
def __init__(self, filename):
(data, metadata, analysis, meta_analysis) = fcs.read(filename)
# List of column names
columns = map(lambda j: metadata['$P{}N'.format(j)],
range(1, data.shape[0] + 1))
# There is an endian-ness bug that requires changing the type of data to satisfy
# pandas
super(FlowData, self).__init__(np.transpose(data).astype('f8'),
columns=columns)
# This variable encodes the original length of the data set as imported for use
# normalizing kernel density estimates
self._original_length = int(metadata['$TOT'])
# Name that will appear in the legend of plots
self.title = ''
try:
self.title = metadata['$FIL']
except:
pass
self._metadata = metadata
self._analysis = analysis
self._meta_analysis = meta_analysis
self._data = data
def tick(io0, io1):
if not fcs._fcs:
raise RuntimeError("Please call fcs.init()")
fcs.write(0, io0)
fcs.write(1, io1)
fcs._fcs.fcs_board_tick()
fcs._fcs.fcs_ahrs_tick()
fcs.read(1, 1023)
fcs.read(2, 1023)
fcs.read(3, 1023)
fcs.read(4, 1023)
sensor_health = fcs.get_sensor_health()
try:
return plog.ParameterLog.deserialize(fcs.read(0, 1023)), sensor_health
except Exception:
return None, None
def __init__(self, path = None):
if not path is None:
(self._data, self._metadata, self._analysis, self._meta_analysis) = \
fcs.read(path, True)
self._path = path
self._filename = os.path.basename(path)
self._original_length = self.nevents
else:
self._data = []
self._metadata = {}
self._analysis = []
self._meta_analysis = {}
# Number of variables in original dataset, in case we make a daughter,
# for normalization when doing KDEs
self._original_length = 0
def main():
import time
import fcs
(data, metadata, analysis, meta_analysis) = fcs.read('test2.fcs')
print "Original data length {}".format(data.shape[1])
data = data[2:37,:]
start = time.time()
s = Spade(data, use_KD_tree = True)
s.nsamples = 2000
print s.estimate_median_dist()
s.compute_local_density()
s.downsample()
stop = time.time()
print "Elapsed time {}".format(stop - start)
def main():
import time
import fcs
(data, metadata, analysis, meta_analysis) = fcs.read('test.fcs')
print data.shape
print "Original data length {}".format(data.shape[1])
data = data[2:20, 0:5000]
start = time.time()
s = Spade(data, use_KD_tree=True)
s.nsamples = 2000
print s.estimate_median_dist()
s.compute_local_density()
s.downsample()
stop = time.time()
print "Elapsed time {}".format(stop - start)
def __init__(self, filename = None, panda = None, metadata = None):
"""Load an FCS file specified by the filename.
"""
if filename is not None:
(data, metadata, analysis, meta_analysis) = fcs.read(filename)
elif panda is not None:
self.panda = panda
data = None
analysis = None
meta_analysis = None
# Fill the metadata if necessary
if metadata is None:
metadata = {}
for j, key in enumerate(self.panda.columns):
metadata['$P{:d}S'.format(j+1)] = key
if '$PAR' not in metadata:
metadata['$PAR'] = panda.shape[1]
if '$TOT' not in metadata:
metadata['$TOT'] = panda.shape[0]
self._metadata = metadata
self._analysis = analysis
self._meta_analysis = meta_analysis
self._data = data
# A dictionary that converts column names to index numbers
# currently we default to using the long name value $PnS
self._alt_names = util.alt_names(self.names, self.short_names)
# There is an endian-ness bug that requires changing the type of data to satisfy
# pandas
if filename is not None:
self.panda = pd.DataFrame(np.transpose(data).astype('f8'), columns = self.names)
# Name that will appear in the legend of plots
self.title = ''
try:
self.title = metadata['$FIL']
except:
pass
self.spade_mst = {}
self.spade_means = {}
def __init__(self, filename):
(data, metadata, analysis, meta_analysis) = fcs.read(filename)
# List of column names
columns = map(lambda j: metadata['$P{}N'.format(j)], range(1,data.shape[0]+1))
self._data = LabeledColumns(data, columns)
# Same for Analysis
columns = map(lambda j: metadata['$P{}N'.format(j)], range(1,data.shape[0]+1))
self._analysis = LabeledColumns(analysis, columns)
# This variable encodes the original length of the data set as imported for use
# normalizing kernel density estimates
self._original_length = int(metadata['$TOT'])
# Name that will appear in the legend of plots
self.title = ''
try:
self.title = metadata['$FIL']
except:
pass
self._metadata = metadata
self._meta_analysis = meta_analysis
def tick(lat=None, lon=None, alt=None, velocity=None, attitude=None,
angular_velocity=None, wind_velocity=None, measurement_input=None):
"""
Runs the FCS control and comms tasks with the state data provided as
though it came from the AHRS, and returns the control output.
"""
if not fcs._fcs:
raise RuntimeError("Please call fcs.init()")
estimate_log = plog.ParameterLog(
log_type=plog.LogType.FCS_LOG_TYPE_ESTIMATE)
estimate_log.append(
plog.DataParameter(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_ESTIMATED_POSITION_LLA,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=32,
values=[
int(lat * (2**31 - 1) / math.pi),
int(lon * (2**31 - 1) / math.pi),
int(alt * 1e2)
]
)
)
estimate_log.append(
plog.DataParameter(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_ESTIMATED_VELOCITY_NED,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=map(lambda x: int(x * 1e2), velocity)
)
)
estimate_log.append(
plog.DataParameter(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_ESTIMATED_ATTITUDE_Q,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=map(lambda x: int(x * (2**15 - 1)), attitude)
)
)
estimate_log.append(
plog.DataParameter(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_ESTIMATED_ANGULAR_VELOCITY_XYZ,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=map(lambda x: int(x * (2**15 - 1) / math.pi * 0.25), angular_velocity)
)
)
estimate_log.append(
plog.DataParameter(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_ESTIMATED_WIND_VELOCITY_NED,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=map(lambda x: int(x * 1e2), wind_velocity)
)
)
estimate_log.append(
plog.DataParameter(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_AHRS_STATUS,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=[0, 0]
)
)
fcs.write(3, estimate_log.serialize())
fcs.write(1, measurement_input)
#print binascii.b2a_hex(estimate_log.serialize())
fcs._fcs.fcs_board_tick()
fcs._fcs.fcs_ahrs_tick()
fcs._fcs.fcs_control_tick()
# Read out ignored streams
fcs.read(0, 1023)
fcs.read(1, 1023)
fcs.read(2, 1023)
sys.stderr.write(fcs.read(4, 1023))
try:
control_log = plog.ParameterLog.deserialize(fcs.read(3, 1023))
# print control_log
control_param = control_log.find_by(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_CONTROL_SETPOINT)
path = control_log.find_by(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_NAV_PATH_ID).values[0]
refp = control_log.find_by(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_KEY_VALUE)
cycles, obj_val, errors, resets = control_log.find_by(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_CONTROL_STATUS).values
last_control = (
map(lambda x: float(x) / float(2**16), control_param.values),
plog.extract_waypoint(refp.value),
obj_val, cycles, errors, resets, control_log.tick, path
)
return last_control
except Exception:
return ([0.0, 0.5, 0.5], {}, 0, 0, 0, 0, 0, 0xFFFF)
#! /opt/local/bin/python
import fcs
data, metadata, analysis, meta_analysis = fcs.read('test.fcs')
fcs.write('test_write.fcs',data, metadata)
def tick(lat=None,
lon=None,
alt=None,
velocity=None,
attitude=None,
angular_velocity=None,
wind_velocity=None,
measurement_input=None):
"""
Runs the FCS control and comms tasks with the state data provided as
though it came from the AHRS, and returns the control output.
"""
if not fcs._fcs:
raise RuntimeError("Please call fcs.init()")
estimate_log = plog.ParameterLog(
log_type=plog.LogType.FCS_LOG_TYPE_ESTIMATE)
estimate_log.append(
plog.DataParameter(device_id=0,
parameter_type=plog.ParameterType.
FCS_PARAMETER_ESTIMATED_POSITION_LLA,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=32,
values=[
int(lat * (2**31 - 1) / math.pi),
int(lon * (2**31 - 1) / math.pi),
int(alt * 1e2)
]))
estimate_log.append(
plog.DataParameter(device_id=0,
parameter_type=plog.ParameterType.
FCS_PARAMETER_ESTIMATED_VELOCITY_NED,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=map(lambda x: int(x * 1e2), velocity)))
estimate_log.append(
plog.DataParameter(device_id=0,
parameter_type=plog.ParameterType.
FCS_PARAMETER_ESTIMATED_ATTITUDE_Q,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=map(lambda x: int(x * (2**15 - 1)),
attitude)))
estimate_log.append(
plog.DataParameter(device_id=0,
parameter_type=plog.ParameterType.
FCS_PARAMETER_ESTIMATED_ANGULAR_VELOCITY_XYZ,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=map(
lambda x: int(x * (2**15 - 1) / math.pi * 0.25),
angular_velocity)))
estimate_log.append(
plog.DataParameter(device_id=0,
parameter_type=plog.ParameterType.
FCS_PARAMETER_ESTIMATED_WIND_VELOCITY_NED,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=map(lambda x: int(x * 1e2), wind_velocity)))
estimate_log.append(
plog.DataParameter(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_AHRS_STATUS,
value_type=plog.ValueType.FCS_VALUE_SIGNED,
value_precision=16,
values=[0, 0]))
fcs.write(3, estimate_log.serialize())
fcs.write(1, measurement_input)
#print binascii.b2a_hex(estimate_log.serialize())
fcs._fcs.fcs_board_tick()
fcs._fcs.fcs_ahrs_tick()
fcs._fcs.fcs_control_tick()
# Read out ignored streams
fcs.read(0, 1023)
fcs.read(1, 1023)
fcs.read(2, 1023)
sys.stderr.write(fcs.read(4, 1023))
try:
control_log = plog.ParameterLog.deserialize(fcs.read(3, 1023))
# print control_log
control_param = control_log.find_by(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_CONTROL_SETPOINT)
path = control_log.find_by(device_id=0,
parameter_type=plog.ParameterType.
FCS_PARAMETER_NAV_PATH_ID).values[0]
refp = control_log.find_by(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_KEY_VALUE)
cycles, obj_val, errors, resets = control_log.find_by(
device_id=0,
parameter_type=plog.ParameterType.FCS_PARAMETER_CONTROL_STATUS
).values
last_control = (map(lambda x: float(x) / float(2**16),
control_param.values),
plog.extract_waypoint(refp.value), obj_val, cycles,
errors, resets, control_log.tick, path)
return last_control
except Exception:
return ([0.0, 0.5, 0.5], {}, 0, 0, 0, 0, 0, 0xFFFF)
