def derive(self, pitch=P('Pitch'), climbs=S('Initial Climb')):
for climb in climbs:
masked_pitch = mask_outside_slices(pitch.array, [climb.slice])
pitch_index = np.ma.argmax(
masked_pitch <= -10) or np.ma.argmin(masked_pitch)
scaling_factor = abs(masked_pitch[pitch_index]) / 10
window_threshold = -10.00 * scaling_factor
min_window_threshold = -8.00 * scaling_factor
window_size = 32
window_threshold_step = 0.050 * scaling_factor
diffs = np.ma.ediff1d(masked_pitch[climb.slice.start:pitch_index])
diffs_exist = diffs.data.size >= 2
big_diff_index = -1
while diffs_exist:
sig_pitch_threshold = window_threshold / window_size
for i, d in enumerate(diffs):
# Look for the first big negative pitch spike
if diffs[slices_int(
i, i + window_size)].sum() < window_threshold:
# Find the first significant negative value within the
# spike and make that the starting point of the phase
big_diff_index = np.ma.argmax(
diffs[i:i + window_size] < sig_pitch_threshold) + i
break
# Bail on match or total failure
if big_diff_index != -1 or window_size < 2:
break
# Shrink window size instead of looking for insignificant
# spikes and scale window/pitch thresholds accordingly
if window_threshold >= min_window_threshold:
window_size //= 2
min_window_threshold /= 2
window_threshold /= 2
window_threshold_step /= 2
sig_pitch_threshold *= 2
else:
window_threshold += window_threshold_step
if big_diff_index != -1:
self.create_section(
slice(climb.slice.start + big_diff_index, pitch_index))
# Worst case fallback, this should happen extremely rarely
# and would trigger all events related to this phase
else:
self.create_section(slice(climb.slice.start, climb.slice.stop))
def derive(self, pitch=P('Pitch'), climbs=S('Initial Climb')):
for climb in climbs:
masked_pitch = mask_outside_slices(pitch.array, [climb.slice])
pitch_index = np.ma.argmax(masked_pitch <= -10) or np.ma.argmin(masked_pitch)
scaling_factor = abs(masked_pitch[pitch_index]) / 10
window_threshold = -10.00 * scaling_factor
min_window_threshold = -8.00 * scaling_factor
window_size = 32
window_threshold_step = 0.050 * scaling_factor
diffs = np.ma.ediff1d(masked_pitch[climb.slice.start:pitch_index])
diffs_exist = diffs.data.size >= 2
big_diff_index = -1
while diffs_exist:
sig_pitch_threshold = window_threshold / window_size
for i, d in enumerate(diffs):
# Look for the first big negative pitch spike
if diffs[i:i+window_size].sum() < window_threshold:
# Find the first significant negative value within the
# spike and make that the starting point of the phase
big_diff_index = np.ma.argmax(diffs[i:i+window_size] < sig_pitch_threshold) + i
break
# Bail on match or total failure
if big_diff_index != -1 or window_size < 2:
break
# Shrink window size instead of looking for insignificant
# spikes and scale window/pitch thresholds accordingly
if window_threshold >= min_window_threshold:
window_size /= 2; min_window_threshold /= 2; window_threshold /= 2; window_threshold_step /= 2
sig_pitch_threshold *= 2
else:
window_threshold += window_threshold_step
if big_diff_index != -1:
self.create_section(slice(climb.slice.start + big_diff_index,
pitch_index))
# Worst case fallback, this should happen extremely rarely
# and would trigger all events related to this phase
else:
self.create_section(slice(climb.slice.start, climb.slice.stop))
def derive(self,
num=P('Flight Number'),
mobiles=S('Mobile')):
# Limit to Mobile sections
num_array = mask_outside_slices(num.array, mobiles.get_slices())
# Q: Should we validate the flight number?
if num.array.dtype.type is np.string_:
value = most_common_value(num_array, threshold=0.45)
if value is not None:
# Only parse valid ASCII characters
try:
self.set_flight_attr(re.sub(r'[^\x00-\x7f]', r'', value.decode()))
except UnicodeDecodeError:
self.set_flight_attr(None)
return
# Values of 0 are invalid flight numbers
array = np.ma.masked_less_equal(num_array, 0)
# Ignore masked values
compressed_array = array.compressed()
_, minvalue = min_value(compressed_array)
if minvalue is None or minvalue < 0:
self.warning(
"'%s' only supports unsigned (positive) values > 0, "
"but none were found. Cannot determine flight number",
self.name)
self.set_flight_attr(None)
return
# note reverse of value, index from max_value due to bincount usage.
value, count = max_value(
np.bincount(compressed_array.astype(np.integer)))
if count > len(compressed_array) * 0.45:
# this value accounts for at least 45% of the values in the array
self.set_flight_attr(str(int(value)))
else:
self.warning("Only %d out of %d flight numbers were the same."
" Flight Number attribute will be set as None.",
count or 0, len(num.array))
self.set_flight_attr(None)
return