def _rtrim(self, endtime, pad=False, nearest_sample=True, fill_value=None):
"""
Cut current trace to given end time. For more info see
:meth:`~obspy.core.trace.Trace.trim`.
.. rubric:: Example
>>> tr = Trace(data=np.arange(0, 10))
>>> tr.stats.delta = 1.0
>>> tr._rtrim(tr.stats.starttime + 2) # doctest: +ELLIPSIS
<...Trace object at 0x...>
>>> tr.data
array([0, 1, 2])
>>> tr.stats.endtime
UTCDateTime(1970, 1, 1, 0, 0, 2)
"""
org_dtype = self.data.dtype
if isinstance(endtime, float) or isinstance(endtime, int):
endtime = UTCDateTime(self.stats.endtime) - endtime
elif not isinstance(endtime, UTCDateTime):
raise TypeError
# check if in boundary
if nearest_sample:
delta = round_away((endtime - self.stats.starttime) *
self.stats.sampling_rate) - self.stats.npts + 1
delta = int(delta)
else:
# solution for #127, however some tests need to be changed
# delta = -1*int(math.floor(compatibility.round_away(
# (self.stats.endtime - endtime) * \
# self.stats.sampling_rate, 7)))
delta = int(
math.floor(
round((endtime - self.stats.endtime) *
self.stats.sampling_rate, 7)))
if delta == 0 or (delta > 0 and not pad):
return self
if delta > 0 and pad:
try:
gap = createEmptyDataChunk(delta, self.data.dtype, fill_value)
except ValueError:
# createEmptyDataChunk returns negative ValueError ?? for
# too large number of points, e.g. 189336539799
raise Exception("Time offset between starttime and " +
"trace.starttime too large")
self.data = np.ma.concatenate((self.data, gap))
return self
elif endtime < self.stats.starttime:
self.stats.starttime = self.stats.endtime + \
delta * self.stats.delta
self.data = np.empty(0, dtype=org_dtype)
return self
# cut from right
delta = abs(delta)
total = len(self.data) - delta
if endtime == self.stats.starttime:
total = 1
self.data = self.data[:total]
return self
def _rtrim(self, endtime, pad=False, nearest_sample=True, fill_value=None):
"""
Cut current trace to given end time. For more info see
:meth:`~obspy.core.trace.Trace.trim`.
.. rubric:: Example
>>> tr = Trace(data=np.arange(0, 10))
>>> tr.stats.delta = 1.0
>>> tr._rtrim(tr.stats.starttime + 2) # doctest: +ELLIPSIS
<...Trace object at 0x...>
>>> tr.data
array([0, 1, 2])
>>> tr.stats.endtime
UTCDateTime(1970, 1, 1, 0, 0, 2)
"""
org_dtype = self.data.dtype
if isinstance(endtime, float) or isinstance(endtime, int):
endtime = UTCDateTime(self.stats.endtime) - endtime
elif not isinstance(endtime, UTCDateTime):
raise TypeError
# check if in boundary
if nearest_sample:
delta = round_away((endtime - self.stats.starttime) *
self.stats.sampling_rate) - self.stats.npts + 1
delta = int(delta)
else:
# solution for #127, however some tests need to be changed
# delta = -1*int(math.floor(compatibility.round_away(
# (self.stats.endtime - endtime) * \
# self.stats.sampling_rate, 7)))
delta = int(math.floor(round((endtime - self.stats.endtime) *
self.stats.sampling_rate, 7)))
if delta == 0 or (delta > 0 and not pad):
return self
if delta > 0 and pad:
try:
gap = createEmptyDataChunk(delta, self.data.dtype, fill_value)
except ValueError:
# createEmptyDataChunk returns negative ValueError ?? for
# too large number of points, e.g. 189336539799
raise Exception("Time offset between starttime and " +
"trace.starttime too large")
self.data = np.ma.concatenate((self.data, gap))
return self
elif endtime < self.stats.starttime:
self.stats.starttime = self.stats.endtime + \
delta * self.stats.delta
self.data = np.empty(0, dtype=org_dtype)
return self
# cut from right
delta = abs(delta)
total = len(self.data) - delta
if endtime == self.stats.starttime:
total = 1
self.data = self.data[:total]
return self
def trim(self, starttime=None, endtime=None, pad=False,
nearest_sample=True, fill_value=None):
""" Cut all traces of this Stream object to given start and end time """
if not self:
return
# select start/end time fitting to a sample point of the first trace
if nearest_sample:
tr = self.traces[0]
if starttime:
delta = round_away(
(starttime - tr.stats.starttime) * tr.stats.sampling_rate)
starttime = tr.stats.starttime + delta * tr.stats.delta
if endtime:
delta = round_away(
(endtime - tr.stats.endtime) * tr.stats.sampling_rate)
# delta is negative!
endtime = tr.stats.endtime + delta * tr.stats.delta
for trace in self.traces:
trace.trim(starttime, endtime, pad=pad,
nearest_sample=nearest_sample, fill_value=fill_value)
# remove empty traces after trimming
self.traces = [_i for _i in self.traces if _i.stats.npts]
return self
def _ltrim(self, starttime, pad=False, nearest_sample=True,
fill_value=None):
"""
Cut current trace to given start time. For more info see
:meth:`~obspy.core.trace.Trace.trim`.
.. rubric:: Example
>>> tr = Trace(data=np.arange(0, 10))
>>> tr.stats.delta = 1.0
>>> tr._ltrim(tr.stats.starttime + 8) # doctest: +ELLIPSIS
<...Trace object at 0x...>
>>> tr.data
array([8, 9])
>>> tr.stats.starttime
UTCDateTime(1970, 1, 1, 0, 0, 8)
"""
org_dtype = self.data.dtype
if isinstance(starttime, float) or isinstance(starttime, int):
starttime = UTCDateTime(self.stats.starttime) + starttime
elif not isinstance(starttime, UTCDateTime):
raise TypeError
# check if in boundary
if nearest_sample:
delta = round_away((starttime - self.stats.starttime) * self.stats.sampling_rate)
# due to rounding and npts starttime must always be right of
# self.stats.starttime, rtrim relies on it
if delta < 0 and pad:
npts = abs(delta) + 10 # use this as a start
newstarttime = self.stats.starttime - npts / \
float(self.stats.sampling_rate)
newdelta = round_away((starttime - newstarttime) * self.stats.sampling_rate)
delta = newdelta - npts
delta = int(delta)
else:
delta = int(math.floor(round((self.stats.starttime - starttime) *
self.stats.sampling_rate, 7))) * -1
# Adjust starttime only if delta is greater than zero or if the values
# are padded with masked arrays.
if delta > 0 or pad:
self.stats.starttime += delta * self.stats.delta
if delta == 0 or (delta < 0 and not pad):
return self
elif delta < 0 and pad:
try:
gap = createEmptyDataChunk(abs(delta), self.data.dtype,
fill_value)
except ValueError:
# createEmptyDataChunk returns negative ValueError ?? for
# too large number of points, e.g. 189336539799
raise Exception("Time offset between starttime and "
"trace.starttime too large")
self.data = np.ma.concatenate((gap, self.data))
return self
elif starttime > self.stats.endtime:
self.data = np.empty(0, dtype=org_dtype)
return self
elif delta > 0:
try:
self.data = self.data[delta:]
except IndexError:
# a huge numbers for delta raises an IndexError
# here we just create empty array with same dtype
self.data = np.empty(0, dtype=org_dtype)
return self
def __add__(self, trace, method=1, interpolation_samples=0,
fill_value="latest", sanity_checks=True):
""" Add another Trace object to current trace """
if sanity_checks:
if not isinstance(trace, Trace):
raise TypeError
# check id
if self.getId() != trace.getId():
raise TypeError("Trace ID differs")
# check sample rate
if self.stats.sampling_rate != trace.stats.sampling_rate:
raise TypeError("Sampling rate differs")
# check calibration factor
if self.stats.calib != trace.stats.calib:
raise TypeError("Calibration factor differs")
# check data type
if self.data.dtype != trace.data.dtype:
raise TypeError("Data type differs")
# check times
if self.stats.starttime <= trace.stats.starttime:
lt = self
rt = trace
else:
rt = self
lt = trace
# check whether to use the latest value to fill a gap
if fill_value == "latest":
fill_value = lt.data[-1]
elif fill_value == "interpolate":
fill_value = (lt.data[-1], rt.data[0])
sr = self.stats.sampling_rate
delta = (rt.stats.starttime - lt.stats.endtime) * sr
delta = int(round_away(delta)) - 1
delta_endtime = lt.stats.endtime - rt.stats.endtime
# create the returned trace
out = self.__class__(header=deepcopy(lt.stats))
# check if overlap or gap
if delta < 0 and delta_endtime < 0:
# overlap
delta = abs(delta)
if np.all(np.equal(lt.data[-delta:], rt.data[:delta])):
# check if data are the same
data = [lt.data[:-delta], rt.data]
elif method == 0:
raise ValueError, "Unknown method 2 in __add__"
elif method == 1 and interpolation_samples >= -1:
try:
ls = lt.data[-delta - 1]
except:
ls = lt.data[0]
if interpolation_samples == -1:
interpolation_samples = delta
elif interpolation_samples > delta:
interpolation_samples = delta
try:
rs = rt.data[interpolation_samples]
except IndexError:
# contained trace
data = [lt.data]
else:
# include left and right sample (delta + 2)
interpolation = np.linspace(ls, rs, interpolation_samples+2)
# cut ls and rs and ensure correct data type
interpolation = np.require(interpolation[1:-1], lt.data.dtype)
data = [lt.data[:-delta], interpolation, rt.data[interpolation_samples:]]
else:
raise NotImplementedError
elif delta < 0 and delta_endtime >= 0:
# contained trace
delta = abs(delta)
lenrt = len(rt)
t1 = len(lt) - delta
t2 = t1 + lenrt
# check if data are the same
data_equal = (lt.data[t1:t2] == rt.data)
# force a masked array and fill it for check of equality of valid
# data points
if np.all(np.ma.masked_array(data_equal).filled()):
# if all (unmasked) data are equal,
if isinstance(data_equal, np.ma.masked_array):
x = np.ma.masked_array(lt.data[t1:t2])
y = np.ma.masked_array(rt.data)
data_same = np.choose(x.mask, [x, y])
data = np.choose(x.mask & y.mask, [data_same, np.nan])
if np.any(np.isnan(data)):
data = np.ma.masked_invalid(data)
# convert back to maximum dtype of original data
dtype = np.max((x.dtype, y.dtype))
data = data.astype(dtype)
data = [lt.data[:t1], data, lt.data[t2:]]
else:
data = [lt.data]
elif method == 1:
data = [lt.data]
else:
raise NotImplementedError
elif delta == 0:
# exact fit - merge both traces
data = [lt.data, rt.data]
else:
# gap; use fixed value or interpolate in between
gap = createEmptyDataChunk(delta, lt.data.dtype, fill_value)
data = [lt.data, gap, rt.data]
# merge traces depending on NumPy array type
if True in [isinstance(_i, np.ma.masked_array) for _i in data]:
data = np.ma.concatenate(data)
else:
data = np.concatenate(data)
data = np.require(data, dtype=lt.data.dtype)
# Check if we can downgrade to normal ndarray
if isinstance(data, np.ma.masked_array) and \
np.ma.count_masked(data) == 0:
data = data.compressed()
out.data = data
return out
def _ltrim(self,
starttime,
pad=False,
nearest_sample=True,
fill_value=None):
"""
Cut current trace to given start time. For more info see
:meth:`~obspy.core.trace.Trace.trim`.
.. rubric:: Example
>>> tr = Trace(data=np.arange(0, 10))
>>> tr.stats.delta = 1.0
>>> tr._ltrim(tr.stats.starttime + 8) # doctest: +ELLIPSIS
<...Trace object at 0x...>
>>> tr.data
array([8, 9])
>>> tr.stats.starttime
UTCDateTime(1970, 1, 1, 0, 0, 8)
"""
org_dtype = self.data.dtype
if isinstance(starttime, float) or isinstance(starttime, int):
starttime = UTCDateTime(self.stats.starttime) + starttime
elif not isinstance(starttime, UTCDateTime):
raise TypeError
# check if in boundary
if nearest_sample:
delta = round_away(
(starttime - self.stats.starttime) * self.stats.sampling_rate)
# due to rounding and npts starttime must always be right of
# self.stats.starttime, rtrim relies on it
if delta < 0 and pad:
npts = abs(delta) + 10 # use this as a start
newstarttime = self.stats.starttime - npts / \
float(self.stats.sampling_rate)
newdelta = round_away(
(starttime - newstarttime) * self.stats.sampling_rate)
delta = newdelta - npts
delta = int(delta)
else:
delta = int(
math.floor(
round((self.stats.starttime - starttime) *
self.stats.sampling_rate, 7))) * -1
# Adjust starttime only if delta is greater than zero or if the values
# are padded with masked arrays.
if delta > 0 or pad:
self.stats.starttime += delta * self.stats.delta
if delta == 0 or (delta < 0 and not pad):
return self
elif delta < 0 and pad:
try:
gap = createEmptyDataChunk(abs(delta), self.data.dtype,
fill_value)
except ValueError:
# createEmptyDataChunk returns negative ValueError ?? for
# too large number of points, e.g. 189336539799
raise Exception("Time offset between starttime and "
"trace.starttime too large")
self.data = np.ma.concatenate((gap, self.data))
return self
elif starttime > self.stats.endtime:
self.data = np.empty(0, dtype=org_dtype)
return self
elif delta > 0:
try:
self.data = self.data[delta:]
except IndexError:
# a huge numbers for delta raises an IndexError
# here we just create empty array with same dtype
self.data = np.empty(0, dtype=org_dtype)
return self
def __add__(self,
trace,
method=1,
interpolation_samples=0,
fill_value="latest",
sanity_checks=True):
""" Add another Trace object to current trace """
if sanity_checks:
if not isinstance(trace, Trace):
raise TypeError
# check id
if self.getId() != trace.getId():
raise TypeError("Trace ID differs")
# check sample rate
if self.stats.sampling_rate != trace.stats.sampling_rate:
raise TypeError("Sampling rate differs")
# check calibration factor
if self.stats.calib != trace.stats.calib:
raise TypeError("Calibration factor differs")
# check data type
if self.data.dtype != trace.data.dtype:
raise TypeError("Data type differs")
# check times
if self.stats.starttime <= trace.stats.starttime:
lt = self
rt = trace
else:
rt = self
lt = trace
# check whether to use the latest value to fill a gap
if fill_value == "latest":
fill_value = lt.data[-1]
elif fill_value == "interpolate":
fill_value = (lt.data[-1], rt.data[0])
sr = self.stats.sampling_rate
delta = (rt.stats.starttime - lt.stats.endtime) * sr
delta = int(round_away(delta)) - 1
delta_endtime = lt.stats.endtime - rt.stats.endtime
# create the returned trace
out = self.__class__(header=deepcopy(lt.stats))
# check if overlap or gap
if delta < 0 and delta_endtime < 0:
# overlap
delta = abs(delta)
if np.all(np.equal(lt.data[-delta:], rt.data[:delta])):
# check if data are the same
data = [lt.data[:-delta], rt.data]
elif method == 0:
raise ValueError, "Unknown method 2 in __add__"
elif method == 1 and interpolation_samples >= -1:
try:
ls = lt.data[-delta - 1]
except:
ls = lt.data[0]
if interpolation_samples == -1:
interpolation_samples = delta
elif interpolation_samples > delta:
interpolation_samples = delta
try:
rs = rt.data[interpolation_samples]
except IndexError:
# contained trace
data = [lt.data]
else:
# include left and right sample (delta + 2)
interpolation = np.linspace(ls, rs,
interpolation_samples + 2)
# cut ls and rs and ensure correct data type
interpolation = np.require(interpolation[1:-1],
lt.data.dtype)
data = [
lt.data[:-delta], interpolation,
rt.data[interpolation_samples:]
]
else:
raise NotImplementedError
elif delta < 0 and delta_endtime >= 0:
# contained trace
delta = abs(delta)
lenrt = len(rt)
t1 = len(lt) - delta
t2 = t1 + lenrt
# check if data are the same
data_equal = (lt.data[t1:t2] == rt.data)
# force a masked array and fill it for check of equality of valid
# data points
if np.all(np.ma.masked_array(data_equal).filled()):
# if all (unmasked) data are equal,
if isinstance(data_equal, np.ma.masked_array):
x = np.ma.masked_array(lt.data[t1:t2])
y = np.ma.masked_array(rt.data)
data_same = np.choose(x.mask, [x, y])
data = np.choose(x.mask & y.mask, [data_same, np.nan])
if np.any(np.isnan(data)):
data = np.ma.masked_invalid(data)
# convert back to maximum dtype of original data
dtype = np.max((x.dtype, y.dtype))
data = data.astype(dtype)
data = [lt.data[:t1], data, lt.data[t2:]]
else:
data = [lt.data]
elif method == 1:
data = [lt.data]
else:
raise NotImplementedError
elif delta == 0:
# exact fit - merge both traces
data = [lt.data, rt.data]
else:
# gap; use fixed value or interpolate in between
gap = createEmptyDataChunk(delta, lt.data.dtype, fill_value)
data = [lt.data, gap, rt.data]
# merge traces depending on NumPy array type
if True in [isinstance(_i, np.ma.masked_array) for _i in data]:
data = np.ma.concatenate(data)
else:
data = np.concatenate(data)
data = np.require(data, dtype=lt.data.dtype)
# Check if we can downgrade to normal ndarray
if isinstance(data, np.ma.masked_array) and \
np.ma.count_masked(data) == 0:
data = data.compressed()
out.data = data
return out