def window(series, start=0, end=None): """ desc: Extracts a window from a signal. arguments: series: desc: The signal to get a window from. type: SeriesColumn keywords: start: desc: The window start. type: int end: desc: The window end, or None to go to the signal end. type: [int, None] returns: desc: A window of the signal. type: SeriesColumn """ if end is None: end = series.depth a = series[:,start:end] depth = a.shape[1] window_series = _SeriesColumn(series._datamatrix, depth) window_series[:] = a return window_series
def _apply_fnc(series, fnc, **kwdict): """ visible: False desc: Applies a function to each cell. arguments: series: desc: A signal to apply the function to. type: SeriesColumn fnc: desc: The function to apply. keyword-dict: kwdict: A dict with keyword arguments for fnc. returns: desc: A new signal. type: SeriesColumn """ new_series = _SeriesColumn(series._datamatrix, depth=series.depth) for i, cell in enumerate(series): new_series[i] = fnc(cell, **kwdict) return new_series
def endlock(series): """ desc: Locks a series to the end, so that any nan-values that were at the end are moved to the front. arguments: series: desc: The signal to end-lock. type: SeriesColumn returns: desc: An end-locked signal. type: SeriesColumn """ endlock_series = _SeriesColumn(series._datamatrix, series.depth) endlock_series[:] = np.nan for i in range(len(series)): for j in range(series.depth-1, -1, -1): if not np.isnan(series[i,j]): break endlock_series[i,-j-1:] = series[i,:j+1] return endlock_series
def threshold(series, fnc, min_length=1): """ desc: Finds samples that satisfy some threshold criterion for a given period. arguments: series: desc: A signal to threshold. type: SeriesColumn fnc: desc: A function that takes a single value and returns True if this value exceeds a threshold, and False otherwise. type: FunctionType keywords: min_length: desc: The minimum number of samples for which `fnc` must return True. type: int returns: desc: A series where 0 indicates below threshold, and 1 indicates above threshold. type: SeriesColumn """ threshold_series = _SeriesColumn(series._datamatrix, series.depth) threshold_series[:] = 0 # First walk through all rows for i, trace in enumerate(series): print() # Then walk through all samples within a row nhit = 0 for j, val in enumerate(trace): hit = fnc(val) if hit: nhit += 1 continue if nhit >= min_length: threshold_series[i,j-nhit:j] = 1 nhit = 0 if nhit >= min_length: threshold_series[i,j-nhit:j] = 1 return threshold_series
def concatenate(*series): """ desc: | Concatenates multiple series such that a new series is created with a depth that is equal to the sum of the depths of all input series. __Example:__ %-- python: | from datamatrix import series as srs dm = DataMatrix(length=1) dm.s1 = SeriesColumn(depth=3) dm.s1[:] = 1,2,3 dm.s2 = SeriesColumn(depth=3) dm.s2[:] = 3,2,1 dm.s = srs.concatenate(dm.s1, dm.s2) print(dm.s) --% argument-list: series: A list of series. returns: desc: A new series. type: SeriesColumn """ if not series or not all(isinstance(s, _SeriesColumn) for s in series): raise TypeError(u'Expecting one or more SeriesColumn objects') if not all(s.dm is series[0].dm for s in series): raise ValueError( u'SeriesColumn objects don\'t belong to the same DataMatrix') newseries = _SeriesColumn(series[0]._datamatrix, depth=sum(s.depth for s in series)) i = 0 for s in series: newseries[:,i:i+s.depth] = s i += s.depth return newseries
def endlock(series):
"""
desc: |
Locks a series to the end, so that any nan-values that were at the end
are moved to the start.
__Example:__
%--
python: |
import numpy as np
from matplotlib import pyplot as plt
from datamatrix import DataMatrix, SeriesColumn, series
LENGTH = 5 # Number of rows
DEPTH = 10 # Depth (or length) of SeriesColumns
sinewave = np.sin(np.linspace(0, 2*np.pi, DEPTH))
dm = DataMatrix(length=LENGTH)
# First create five identical rows with a sinewave
dm.y = SeriesColumn(depth=DEPTH)
dm.y.setallrows(sinewave)
# Add a random offset to the Y values
dm.y += np.random.random(LENGTH)
# Set some observations at the end to nan
for i, row in enumerate(dm):
row.y[-i:] = np.nan
# Lock the degraded traces to the end, so that all nans
# now come at the start of the trace
dm.y2 = series.endlock(dm.y)
plt.clf()
plt.subplot(121)
plt.title('Original (nans at end)')
plt.plot(dm.y.plottable)
plt.subplot(122)
plt.title('Endlocked (nans at start)')
plt.plot(dm.y2.plottable)
plt.savefig('content/pages/img/series/endlock.png')
--%
%--
figure:
source: endlock.png
id: FigEndLock
--%
arguments:
series:
desc: The signal to end-lock.
type: SeriesColumn
returns:
desc: An end-locked signal.
type: SeriesColumn
"""
endlock_series = _SeriesColumn(series._datamatrix, series.depth)
endlock_series[:] = np.nan
src = series._seq
dst = endlock_series._seq
for rownr, row in enumerate(src):
nancols = np.where(np.isnan(row))[0]
for nancol in nancols:
if np.any(~np.isnan(row[nancol:])):
continue
dst[rownr, -nancol:] = row[:nancol]
break
else:
dst[rownr] = row
return endlock_series
def threshold(series, fnc, min_length=1):
"""
desc: |
Finds samples that satisfy some threshold criterion for a given period.
__Example:__
%--
python: |
import numpy as np
from matplotlib import pyplot as plt
from datamatrix import DataMatrix, SeriesColumn, series
LENGTH = 1 # Number of rows
DEPTH = 100 # Depth (or length) of SeriesColumns
sinewave = np.sin(np.linspace(0, 2*np.pi, DEPTH))
dm = DataMatrix(length=LENGTH)
# First create five identical rows with a sinewave
dm.y = SeriesColumn(depth=DEPTH)
dm.y.setallrows(sinewave)
# And also a bit of random jitter
dm.y += np.random.random( (LENGTH, DEPTH) )
# Threshold the signal by > 0 for at least 10 samples
dm.t = series.threshold(dm.y, fnc=lambda y: y > 0, min_length=10)
plt.clf()
# Mark the thresholded signal
plt.fill_between(np.arange(DEPTH), dm.t[0], color='black', alpha=.25)
plt.plot(dm.y.plottable)
plt.savefig('content/pages/img/series/threshold.png')
print(dm)
--%
%--
figure:
source: threshold.png
id: FigThreshold
--%
arguments:
series:
desc: A signal to threshold.
type: SeriesColumn
fnc:
desc: A function that takes a single value and returns True if
this value exceeds a threshold, and False otherwise.
type: FunctionType
keywords:
min_length:
desc: The minimum number of samples for which `fnc` must return
True.
type: int
returns:
desc: A series where 0 indicates below threshold, and 1 indicates
above threshold.
type: SeriesColumn
"""
threshold_series = _SeriesColumn(series._datamatrix, series.depth)
threshold_series[:] = 0
# First walk through all rows
for i, trace in enumerate(series):
# Then walk through all samples within a row
nhit = 0
for j, val in enumerate(trace):
hit = fnc(val)
if hit:
nhit += 1
continue
if nhit >= min_length:
threshold_series[i, j - nhit:j] = 1
nhit = 0
if nhit >= min_length:
threshold_series[i, j - nhit + 1:j + 1] = 1
return threshold_series
def normalize_time(dataseries, timeseries):
"""
desc: |
*New in v0.7.0*
Creates a new series in which a series of timestamps (`timeseries`) is
used as the indices for a series of data point (`dataseries`). This is
useful, for example, if you have a series of measurements and a
separate series of timestamps, and you want to combine the two.
The resulting series will generally contain a lot of `nan` values, which
you can interpolate with `interpolate()`.
__Example:__
%--
python: |
from matplotlib import pyplot as plt
from datamatrix import DataMatrix, SeriesColumn, series as srs, NAN
# Create a DataMatrix with one series column that contains samples
# and one series column that contains timestamps.
dm = DataMatrix(length=2)
dm.samples = SeriesColumn(depth=3)
dm.time = SeriesColumn(depth=3)
dm.samples[0] = 3, 1, 2
dm.time[0] = 1, 2, 3
dm.samples[1] = 1, 3, 2
dm.time[1] = 0, 5, 10
# Create a normalized column with samples spread out according to
# the timestamps, and also create an interpolate version of this
# column for smooth plotting.
dm.normalized = srs.normalize_time(
dataseries=dm.samples,
timeseries=dm.time
)
dm.interpolated = srs.interpolate(dm.normalized)
# And plot!
plt.clf()
plt.plot(dm.normalized.plottable, 'o')
plt.plot(dm.interpolated.plottable, ':')
plt.xlabel('Time')
plt.ylabel('Data')
plt.savefig('content/pages/img/series/normalize_time.png')
--%
%--
figure:
source: normalize_time.png
id: FigNormalizeTime
--%
arguments:
dataseries:
desc: A column with datapoints.
type: SeriesColumn
timeseries:
desc: A column with timestamps. This should be an increasing list
of the same depth as `dataseries`. NAN values are allowed,
but only at the end.
type: SeriesColumn
returns:
desc: A new series in which the data points are spread according to
the timestamps.
type: SeriesColumn
"""
if (not isinstance(dataseries, _SeriesColumn)
or not isinstance(timeseries, _SeriesColumn)):
raise TypeError(
'dataseries and timeseries should be SeriesColumn objects')
if dataseries.dm is not timeseries.dm:
raise ValueError(
'dataseries and timeseries should belong to the same DataMatrix')
if dataseries.depth != timeseries.depth:
raise ValueError(
'dataseries and timeseries should have the same depth')
if max(timeseries.max) < 0 or min(timeseries.min) < 0:
raise ValueError('timeseries should contain only positive values')
series = _SeriesColumn(dataseries.dm, depth=int(max(timeseries.max)) + 1)
haystack = np.arange(series.depth, dtype=int)
for row in range(series._seq.shape[0]):
needle = timeseries._seq[row]
values = dataseries._seq[row]
while len(needle) and np.isnan(needle)[-1]:
needle = needle[:-1]
values = values[:-1]
if np.any(np.isnan(needle)):
raise ValueError(
'timeseries should not contain NAN values, except at the end')
if not np.all(np.diff(needle) > 0):
raise ValueError('timeseries should contain increasing values '
'(i.e. time should go forward)')
indices = np.searchsorted(haystack, needle)
series._seq[row, indices] = values
return series
def lock(series, lock):
"""
desc: |
Shifts each row from a series by a certain number of steps along its
depth. This is useful to lock, or align, a series based on a sequence of
values.
__Example:__
%--
python: |
import numpy as np
from matplotlib import pyplot as plt
from datamatrix import DataMatrix, SeriesColumn, series as srs
LENGTH = 5 # Number of rows
DEPTH = 10 # Depth (or length) of SeriesColumns
dm = DataMatrix(length=LENGTH)
# First create five traces with a partial cosinewave. Each row is
# offset slightly on the x and y axes
dm.y = SeriesColumn(depth=DEPTH)
dm.x_offset = -1
dm.y_offset = -1
for row in dm:
row.x_offset = np.random.randint(0, DEPTH)
row.y_offset = np.random.random()
row.y = np.roll(np.cos(np.linspace(0, np.pi, DEPTH)),
row.x_offset)+row.y_offset
# Now use the x offset to lock the traces to the 0 point of the cosine,
# i.e. to their peaks.
dm.y2, zero_point = srs.lock(dm.y, lock=dm.x_offset)
plt.clf()
plt.subplot(121)
plt.title('Original')
plt.plot(dm.y.plottable)
plt.subplot(122)
plt.title('Locked to peak')
plt.plot(dm.y2.plottable)
plt.axvline(zero_point, color='black', linestyle=':')
plt.savefig('content/pages/img/series/lock.png')
--%
%--
figure:
source: lock.png
id: FigLock
--%
arguments:
series:
desc: The signal to lock.
type: SeriesColumn
lock:
desc: A sequence of lock values with the same length as the
Series. This can be a column, a list, a numpy array, etc.
returns:
desc: A `(series, zero_point)` tuple, in which `series` is a
`SeriesColumn` and `zero_point` is the zero point to which the
signal has been locked.
"""
if not isinstance(series, _SeriesColumn):
raise TypeError('series should be a SeriesColumn')
if len(series) != len(lock):
raise ValueError('lock and series should be the same length')
try:
zero_point = int(max(lock))
except TypeError:
raise TypeError('lock should be a sequence of integers')
lpad = [int(zero_point - l) for l in lock]
lock_series = _SeriesColumn(series.dm, series.depth + max(lpad))
for lpad, lock_row, orig_row in zip(lpad, lock_series, series):
lock_row[lpad:lpad + series.depth] = orig_row
return lock_series, zero_point
def fft(series, truncate=True):
"""
desc: |
*New in v0.9.2*
Performs a fast-fourrier transform (FFT) for the signal. For more
information, see [`numpy.fft`](https://docs.scipy.org/doc/numpy/reference/routines.fft.html#module-numpy.fft).
__Example:__
%--
python: |
import numpy as np
from matplotlib import pyplot as plt
from datamatrix import DataMatrix, SeriesColumn, series
LENGTH = 3
DEPTH = 200
# Create one fast oscillation, and two combined fast and slow oscillations
dm = DataMatrix(length=LENGTH)
dm.s = SeriesColumn(depth=DEPTH)
dm.s[0] = np.sin(np.linspace(0, 150 * np.pi, DEPTH))
dm.s[1] = np.sin(np.linspace(0, 75 * np.pi, DEPTH))
dm.s[2] = np.sin(np.linspace(0, 10 * np.pi, DEPTH))
dm.f = series.fft(dm.s)
# Plot the original signal
plt.clf()
plt.subplot(121)
plt.title('Original')
plt.plot(dm.s[0])
plt.plot(dm.s[1])
plt.plot(dm.s[2])
plt.subplot(122)
# And the filtered signal!
plt.title('FFT')
plt.plot(dm.f[0])
plt.plot(dm.f[1])
plt.plot(dm.f[2])
plt.savefig('content/pages/img/series/fft.png')
--%
%--
figure:
source: fft.png
id: FigFFT
--%
arguments:
series:
desc: A signal to determine the FFT for.
type: SeriesColumn
keywords:
truncate:
desc: FFT series of real signals are symmetric. The `truncate`
keyword indicates whether the last (symmetric) part of the
FFT should be removed.
type: bool
returns:
desc: The FFT of the signal.
type: SeriesColumn
"""
newseries = _SeriesColumn(series._datamatrix, depth=series.depth)
newseries[:] = np.fft.fft(series, axis=1)
if truncate:
newseries.depth = newseries.depth // 2
return newseries