import numpy as np
from ..functionbase import FncBaseClass
[docs]class Downsample(FncBaseClass):
"""
Downsample an event by a given factor (which has to be a factor of the event's length).
Also works for multiple channels simultaneously.
:param down: The factor by which to downsample the voltage trace.
:type down: int
:return: Downsampled event.
:rtype: np.ndarray
**Example:**
::
import cait.versatile as vai
# Construct mock data (which provides event iterator)
md = vai.MockData()
it = md.get_event_iterator()[0].with_processing(vai.RemoveBaseline())
# View effect of downsample on events
vai.Preview(it, vai.Downsample(16))
.. image:: media/Downsample_preview.png
"""
def __init__(self, down: int = 2):
self._down = down
def __call__(self, event):
if event.ndim > 1:
shape = (event.shape[0], int(event.shape[-1]/self._down), self._down)
else:
shape = (int(event.shape[-1]/self._down), self._down)
self._downsampled = np.mean(np.reshape(event, shape), axis=-1)
return self._downsampled
@property
def batch_support(self):
return 'trivial'
def preview(self, event):
self(event)
x = np.arange(event.shape[-1])
x_down = np.mean(np.reshape(x,(int(len(x)/self._down), self._down)), axis=1)
if np.ndim(event) > 1:
d = dict()
for i in range(np.ndim(event)):
d[f'channel {i}'] = [x, event[i]]
d[f'downsampled channel {i}'] = [x_down, self._downsampled[i]]
else:
d = {'event': [x, event],
'downsampled event': [x_down, self._downsampled]}
return dict(line = d)