def get(self, start, interval=None, step=1):
start, interval = data._prepare(start, interval)
x_vals = data.timerange(start, interval, step)
y_vals = self.interp(start, interval, step)
try:
return zip(x_vals.tolist(), y_vals.tolist())
except TypeError, ValueError:
return zip(x_vals.tolist(), [None] * len(x_vals))
def get (self, start, interval = None, step = 1): start, interval = data._prepare(start, interval) x_vals = data.timerange(start, interval, step) y_vals = self.interp(start, interval, step) try: return zip(x_vals.tolist(), y_vals.tolist()) except TypeError, ValueError: return zip(x_vals.tolist(), [None] * len(x_vals))
def get (self, start, interval, step): new_x = data.timerange(start, interval, step) # Get the slice of y according to frame. # Need to use raw data # TODO: make all manupulations use raw data! x = self._expr._x y = self._expr._y if len(y) > self._window_len: # Extend the slice so that the window can be applied to the edges. s = np.r_[y[self._window_len-1:0:-1], y, y[-1:-self._window_len:-1]] y_smooth = np.convolve(self._window, s, mode = 'valid') y = y_smooth[self._half_window_len : len(y_smooth) - self._half_window_len] return np.interp(new_x, x, y)
def get(self, start, interval, step):
new_x = data.timerange(start, interval, step)
# Get the slice of y according to frame.
# Need to use raw data
# TODO: make all manupulations use raw data!
x = self._expr._x
y = self._expr._y
if len(y) > self._window_len:
# Extend the slice so that the window can be applied to the edges.
s = np.r_[y[self._window_len - 1:0:-1], y,
y[-1:-self._window_len:-1]]
y_smooth = np.convolve(self._window, s, mode='valid')
y = y_smooth[self._half_window_len:len(y_smooth) -
self._half_window_len]
return np.interp(new_x, x, y)
def get(self, start, interval, step):
new_x = data.timerange(start, interval, step)
m = np.min(self._expr.interp(-self._frame, self._frame, step))
return np.ones_like(new_x) * m
def get (self, start, interval, step): new_x = data.timerange(start, interval, step) m = np.max(self._expr.interp(-self._frame, self._frame, step)) return np.ones_like(new_x) * m
