Return input y-range min/max values from m4
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3977f1cc7e
commit
d92ff9c7a0
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@ -223,14 +223,20 @@ def ds_m4(
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assert frames >= (xrange / uppx)
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assert frames >= (xrange / uppx)
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# call into ``numba``
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# call into ``numba``
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nb, i_win, y_out = _m4(
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(
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nb,
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x_out,
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y_out,
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ymn,
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ymx,
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) = _m4(
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x,
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x,
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y,
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y,
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frames,
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frames,
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# TODO: see func below..
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# TODO: see func below..
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# i_win,
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# x_out,
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# y_out,
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# y_out,
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# first index in x data to start at
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# first index in x data to start at
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@ -243,10 +249,11 @@ def ds_m4(
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# filter out any overshoot in the input allocation arrays by
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# filter out any overshoot in the input allocation arrays by
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# removing zero-ed tail entries which should start at a certain
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# removing zero-ed tail entries which should start at a certain
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# index.
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# index.
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i_win = i_win[i_win != 0]
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x_out = x_out[x_out != 0]
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y_out = y_out[:i_win.size]
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y_out = y_out[:x_out.size]
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return nb, i_win, y_out
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# print(f'M4 output ymn, ymx: {ymn},{ymx}')
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return nb, x_out, y_out, ymn, ymx
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@jit(
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@jit(
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@ -260,8 +267,8 @@ def _m4(
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frames: int,
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frames: int,
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# TODO: using this approach by having the ``.zeros()`` alloc lines
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# TODO: using this approach, having the ``.zeros()`` alloc lines
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# below, in put python was causing segs faults and alloc crashes..
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# below in pure python, there were segs faults and alloc crashes..
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# we might need to see how it behaves with shm arrays and consider
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# we might need to see how it behaves with shm arrays and consider
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# allocating them once at startup?
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# allocating them once at startup?
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@ -274,14 +281,22 @@ def _m4(
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x_start: int,
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x_start: int,
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step: float,
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step: float,
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) -> int:
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) -> tuple[
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# nbins = len(i_win)
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int,
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# count = len(xs)
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np.ndarray,
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np.ndarray,
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float,
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float,
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]:
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'''
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Implementation of the m4 algorithm in ``numba``:
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http://www.vldb.org/pvldb/vol7/p797-jugel.pdf
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'''
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# these are pre-allocated and mutated by ``numba``
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# these are pre-allocated and mutated by ``numba``
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# code in-place.
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# code in-place.
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y_out = np.zeros((frames, 4), ys.dtype)
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y_out = np.zeros((frames, 4), ys.dtype)
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i_win = np.zeros(frames, xs.dtype)
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x_out = np.zeros(frames, xs.dtype)
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bincount = 0
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bincount = 0
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x_left = x_start
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x_left = x_start
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@ -295,24 +310,33 @@ def _m4(
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# set all bins in the left-most entry to the starting left-most x value
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# set all bins in the left-most entry to the starting left-most x value
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# (aka a row broadcast).
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# (aka a row broadcast).
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i_win[bincount] = x_left
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x_out[bincount] = x_left
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# set all y-values to the first value passed in.
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# set all y-values to the first value passed in.
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y_out[bincount] = ys[0]
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y_out[bincount] = ys[0]
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mx: float = 0
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mn: float = np.inf
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# compute OHLC style max / min values per window sized x-frame.
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for i in range(len(xs)):
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for i in range(len(xs)):
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x = xs[i]
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x = xs[i]
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y = ys[i]
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y = ys[i]
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if x < x_left + step: # the current window "step" is [bin, bin+1)
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if x < x_left + step: # the current window "step" is [bin, bin+1)
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y_out[bincount, 1] = min(y, y_out[bincount, 1])
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ymn = y_out[bincount, 1] = min(y, y_out[bincount, 1])
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y_out[bincount, 2] = max(y, y_out[bincount, 2])
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ymx = y_out[bincount, 2] = max(y, y_out[bincount, 2])
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y_out[bincount, 3] = y
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y_out[bincount, 3] = y
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mx = max(mx, ymx)
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mn = min(mn, ymn)
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else:
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else:
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# Find the next bin
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# Find the next bin
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while x >= x_left + step:
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while x >= x_left + step:
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x_left += step
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x_left += step
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bincount += 1
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bincount += 1
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i_win[bincount] = x_left
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x_out[bincount] = x_left
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y_out[bincount] = y
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y_out[bincount] = y
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return bincount, i_win, y_out
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return bincount, x_out, y_out, mn, mx
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