Drop legacy step mode data formatter
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f93ac15440
commit
e3dd933b34
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@ -44,80 +44,6 @@ from ..log import get_logger
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log = get_logger(__name__)
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# TODO: numba this instead..
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# def step_path_arrays_from_1d(
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# x: np.ndarray,
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# y: np.ndarray,
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# include_endpoints: bool = True,
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# ) -> (np.ndarray, np.ndarray):
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# '''
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# Generate a "step mode" curve aligned with OHLC style bars
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# such that each segment spans each bar (aka "centered" style).
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# '''
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# # y_out = y.copy()
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# # x_out = x.copy()
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# # x2 = np.empty(
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# # # the data + 2 endpoints on either end for
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# # # "termination of the path".
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# # (len(x) + 1, 2),
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# # # we want to align with OHLC or other sampling style
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# # # bars likely so we need fractinal values
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# # dtype=float,
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# # )
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# x2 = np.broadcast_to(
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# x[:, None],
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# (
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# x.size + 1,
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# # 4, # only ohlc
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# 2,
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# ),
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# ) + np.array([-0.5, 0.5])
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# # x2[0] = x[0] - 0.5
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# # x2[1] = x[0] + 0.5
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# # x2[0, 0] = x[0] - 0.5
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# # x2[0, 1] = x[0] + 0.5
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# # x2[1:] = x[:, np.newaxis] + 0.5
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# # import pdbpp
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# # pdbpp.set_trace()
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# # flatten to 1-d
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# # x_out = x2.reshape(x2.size)
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# # x_out = x2
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# # we create a 1d with 2 extra indexes to
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# # hold the start and (current) end value for the steps
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# # on either end
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# y2 = np.empty(
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# (len(y) + 1, 2),
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# dtype=y.dtype,
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# )
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# y2[:] = y[:, np.newaxis]
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# # y2[-1] = 0
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# # y_out = y2
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# # y_out = np.empty(
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# # 2*len(y) + 2,
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# # dtype=y.dtype
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# # )
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# # flatten and set 0 endpoints
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# # y_out[1:-1] = y2.reshape(y2.size)
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# # y_out[0] = 0
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# # y_out[-1] = 0
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# if not include_endpoints:
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# return x2[:-1], y2[:-1]
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# else:
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# return x2, y2
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_line_styles: dict[str, int] = {
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'solid': Qt.PenStyle.SolidLine,
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'dash': Qt.PenStyle.DashLine,
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